Co-Working spaces are fast outpacing monolithic workspaces in terms of commercial real estate absorption in India. While the share of IT/ITeS in the share of transacted space is following a declining trend, co-working spaces are proliferating in the country.
Real estate services company, JLL estimates a 40–50% growth in flexible office space in India in 2018. In a recently released research report ‘Spotting the opportunities: flexible space in the Asia Pacific’, the company says that demand for flexible offices — including co-working spaces and serviced offices — is growing faster in the Asia Pacific than anywhere else in the world. It estimates that over 13 million people will work out of co-working spaces by 2020. The report further indicates, that the top 6 cities will require an estimated 5 million seats in co-working spaces, while, 8.5 million of the projected demand will be in Tier 2 and 3 cities.
The trend is in consonance with the demographic distribution in India. By 2020, the average age in India will be 29 and it is set to become the world’s youngest country with 64% of its population in the working age group. Both Millennials (those born between the early 1980s and mid-1990s) and Post-Millennials seem to prefer operating from co-working spaces.
Enterprises adapting to the Future of Work
While co-working spaces are often associated with startups, they are currently the smallest segment occupying such spaces. Enterprises account for the largest share of co-working space occupied. Generally enterprises are adopting shared workplaces as a means to inject agility into their overall real estate strategies.
Companies like Microsoft, General Electric, Truecaller, GoDaddy, Twitter, Vice Media, Jaguar Land Rover have specialised teams and divisions operating out of co-working spaces in India currently.
According to JLL Research, one of the key drivers of the surge in corporate demand for flexible spaces is plug-and-play simplicity,particularly for larger companies. The ability to move in and out of an office at short notice, avoid complicated contract negotiations and fit-out work is a convenient option for many occupiers. At the same time, businesses are looking to encourage collaboration among employees and are using shared work-spaces as a way to foster innovation through exposure to new ideas and ways of working.
The potential market size of co-working across India is expected to be 13.5 million users by 2020 about half of which will be from enterprises, which are expected to take up 10.3 mn seats. Freelancers and Small & Medium Enterprises (SMEs) are expected to contribute 1.5 million users worth of demand, while it is anticipated that startups will demand up to 100,000 seats by 2020.
According to a report by JLL Research, the maximum (nearly 70%) business opportunity here lies with large corporate firms seeking alternative, activity-based workplaces to nurture their talent and further their business growth. The small, emerging business sector (~20%) follows as the second demand group looking for hot desking opportunities at lowcost shared workplaces, while professional freelancers and start-ups form the rest (~10%) of the market demand.
More Office Spaces becoming Co-working Spaces
In 2017, nearly 1.1 million people worked at the 13,800 coworking spaces around the world, according to the 2017 Global Coworking Survey by Deskmag.
“From 600 centres in 2010 to 18,900 centres in 2018, co-working has come a long way. The Indian market is not dissimilar to global market conditions,” says Dr. Lee Elliott — Global Head of Occupier Research, Knight Frank. He said that Productivity, Technology, Changing Corporate Constitutions, Space as a service, Heightened Mobility & M&A Activities would be the 5 themes for next 5 years.
“We have seen the emergence of co-working spaces in a big way. It represented about 8% of office space absorption in 2017 compared to the previous year’s share of 3%. Technologies such as automation/artificial intelligence could replace traditional job roles in industries such as IT and BFSI. A loss in jobs and a change in the work culture are likely to have an impact on the way companies lease or buy office space,” said Nimish Gupta, Managing Director, RICS South Asia (The Royal Institution of Chartered Surveyors).
Knight Frank India, in the ninth edition of its half-yearly report, indicated that co-working service providers accounted for 13 percent of the total transacted space (across seven cities) for the period January — June 2018 (H1 2018). In Bangalore alone, co-working spaces garnered 19 percent of the total H1 2018 transactions, of a total transaction volume of 0.61 mn sq m or 6.5 mn sq ft. Over all, the share of IT/ITeS in the share of transacted space is following a declining trend.
“In India, the co-working segment is expected to grow by 40–50% in 2018 alone. By the end of the year, we anticipate flexible workplaces would attract investment up to US$ 400 mn. With over 200 premium business centers across the country, set to double by 2020, co-working spaces will reflect the global trend of being closer to 20% of total workspace”, mentioned Sandeep Sethi — MD, Integrated Facilities Management West Asia, JLL.
“By 2030, flexible workspaces could comprise 30 percent of corporate commercial property portfolios worldwide,” said Jeremy Sheldon, Managing Director, Markets & Integrated Portfolio Services, JLL Asia Pacific. “Although corporate adoption is still in its early days, there are certain factors that will continue to make this region a hot spot for co-working growth.”
“Flexibility has become an interesting and appealing aspect in co-working. Co-working will co-exist with conventional office spaces : conventional spaces will start looking and feeling like co-working spaces,” said Dr. Lee Elliott. According to him, 30 percent of occupiers said in the survey that there was no real estate cost benefit. Less than 40% of the respondents said they would regard enterprise solutions in the next 3 years in their business.
Currently, most of the coworking spaces (over 95%) in India function out of rented properties, which is in line with the global average. Most shared office operations in India are also currently dominated by domestic players (almost 90%), with global operators just beginning to make an entry into the marketplace. Alternative spaces like restaurants are also doubling up as co-working spaces.
According to JLL, landlords will continue to form joint ventures with co-working operators or create their own flexible space offerings to meet tenants’ needs. Meanwhile, developers are adapting to what could be a new standard in property development whereby flexible work-space will be an amenity as essential in a commercial building as food and beverage outlets or a gym.
Colliers Research forecasts that commercial real estate is likely to dominate the real estate investment in coming years. A report by Colliers indicates that India witnessed investment transactions totalling to INR156 billion (USD2.4billion) in H1 2018, up 26% compared to H1 2017 (USD1.9billion). The commercial segment dominating with global players such as Blackstone, Brookfield, Xander, etc. remained bullish on investment in commercial real estate in H1 2018. With increasing investment, the CRE market has also started witnessing profound structural changes in the way commercial real estate is built, financed and managed. According to Colliers research the change in ownership from local developers to institutional investors and advent of REITs should lead to the institutionalisation of the CRE in the next three years.
With co-working still being at its early inception stages, specialisation and focus will help differentiate the co-working players.
About the Author
This article was written by Arjun G, Editor of REDACT. see more.
In 2011, I left my job as the second employee at Pinterest — before I vested any of my stock — to work on what I thought would be my life’s work.
I thought Gumroad would become a billion-dollar company, with hundreds of employees. It would IPO, and I would work on it until I died. Something like that.
Needless to say, that didn’t happen.
Now, it may look like I am in an enviable position, running a profitable, growing, low-maintenance software business serving adoring customers. But for years, I considered myself a failure. At my lowest point, I had to lay off 75 percent of my company, including many of my best friends. I had failed.
It took me years to realize I was misguided from the outset. I no longer feel shame in the path I took to get to where I am today — but for a long time, I did. This is my journey, from the beginning.
A weekend project turned VC-backed startup
The idea behind Gumroad was simple: Creators and others should be able to sell their products directly to their audiences with quick, simple links. No need for a storefront.
I built Gumroad the weekend I thought up the idea, and launched it early Monday morning on Hacker News. The reaction exceeded my grandest aspirations. Over 52,000 people checked it out on the first day.
Later that year, I left my job as the second employee at Pinterest — before I vested any of my stock — to turn Gumroad into what I thought would become my life’s work.
Almost immediately, I raised $1.1M from an all-star cast of angel investors and venture capital firms, including Max Levchin, Chris Sacca, Ron Conway, Naval Ravikant, Collaborative Fund, Accel Partners, and First Round Capital. A few months later, in May 2012, we raised $7M more. Mike Abbott from Kleiner Perkins Caufield & Byers (KPCB), a top-tier VC firm, led the round.
I was on top of the world. I was just 19, a solo founder, with over $8M in the bank and three employees. The world was starting to take note.
We grew the team. We stayed focused on our product. The monthly numbers started to climb. And then, at some point, they didn’t.
To keep the product alive, I laid off 75 percent of my company — including many of my best friends. It really sucked. But I told myself things would be fine: The product would continue to grow and no one far from the company would ever find out.
Then, TechCrunch got wind of the layoffs and published “Layoffs Hit Gumroad As The E-Commerce Startup Restructures.” All of a sudden, my failure was public. I spent the week ignoring my support network and answering our customers’ concerns, many of whom relied on us to power their businesses. They wanted to know if they should look for alternative products. Some of our favorite, most successful creators left. This hurt, but I don’t blame them for trying to minimize the risk in their own businesses.
So what exactly went wrong, and when?
Failing in style
Let’s start with the numbers. This is our monthly processed volume, until the layoffs:
It doesn’t look too bad, right? It’s going in the right direction: up.
But we were venture-funded, which was like playing a game of double-or-nothing. It’s euphoric when things are going your way — and suffocating when they’re not. And we weren’t doubling fast enough to raise the $15M+ Series B (the second major round of funding) we were looking for to grow the team.
For the type of business we were trying to build, every month of less than 20 percent growth should have been a red flag.
But at the time, I thought it was okay. We had money in the bank and product-market fit. We would continue to ship product and things would work out. The online creator movement was still nascent; the slow growth wasn’t our fault. It always looked like change was right around the corner.
But now, I realize: It doesn’t matter whose “fault” it is; we hit a peak in November 2014 and stalled. A lot of creators absolutely loved us, but there weren’t enough of them who needed our specific product offering. Product-market fit is great, but we needed to find a new, larger fit to justify raising more money (and then do it again and again, until acquisition or IPO).
For the type of business we were trying to build, every month of less than 20 percent growth should have been a red flag.
In January 2015, after our final double-or-nothing hail-mary, our bank balance dipped below 18 months of runway. I told my 20-person team the road ahead would be a tough one. We didn’t have the numbers to raise a Series B, and we would have to work really hard over the next nine months to get even close. To that end, we deprioritized everything except features that would directly move the needle. Many were not core to our business, but we needed to try everything we could to get our monthly processed volume to where it needed to be.
If we succeeded, we would raise money from a top-tier VC again, hire more people, and pick up the journey where we’d left off. If we didn’t, we would have to drastically downsize the company.
In those nine months, when the whole team knew we were fighting for our company’s life, not a single person left Gumroad. From “this is gonna be hard,” to “yep, turns out it was,” every single person worked harder than ever.
We launched a “Small Product Lab” to teach new creators how to grow and sell. We shipped a ton of features, including weekly payouts, payouts to debit cards, payouts to the U.K., Australia, and Canada, various additions to our email features, product recommendations and search, analytics to see how customers are reading/watching/downloading the products they’ve purchased, and add-to-cart functionality. And that was just between August and November.
Unfortunately, we didn’t hit the numbers we needed.
Slim down or shut down?
Looking back, I’m glad we didn’t hit those numbers. If we’d doubled down, raised more money, and appeared in the headlines again, there would have been a very real possibility of even more spectacular failure.
With that off the table, our options were:
Shut down the business, return the remaining money to investors, and try something new.
Continue with a slimmed-down version of the company to aim for sustainability.
Position the company for an acquihire.
Some of my investors wanted me to shut down the business. They tried to convince me that my time was worth more than trying to keep a small business like Gumroad afloat, and I should try to build another billion-dollar company armed with all of my learnings — and their money.
I tended to agree with them, to be honest. But I was accountable to our creators, our employees, and our investors — in that order. We helped thousands of creators get paid, every month. About $2,500,000 was going to go into the pockets of creators — for rent checks and mortgages, for student loans and kids’ college funds. And it was only growing! Could I really just turn that faucet off?
If I sold the company, it would be mostly for our stellar team — and I would no longer be able to control the destiny of the product. There were too many acquisition stories of companies promising exciting journeys and amazing synergies to come — and ending with a deprecated product a year later.
Selling was certainly tempting. I could say I sold my first company, raise more money, and do this all again with a new idea. But that didn’t sit right with me. We were responsible to our creators first. That’s what I told every new hire and every investor. I didn’t want to become a serial entrepreneur and risk disappointing yet another customer base.
We decided to become profitable at any cost. The next year was not fun: I shrunk the company from twenty employees to five. We struggled to find a new tenant for our $25,000/month office. We focused all of our remaining resources on launching a premium service.
In June 2015, a few months before our layoffs, our financials looked like this:
Revenue: $89,000 for the month
Gross profit: $17,000
Operating expenses: $364,000
Net profit: –$351,000
A year later, in June 2016, our monthly numbers looked like this:
Revenue: $176,000 for the month
Gross profit: $42,000
Operating expenses: $32,000
Net profit: +$10,000
It hurt, but it meant creators would keep getting paid. It also meant that we were in control of our own destiny.
From skeleton crew to lifestyle business
It got worse from there.
Gumroad was no longer the venture-funded, fast-growing startup our investors and employees signed up for. As everyone else found other opportunities, the skeleton crew fizzled from five to one.
I was basically alone. I didn’t have a team, nor an office. And San Francisco was full of startups raising gobs of money, building amazing teams, and shipping great products. Some of my friends became billionaires. Meanwhile, I was running a “measly” lifestyle business. It wasn’t what I wanted to do, but I had to keep the ship from sinking.
Now, I understand some people would dream to be in that position. But at the time, I just felt trapped. I couldn’t stop, but there was only so much I could do as an army of one.
For years, my only metric of success was building a billion-dollar company. Now, I realize that was a terrible goal.
I shut off the rest of the world. I didn’t tell my mom about the layoffs — she had to read the article and tweets herself to find out. My friends were worried, but I assured them I was neither depressed nor suicidal. I left San Francisco for long stretches at a time, thinking that some travel would give me adequate distance. It only made me more lonely.
Every day, I woke up and took care of all of Gumroad’s support queries. I tried to fix all of the bugs I could. Often, I had to ask for help from former Gumroad engineers. They were all employed by then, but they always found time to help. Once all things Gumroad were taken care of, I tried to go to the gym, and if I had the willpower, work on a side project (a fantasy novel). Most days, I failed.
To me, happiness is about an expectation of positive change. Every year before 2016, there was an improvement in my expectations — in the team, the product, or the company. This was the first time in my life when the present year felt worse than the last.
Then one day, everything changed. Again. I’m wary about sharing this part of the story, because I don’t know if there is anything to learn from it. But it happened, so here it is.
On November 27, 2017, I got this email from KPCB, our lead investor:
I am following up our conversation a few months ago. KP would like to sell our ownership back to Gumroad for $1. Can we discuss this week?
Mike had left KPCB to start a new company, and KPCB didn’t want the operational headache of appointing a new board member. Plus, it helped their taxes. In one fell swoop, our liquidation preferences (how much we would have to sell for before dollars started going to employees) went from about $16.5M to $2.5M. All of a sudden, there was a light at the end of the tunnel. Small, dim, and far away, but present. There was a path to an independent business, not beholden to the go-big-or-go-home mentality I signed up for when I raised money.
One investor joined them. We’ve bought back a couple more, since then. I keep the rest of the investors up-to-date with a brief email every few months.
The future came into focus: I could grow a small team, slowly buy back our investors, and build Gumroad into a meaningful business focused on our creators. We would never become a billion-dollar company, and that started to feel okay. Certainly, the thousands of creators selling on Gumroad wouldn’t mind.
Finding new forms of impact
The eight years I worked on Gumroad were full of personal ups and downs. There were months where I worked 16 hours a day, but there were also some months where I worked four hours a week. Here’s one way to picture that time:
Can you tell which is which? I can’t. We had a sales team for a few years, then we didn’t. Can you tell when we made the switch? I can’t.
It doesn’t matter how amazing your product is, or how fast you ship features. The market you’re in will determine most of your growth. For better or worse, Gumroad grew at roughly the same rate almost every month because that’s how quickly the market determined we would grow.
Instead of pretending to be some sort of product visionary, trying to build a billion-dollar company, I’m just focused on making Gumroad better and better for our existing creators. Because they are the ones that have kept us alive.
Creating and capturing value
At a CEO Summit many years ago, my all-time hero, Bill Gates, took the stage. Someone asked him how he dealt with failing to capture so much value. Microsoft was huge, sure, but tiny compared to the total impact it has had on the world and on humanity.
Bill’s answer: “Sure, but that’s true with all companies, right? They create some value and succeed in capturing a very small percentage of it.”
I am now more focused on creating value than capturing it. I still want to have as large an impact as possible, but I don’t need to create it directly or capture it in the form of revenue and valuation.
Take Austen Allred, for example. He’s raised $48M for his startup Lambda School, and he got his start selling a book on Gumroad.
Startups have been founded by former Gumroad employees, and dozens more companies have been massively improved by recruiting our alumni. On top of that, our product ideas, like our credit card form and inline-checkout experience, have proliferated across the web, making it a better place for everyone — including those that have never used Gumroad.
While Gumroad, Inc. may be small, our impact is large. There is, of course, the $178,000,000 we have sent to creators. But then there’s the impact of the impact, the opportunities that those creators have taken to create new opportunities for others.
Opening up about our financials
I’ve found other ways to create value, too. After the layoffs, I didn’t talk to anyone about Gumroad. Not even my mom. And after moving away from San Francisco, I felt pretty disconnected from the startup community.
As a way to re-engage with the community, I thought about sharing our financials publicly. Founders starting their own companies could learn from our mistakes, utilizing our data to make better decisions.
It was scary: What if we don’t grow every month? It could scare off prospective customers. It’s something I would never expect a startup seeking venture capital to do. It makes sense to hold those cards as close to your chest for as long as possible when you must raise money, hire people, and compete for customers with other venture-seeking startups.
But, since we were not any of those things anymore, it was easier to share that information. We were profitable, and a no-growth month won’t change that. So in April 2018, I started to release our monthly financials publicly.
Ironically, more investors have reached out (we’re just interested in raising money from our customers for the moment, thanks!), more folks want to contribute to Gumroad, and our shift in focus has brought us closer to our creators.
And instead of freaking out about how “small” Gumroad actually is (like I thought they would), our creators have grown more loyal. It feels like we’re all in this together, trying to earn a living doing what we love.
Soon, we’re also planning to open-source the whole product, WordPress-style. Anyone will be able to deploy their own version of Gumroad, make the changes they want, and sell the content they want, without us being the middleman.
In 2018, we donated over $23,775 (eight percent of our profits) to different causes. We raised money for the hurricane relief efforts in Puerto Rico and the floods in Kerala. We helped fund the Presence-of-Blackness project in speculative fiction, and a Mexicanx publication.
Seeking the non-binary
For years, my only metric of success was building a billion-dollar company. Now, I realize that was a terrible goal. It’s completely arbitrary and doesn’t accurately reflect impact.
I’m not making an excuse or pretending that I didn’t fail. I’m not pretending that failure feels good. Everyone knows that the failure rate in startups — especially venture-funded ones — is super high, but it still sucks when you don’t reach your goals.
I failed, but I also succeeded at many other things. Gumroad turned $10 million of investor capital into $178 million (and counting) for creators. Without a fundraising goal coming up, we’re simply focused on building the best product we can for our customers. On top of all that, I’m happy creating value beyond our revenue-generating product (like these words you’re reading).
I consider myself “successful” now. Not exactly in the way I intended, though I think what I’m doing now counts.
Where did my singular focus on building a billion-dollar company come from in the first place? I think I inherited it from a society that worships wealth. I don’t think it’s a coincidence that Bill Gates was my all-time hero and the world’s richest person. Ever since I can remember, I’ve equated “success” with net worth. If I heard someone say “that person’s really successful,” I didn’t assume they were improving the well-being of those around them, but that they’d found a way to make a ton of cash.
Wealth can be a measure of being able to improve the well-being of those around you, as seems to be the case for someone like Bill Gates, who has invested heavily in philanthropy. But it’s not the only way to measure success, nor is it the best one.
There’s nothing wrong with trying to build the next Microsoft. I personally don’t think billionaires are evil. And there’s a part of me that wishes I was still on that path.
But for better or worse, I’m on this one now. This has been my path to notbuilding a billion-dollar company. There are many like it, but this one is mine.
About the Author
Let me know if you have any questions. I’m happy to help, or at least to listen.
This submitted article was written by Sahil Lavingia, Founder and CEO of GumRoad.
The product vision is the single most important goal that you are aiming for with your product. It is the reason that the product is a reality and without it, your product is a side project.
You should not be the only person who knows what the product vision is. The whole team should know your product vision and use it in making the product a success.
Product Principles are a set of beliefs and intentions that reflect your team’s values and vision. These can provide direction to the team and in understanding what is important to the team (and the product). Also, it can serve as a base for inspiring product features.
These are not Design Principles which help designers find ways to enhance usability, influence perception, increase appeal, teach users, and make sound design decisions during projects.
Product principles help provide direction and guidance to the whole of the product team. This should enable the team to focus on developing inspiring product features that are important to your product.
Key metrics help teams check the success of their product. They also help stakeholders determine how customers are interacting with a product, the value it brings a company and how to improve it.
Key metrics need some sort of analytics. Exploiting the power of analytics will allow you to optimize your product to a new level. Key metrics let you see the results of changes that you make to your product.
“You can’t manage what you can’t measure. If you can’t measure it, you can’t improve it.” — Peter Drucker
Google’s HEART framework is great for defining product metrics that follow from product goals. It enables a product team’s decisions to use the insights gained from data.
If you want to build a better product, feature, or service you need to understand who your audience is. You also need to know their behaviour, what frustrates them, motivates them, and what makes them happy. You need to have an understanding of these people and how they are different from you and your team. Let us be clear, you are not your users!
You will answer this question through analytics, interviews, and you can create proto-personas to keep the team aligned with the different types of users your product has.
You need to find out “how many people are actually using our product’s features?”. To answer this question you will need to perform a feature audit of the product. A feature audit is a powerful tool as it will let you focus your work on the areas where it will have the most impact.
Improving a feature knowing that you’re trying to increase adoption lets you measure the results and avoid pushing features for the sake of it. It also identifies the features do not need to be worked on and should be removed from your product.
A product roadmap is a high-level plan that describes how the product is likely to grow over a certain period of time. It should communicate where the product is going, why it is going in that direction, and what goals you what to achieve.
A roadmap is great for communicating and aligning stakeholders to where the product is going over the next year and what the team will be developing.
“Building a great product is hard. Building a great product without a plan that accounts for iteration, quality control and user feedback is nearly impossible.” — Janna Bastow
I wrote an article before about a better way of building a product roadmap. The following is from that article.
Let’s face it, planning is guessing. Long-term planning is so unpredictable (due to the unknown) that unless you’ve got a DeLorean time machine you’re better off not doing it.
Deciding on what you’re going to build in Q4 (before the year has even begun)is a surefire way of building the wrong thing. Shouldn’t you spend your time building what’s important at the time? I think so.
Instead of using a yearly roadmap broken into quarters, use a “Current, Near, and Future” roadmap.
Recently, Silicon Valley has been abuzz with an article penned by legendary Sequoia Capital VC Mike Moritz. Titled “Silicon Valley would be wise to follow China’s Lead,” Moritz details how a recent trip to China highlighted the differences between Mainland Chinese tech culture and Silicon Valley — and ultimately why the former contains elements for success that the latter would be wise to follow.
While in the general case I think this is true, the content of Mike Moritz’s editorial has ignited a firestorm of controversy. Moritz contends that the Chinese tech worker is more successful because of their superior “work ethic”, citing the following as evidence of a serious divide in dedication between engineers in the PRC and staff in Silicon Valley that lead to the former being better positioned for success than the latter:
Chinese tech workers work longer (“Here, top managers show up for work at about 8am and frequently don’t leave until 10pm. Most of them will do this six days a week — and there are plenty of examples of people who do this for seven.”) to the point that they willingly accept “unhealthy” work environments and “work with a “disregard paid to physical fitness.” Mike particularly highlights the difference between Silicon Valley engineers and their counterparts in Shanghai and Beijing here: “[Chinese] Engineers have slightly different habits: they will appear about 10am and leave at midnight.”
Chinese tech workers are willing to put their job and their company before their family. Workers do not question (or even seek) “the appropriate length of paternity leave or work-life balance,” and Moritz praises this dedication: “such high-flyers only see their children — who are often raised by a grandmother or nanny — for a few minutes a day.”
Chinese tech workers are frugal and more focused. “You don’t see $700 office chairs or large flat panel computer screens at most of the leading technology companies,” he writes. “Instead, the furniture tends to be spartan and everyone works on laptops.” Mike also notes that Chinese engineers don’t need to worry about having “jam sessions,” and other luxuries that their American counterparts employ.
As a result of such contentions, Mike’s commentary has invited tsunami of repsonses across the Silicon Valley and SF tech community.
This isn’t to say that all of the response to Mike Moritz’s contentions are negative. I’ve had a series of conversations (mostly with other VCs) who are supportive of at least Mike’s possible intentions with this post. Some Chinese-American entrepreneurs have also come out to defend Mike’s commentary, noting that the work ethic and dedication in Chinese tech is an admirable result of habits in Chinese millennials who are the sons and daughters of those who lived through the Cultural Revolution.
I’ve spent a good amount of time working with Chinese tech companies. As an associate at GGV Capital with a unique technical background, I had the opportunity to work very closely with Chinese companies such as YY and collaborated with my colleagues in Shanghai on evaluating investments in cloud infrastructure, enterprise tech, and gaming.
Because of the rise of enterprise and cloud infrastructure in China in the last decade, much of my operational career outside of VC has also involved working with Chinese high tech companies. As NetApp’s product manager for cryptography and compliance, I worked closely with NetApp co-founder James Lau and our head of corporate counsel on evaluating legal issues and compliance issues around selling our products with security features in China. At HashiCorp I also work on similar issues.
After Moritz’s article came out, I’ve had a series of conversations with friends and old colleagues of mine who have similar — and frequently better — experience working in and around Chinese tech society. After all of this (and a few nights sleeping on the issue) I feel confident in the following:
Mike is right that Silicon Valley can, and should, learn lessons from Shanghai and Beijing tech cultures. But none of those lessons have to do with the unhealthy dedication to work he praises in his article.
In fact, Mike Mortiz’s article instead highlights deep informational divides about engineering and product development between non-operator, non-technical VCs and the Silicon Valley engineers who work at companies funded by them.
Context is for Kings
Before we get into all of this, it’s important to set the context for this article. Mike Moritz’s article comes at a time when it feels like Western tech culture (particularly Western VCs) are just starting to appreciate the value of Chinese technology companies and startups.
Commentary about Chinese tech from Western investors has spiked over the last 18 months, and reflections on Chinese tech from investors like Moritz or Jason Lemkin’s commentary below are becoming more common.
This is not serendipitous. Most venture investors, really all of the good ones, use social media and personal branding as mechanisms to source and better competitively position themselves when competing against other VCs.
Content marketing isn’t nefarious. Cultivating a personal brand and strategically developing content is an important part of the job in modern venture capital. It helps entrepreneurs to get to know “you” and what you’re interested in, and improves the efficiency of sourcing, dilligencing, and ultimately funding tech startups on both sides of the table.
The rise of western VC content marketing on China is ostensibly a response to the extremely price and sourcing-competitive nature of Silicon Valley tech — and a time of uncertainty for the asset class of venture capital as a whole.
According to Cambridge Associates, US VC performance (a market dominated by Silicon Valley venture) in 2017 was “middling” compared to its other counterparts in private equity. US VC funds within the CA index in 2017Q2 returned only 4.7% YTD — almost half of what similar money invested in other areas of Private Equity would have made (7.6%) or even investment in the NASDAQ Constructed mPME.
Performance like this has anecdotally given pause to LPs and fund of funds who supply venture firms with the capital to invest. There is a concern that larger institutional LPs may revise their allocations in VC in the upcoming next few years. For VCs looking to raise money (or more in the case of Moritz, a largely non-investing partner at Sequoia who presumably is incentivized for the fund to just continue to succeed), it is very important to find ways to beat this “middling” average and return far north of the 2.6x CoC return norm.
The best way to do that has been to look outside of Silicon Valley for new investments. Areas like Seattle, New York, Austin, Tel Aviv, and the UK have sparked with venture investment from Silicon Valley firms looking to find less competitive markets with better arbitrage opportunities in local startup valuations.
China is one of those places. While startup enclaves like Beijing and Shanghai already have local VCs with deep pockets, the combination of a potentially less competitive market for valuations (Chinese guanxi business culture has much more wiggle room for valuation than Silicon Valley’s multiples-based approach given high NTM TEV/REV in US tech markets) with mature, capable technology has been very attractive for US investors who look to wield their own deep pocketeted-funds and attract startups with favorable content marketing and promises to help shepherd their entrance into US markets.
There’s just one problem with this recent Chinese enthusiasm for Western VCs new to China: China is not Silicon Valley, and not respecting the major economic and cultural differences of the two in comparing each’s tech cultures leads to erroneous assessments like those found in Mike Moritz’s article.
Comparing Apples to Mandarin Oranges
When I first started working with Chinese tech companies, I began hearing a phrase that has been repeated in a variety of different forms over the years:
Major differences in population (New York has the population of a tier 2 Chinese city) and a very different political, social, and economic culture lend themselves to a very different components for success in China. Western failures to appreciate, and most importantly respect, these differences can and do lead to failure.
Mike Moritz’s article seems to tread a familiar path in not respecting these cultural differences. For example, while he praises the insane work culture of Chinese tech and notes it has to do with something culturally Chinese, he fails to appreciate that it is not an enviable part of Chinese culture and instead is a fatalistic holdover from aspects of the cultural revolution.
Many Chinese tech workers aren’t working these hours because they want to — they’re working them because they feel/actually have to in order to adequately support their families. As one of my friends from Shanghai noted, Chinese families who survived the Cultural Revolution have embraced the need to “eat bitterness” and sacrifice for their families to survive.
If you read Mike’s article, you might think that the Chinese software engineer willingly embraces the 70–100 hour work week and casually leaves their family behind. In reality, many Chinese engineers work these hours because these are simply the table stakes of their field and they need to work them in order to help their family prosper.
They are eating the bitterness of the environment, and trying to attribute their success by simply comparing hours worked to their American counterparts both fails to appreciate these cultural differences and is even somewhat insulting in its lack of respect for the major cultural differences and differences in history of industrialization in China and the United States.
For many of my Chinese friends who worked in China and had a visceral response to Mike Moritz’s article, this is what set them off the most. Sacrificing personal health and relationships with their children and spouses is by no means enviable, and Mike Moritz’s tone deaf praise of such behavior fails to appreciate that Chinese history and that Chinese tech is an accomplishment in spite of such adversity rather than a consequence of it.
Mens Sana, C[ode] Sana Est.
Furthermore, while Mike Moritz was willing to comment on how perceived cultural differences between Silicon Valley engineers and their more frugal and seemingly harder working counterparts in China led to success, Mike fails to review why this is the case.
Why, for example, when we’re using the same programming languages and frequently the same software engineering methodologies is Chinese tech superior to its Silicon Valley counterparts? What variables in Chinese software development are different — is it just the difference in hours? Are there differences in software design methodologies?
The reason why Mike doesn’t go into such detail in his writing here is simple: he doesn’t know.
While Mike Mortiz has had a legendary history in venture capital investing, he’s never been a software engineer or physically written and shipped production code. Prior to joining Sequoia, Mike Moritz was a writer for Time Magazine who covered figures like Steve Jobs.
For what it’s worth this isn’t a bad thing. There are serious contentions about whether or not modern VCs need to have operational experience. But as a VC with operational experience, I don’t think you do. I’ve met wonderfully successful VCs (even those who invest in heavily technical areas) who have never compiled “Hello World” and have been an elemental part of advancing high tech through their investments.
That being said, the moment those individuals start talking about engineering culture they wade into an area where they don’t have personal experience. At best, non-operational VCs without experience building and shipping code can highlight the experiences of others who do have operational engineering experience in citing their commentary.
But you run a dangerous line in trying to make strong contentions about the practice of software engineering without actually having worked in the field, and here again Mike Moritz’s rush to conform his limited exposure to generaization has led to error.
To make a very long story short: software engineering is weird. Despite the practice being nearly four decades old, we still struggle to find good ways to quantify and streamline the field — even from the inside.
But while we’re still figuring out how to operationalize and improve effectiveness software enigneering, we’re very aware as an industry and culture of ways to hurt our effectiveness. I’m reminded of a great blog post from an early Twitter engineer on his experiences watching the company deal with scaling and growth per-IPO:
I think a big part of the problem is that we — as an industry — are not very good about thinking about how to make engineers effective. For our software, especially back-end software, we can measure its goodness by the number queries per second it can handle, the number of incidents we experience, and the amount of hardware we have to buy to run it. Those things are easy to measure and even fairly easy to tie to financial implications for the business.
Engineers’ effectiveness, on the other hand, is hard to measure. We don’t even really know what makes people productive; thus we talk about 10x engineers as though that’s a thing when even the studies that lead to the notion of a 10x engineer pointed more strongly to the notion of a 10x office.
But we’d all agree, I think, that it is possible to affect engineers’ productivity. At the very least it is possible to harm it.
One area where we know effectiveness gets harmed is in overwork. Producing quality, production code is not simply a sack race, and forcing engineers to work longer hours with the idea that product quality and features scale linearly with time worked is a frequent misunderstanding from laymen who do not have experience working in tech.
There have been a number of studies into the ineffectiveness of overwork in software engineering. One area notorious for overwork leading to problems is in video game development, where the infamous “crunch” of a pre-release breakneck sprint of work can lead to major problems in game quality on release.
In 2006, a post from the spouse of an engineer at Electronic Arts sparked a controversy about the ineffectiveness (and unhealthiness) of crunch. This sparked a study by economists and computer science grad students at Stanford, which yielded strong empirical evidence of the dangers of “crunch” engineering.
The result of their analysis was simple: engineers working too long started to see deminishing returns on code quality and production. As the project discovered:
Thus, overworked employees may simply be substantially less productive at all hours of the work day, enough so that their average productivity decreases to the extent the additional hours they are working provide no benefit (and, in fact, are detrimental).
Further studies into labor economics and software engineering have revealed similar results into the long-run detriment of employing “crunch” development. According to a paper submitted to the IGDA analyzing Stanley Chapman’s work in labor economics to software engineering:
Chapman’s diagram of the work curve assumes that a working day of a given length is maintained over a considerable period of time. Thus it incorporates both simple and accumulated fatigue into its model. At first the declines in output per hour simply reflect the effects of fatigue on both quantity and quality of work performed toward the end of a given day. But eventually daily fatigue is compounded by cumulative fatigue. That is, any additional output produced during extended hours today will be more than offset by a decline in hourly productivity tomorrow and subsequent days.
Studies like these have provided empirical insight into why software engineering effectiveness does not linearly scale with hours worked. But experience programming also yields similar results.
Sometimes you run up against difficult problems that you can’t solve on a first pass. After a while, working more to try to solve that problem is counter-productive. The common response to these problems is simple: take a break. Step away from your computer and your copy of the CLRS, and do something to take your mind away from the problem so you can come at it fresh and rejuvinated.
The importance of small breaks and a positive work environment where engineers are not overstressed or overworked cannot be understated. This is why places like Google and Facebook are bastions of engineering innovation: they recognize why investment in engineer well-being is elemental to innovation and success, and are willing to invest millions in things like rooms for “jam sessions” because they will receive billions in return on new products and services built by their happier, more effective engineers.
Again, Mike Moritz’s lack of deep personal experience and generalizations about his exposure to the subject have led him astray. You can’t just force an engineer to work more hours to be effective.
So if that’s the case, why is China technology so successful? Why have companies like Tencent seen such roaring success both in China and abroad.
Again, the devil is in the details — details Mike Moritz unfortunately doesn’t go into in his article.
To the Stars, Through Adversity
During one of YY’s visits to the United States, I had an opportunity to spend time with their senior leadership team as they attended a series of roadshow meetings with industry analysts in preparation for their IPO.
I was astonished to learn how advanced YY’s technology was compared to the norm of Silicon Valley infrastructure tech at the time. While Silicon Valley was just being introduced to mainstream distributed computing, YY was using concepts in grid computing and graph-theoretic workload optimization to streamline synchronous communication between their users. We are just now starting to focus on gossip protocols and decentralized computing in Silicon Valley with the rising interest in cryptocurrencies and blockchains. But these were table stakes areas of tech for YY since the late 00’s.
When I asked how YY was able to pull off creating and integrating such advanced infrastructure technology so early, I remember YY’s CTO shrugging and simply noting that it was simple: “we have to.”
China’s advances modern consumer internet is ridden on the back of a surging explosion of domestic infrastructure tech. In a country where major portions of the population sit in areas with unreliable connectivity, Chinese tech companies have had to innovate radical solutions in order to serve what we consider normal levels of service in connected applications.
Even more impressively, the scale at which Chinese technology companies (even new startups) operate on is almost unfathomable to most US startups. In a country where there is easily more than a billion people than the total population of the United States, growth-stage Chinese startups regularly deal with user traffic on daily volume of YouTube. Maintaining autoscaling and performance optimization to deal with the deluge in traffic is critical — and normal — for Chinese engineering organizations.
In response, many Chinese technology companies began to invest in distributed computing over GPUs. Suites like Nvidia’s CUDA gained acclaim in China for their general purpose compute properties. And as a result, China quickly became the home for most Bitcoin mining as the country already had a GPU/Cell-computing expertise due to their need to solve HPC problems that otherwise couldn’t be addressed with using US-banned high performance traditional processors.
As China and Austria recently celebrated the launch of the first quantum key distribution system in production (a poignant advance given the US’ history of restricting cryptographic technology outside of their partners in NATO) it is once again made clear that Chinese tech is built from a hacker culture of innovate or die.
TL;DR
There are a lot of things Silicon Valley can learn from Chinese tech culture. But in learning from Chinese tech we need to appreciate why Chinese tech is successful and innovative.
Chinese tech is successful because Chinese technology companies have been forced to innovate or die. In a world where “Facebook”-level scale issues are normal, Chinese engineering culture has had to develop radical solutions just to survive. As a result, successful Chinese technology companies harbor a laser focus on innovation and technical substance. Disengenuous hype with minimal technical substance, such as what we’ve seen in Silicon Valley around Bitcoin, is downplayed in importance or discarded.
But contrary to what Mike Moritz has contended in his review of Chinese tech, Chinese tech is not successful because of difficult work environments. As we have learned ourselves throughout the 40 year history of software engineering in the US, simply adding on the hours for engineers does not yield a better product.
Silicon Valley engineers are not lazy, spoiled, or entitled: they are simply different than their Chinese counterparts. If we want to learn from each other, we need to respect those differences and appreciate them.
Whether it’s breaking Enigma or factoring large primes to attack Soviet naval codes, cryptography has historically dragged computing kicking and screaming into major advances in algorithms and the hardware necessary to run them.
Quantum computing is no exception. Computing math using qubits, spinning sub-atomic particles that can theoretically hold infinitely more states compared to the 2 states held in digital transistors used in modern computers, has become an exciting next chapter in the story of applying computer science towards solving hard problems in areas like artificial intelligence and scientific analysis.
But while recent excitement has been building around using quantum computers to simulate random walks down Wall Street or run millions of simulations to teach computer vision systems to recognize whether or not something is a hot dog, quantum computing’s initial application (and the driving impetus for its funding from the US) is cryptography and code-breaking.
“Mathematics is Truth” — RZA, Wu-Tang Clan
Key generation and exchanges used for generating ephemeral encrypted sessions (such as those used in the OpenSSL implementation of TLS) are vulnerable to new types of mathematic attacks from quantum computers
Most of the encryption used by modern cryptography is protected by computationally difficult math problems.
For example, the popular OpenSSL suite protects against the compromise of internet traffic shuttled through its protocol by using ephemeral session keys.
These session keys ensure that the key being used for a “conversation” between a client and a server can’t be intercepted and later, with access to the server’s long term certification credentials, be decrypted.
This practice is called Perfect Forward Secrecy or PFS. And while PFS was originally formulated at the end of the Cold War to respond to the dramatically-increasing power of computer systems and the infancy of hacking, perfect forward secrecy is an absolutely critical aspect of modern information security given the surging rise of data breaches over the last five years.
Data breaches like those that struck Target and Home Depot could lead to the theft of a server’s credentials, and it is critical to enforce PFS on secure communication between clients and servers in order to ensure that a successful attack doesn’t yield sensitive client data like health records or credit card numbers.
Ephemeral session keys in OpenSSL tend to enforce PFS by using the Epehemeral Diffie-Hellman algorithm, or EDH. EDH works by having two authenticated parties generate a series of random numbers and raise a publicly-known value to the power of each’s randomly chosen number.
The Diffie-Hellman algorithm. EDH ensures perfect forward secrecy by requiring Alice and Bob to generate X and Y randomly.
To establish the session key, both parties then exchange their resulting value and multiply them together. By the Laws of Exponents, the resulting value each would get would be the same. This is their ephemeral session key.
There are two ways to attack this cryptography. First, you can attack how the random numbers are generated. Sometimes the methods used to create these numbers can be tampered with, leading to an attacker substituting their own keys in the transaction.
But beyond this attack which avoids the encryption itself (something we call a side-channel attack in cryptology) there’s the “head-on” approach: break the math used to encrypt the session key. In cryptography this is called cryptanalysis, and prior to modern quantum computing it was computationally impossible to employ on EDH.
To cryptanalytically attack EDH, we need to compute something called a discrete logarithm (or simply discrete log). A discrete log is a much more complicated version of the logarithms that many of us learned about in high school algebra; much like normal logarithm it represents the power that one needs to raise a base number to yield a result.
But a discrete log further complicates things by forcing one to take the logarithm of a finite group rather than a single number. In math-speak, this means that we’re taking the discrete log of a set of components used to compute a result. In reference to EDH, this means we’re trying to find the two keys that are used and generated by each party.
Computing a discrete log is computationally intractable. While normal logarithms can be calculated so easily that pocket calculators typically include LOG functions, computing a discrete log requires a computer to arduously search for the set of inputs for the finite group used in the exponent of the base. And given that EDH typically selects large, prime integers (and that there are literally an infinite number of integers), this could take a while.
That is, unless we decide to whip out Shor’s Algorithm.
Break Glass in Case of Discrete Log
Shor’s Algorithm exploits unique properties within quantum mechanics that could allow hackers to stealthily break encryption protected by the difficulty factoring large prime numbers
Shor’s Algorithm is a quantum computing algorithm that uses a property in quantum mechanics called superposition to dramatically speed up how the search for a set of numbers.
Superposition refers to a peculiar property where a quantum variable can exist in multiple states of existence. In quantum computing, this means that the qubits used by quantum computers can occupy far more than the 2 states used in binary transistors by modern computers.
Shor’s exploits superposition by allowing a quantum computer to search for prime factors orders of magnitude faster than its digital counterparts ever could. This search, when applied to EDH, allows a quantum computer to reduce the time needed to compute a discrete log from “longer than it will take to reach the theoretical heat death of the universe” to potentially days or even hours.
That means that in the very near future an adversary with access to a quantum computer could capture all of your web traffic encrypted over suites like OpenSSL and, using a quantum program that employed Shor’s Algorithm, steal all of your information regardless of your encryption without ever alerting you or the server you were communicating with.
Yikes.
Indistinguishable from Magic
Recent advances in quantum computing have brought new forms of encryption that, until recently, were simply tropes in science fiction
Quantum computing is by no means a purely destructive force in security. In addition to providing new means of breaking existing cryptosystems, it also provides new opportunities in securing and transmitting data.
One of the most exciting opportunities afforded by quantum computing is in quantum encryption. This is a nascent field that looks at using curious properties of quantum mechanics (like how Shor’s Algorithm exploits superposition) in new ways for securing the transmission and storage of sensitive data.
The hottest area in quantum encryption lies in secure communication using quantum entanglement, a property of quantum mechanics where a pair of particles or group of particles are subject to an interaction that results in them all having non-independent states. This means that in order to describe the state of a single particle in the pair/group, you need information from all of its entangled twins.
Even more interesting, the process of entanglement seems to ignore all convention of distance: particles remained entangled (or to use the physics term, remain coherent) regardless of how far away they are in space. This means that a subset of an entangled group could theoretically move an infinite distance away and reflect changes to its counterparts immediately with no time lag or loss of data. Or, as Einstein put it, entangled particles exhibit “spooky action at a distance.”
Such properties are extremely valuable in cryptography. Entanglement provides the opportunity to create a lossless, high performance encrypted communication network that is impervious to compromise and otherwise near impossible to even detect.
While much of this sounds like something from Star Wars (and is literally how instantaneous faster-than-light communication is performed in the video game Mass Effect 2), science is rapidly advancing in quantum mechanics to allow us to reliably wield quantum entanglement as a means of “teleporting” information. Recent studies have confirmed the existence of these once theoretical properties, and scientists have even wielded “spooky action” to “teleport” data remotely and instantaneously across small distances.
Creative Destruction
The dilution refrigeration chamber inside of a quantum computer. Serious structural and scientific barriers still inhibit our ability to easily exploit quantum mechanics in infosec and cryptography, but the science and engineering behind quantum computing has brought quantum cryptography surprisingly close to reach.
Quantum cryptography and quantum “hacking” are some of the most exciting and disruptive topics in cryptography, security, and computing as a whole. But there are serious challenges to overcome before we enter a world where teenagers wielding 100+ qubit quantum computers are stealing everyone’s personal information.
Interacting with any quantum system is tricky. Quantum states are notoriously finicky, as they’re subject to Heisenberg Uncertainty (or simply “uncertainty”). Uncertainly shows that any attempt to interact with or measure a quantum state irrevocably changes it.
As a result, quantum computers have to take extreme steps to work. Quantum computing interactions are typically conducted in secured environments where temperatures are kept close to absolute zero in order to minimize the impact that other variables may have on a quantum system. Conducting the interaction also impacts the state of the system, forcing quantum computers to have to conduct a single operation then immediately wipe the entire array of qubits and start over before moving onto their next step.
Even worse, the output of a quantum operation is far from definitive. Because of uncertainty, there is a high probability that a single output of an operation may not reflect the intended result of the computation. To compensate for this, quantum computers run a gauntlet of computations and tests in order to return a set of results that are probabilistically likely to be correct.
Running a quantum computer is difficult. It’s like running a laptop where, after every instruction in code, you need to smash open the bottom of it with an aluminum bat and install a new motherboard and processor. And even then, the output of what you get isn’t definitively correct. The result you compute is just — after you’ve bought and shredded several US states’ supplies of Intel processors and ASUS motherboards later — decently likely to be the right answer.
But these are problems we’re overcoming surprisingly well. And in the very near future (so close that algorithms like EDH are starting to fall out of favor in lieu of other quantum-resistant means of establishing/exchanging keys) quantum computers are going to redraw much of the landscape for cryptography and information security.
About the Author
Andy Manoske is an Amplifier at Amplify Partners and a product manager at HashiCorp. He currently manages the design, strategy, and product development of Vault, HashiCorp’s secrets management and cryptography suite. Previously he led the design and development of OTX (Open Threat Exchange) at AlienVault and managed cryptography and product security at NetApp.
Some of the best parts of my time at Mozilla were the moments of brilliant humility. We had assholes, to be sure, but there would be these moments…
20 people sitting around a conference table during an all hands. Each of them brilliant. Brilliant like I’ve rarely met before or since. The people in that room could reinvent the internet from sand and germanium on up if humanity ever misplaced the one we had. And every one of them in that room convinced that they were the dumbest one there.
It felt like we could out-think any problem. I loved those moments.
I was in one of those moments the first time I read the lead bullets essay. It’s a story borne out of Netscape, Mozilla’s origin myth, and still I almost skipped it. I don’t love war metaphors in business; they often feel callous and gross. But somehow this essay got through.
It kicked me in the gut. It lit something up that I had been feeling but hadn’t labelled. If you haven’t read it yet, go. It’s not long. I’ll wait.
What hurts about that essay is that I, and most of Mozilla, had gotten addicted to silver bullets. I don’t blame anyone in tech for falling into the same trap. Our industry is built around disrupting. We think different. It infuses our language and our value systems because it’s an incredibly powerful tool set.
People rag on “disruption” as an overused word, and I get that. But holy shit our industry really does blow up some stuff. It’s so rewarding to fix an entire class of problems. It reaffirms our belief that there are silver bullets out there if we’re smart enough to look for them.
Is your organization lured by the same temptation? Have you fallen into the same quicksand? It’s easy enough to figure out:
Imagine a problem comes up that would be labour-intensive to fix manually. You discover that thousands of new accounts haven’t been tagged with the right attributes. Or you have an API change that’s going to break a whole ecosystem of third party apps. How do you respond? How does your team approach the problem?
Everyone will, of course, look for the obvious smart fix first. Can we auto-deduce the missing tag? Can we shim the old API? Let’s say there isn’t one. Now what? This is where it gets really interesting. This is where the ground sinks beneath you.
Many companies, smart companies full of smart people, will keep looking for a way to think themselves out of it. They’ll look for a very, very long time. At small scales, this is the running joke of engineering. Every programmer has a story about the time they spent longer automating a task than it would take to just do it.
But some organizations spend years on this stuff. They spin up teams for it. They find a glimmer of hope, announce that a fix is on the way, and then delay over and over as they discover that the problem goes deeper. They are addicted to out-thinking. The sunk costs are devastating.
You don’t have years to spend on this stuff.
The Only Way to Win is Not to Play
The organizations that dodge this trap share a crucial adaptive trait. After that initial sniff test to see if there’s a quick way out, these groups stop chasing after an elegant solution and just do the grunt work. That’s it. They see the ground shifting under them as they start to contemplate a brilliant way across. And they stop it dead. They sense that it’s not safe. And they’re right.
Need to re-tag those accounts? Grind it out through a mix of partial, unsatisfying fixes and good old fashioned repetitive labour. Need a way to port those third party apps? Send some emails. See which ones you can convince to rewrite on their own, and volunteer your own developers to help fix the rest.
Do the hard work. Put the problem to bed, and move on. This isn’t news. I’m not the first to suggest that grinding works. It’s what Paul was on about, and he wasn’t first either. Neither was the lead bullets essay. But I still meet plenty of folks stuck in quicksand, so it seems the message bears repeating.
3% Wins.
Not all problems get the grunt work treatment. Sometimes incomplete solutions are more damaging and you do need to fix things the right way. A sign of a healthy team is that it should be easy to cite examples of each. But many teams, particularly those with engineer founders, strongly prefer “elegant” solutions to grunt work. And it’s where you can win.
While your competitors look for silver bullets, push through. You pay an upfront cost in ugly labour that your competitors won’t. But with your team no longer distracted, every day that goes by is a little incremental win. Not huge, 2 or 3%. For a big, really distracting problem, more.
Those wins add up. They compound. There is no greater miracle than compound interest in your favour. And there is no greater foe than compound interest pitched against you.
Your team will get better at finding these wins. It’s a different mindset to see grunt work and think “how can I grind this out efficiently,” instead of only ever asking “how can I obviate this problem altogether?” The 3% wins build up faster. And more of them are 5% wins.
The silver bullets do happen. Occasionally you find a 100% win. Once in a very long while you will find a 10x lift, a 1000% win. They’re worth looking for, at least some of the time. But the team doing the hard work and putting up those little wins every day is a lot more reliable. You can predict those marginal wins. You can plan for them. You get to the point where you can rack up a few 2%, and 3%, and 5% wins every week. Do the math over months and years. I’ll give you a hint: it’s far better than 1000%.
When speaking of startups, people tend to believe that they follow democratic rules of governance. They’re companies. They have founders, investors, a board, people vote, decisions are taken. All of this looks a lot like a small-scale democracy. At least that’s how it would work in any other company.
The truth is that startup are not normal companies. They’re dedicated to deliver high growth and high output. To achieve this, people believe that they need a pilot in control. Thus, the practice has arisen to grant super-voting to Silicon Valley founders, which can lead both to super-tight and efficient governance, or to “the ultimate job security” as Backchannel puts it.
Most part of the time, in most companies, one share equals one vote. But in many US startups, some shares are worth more votes than others. When an investor will control 1 vote, the founder will control 10 votes.
It’s called dual-class stock structure.
This is not new. Older companies have used this trick for centuries. Well-known companies such as Ford or Berkshire Hathaway allow founders, executives and family to control the majority shareholder voting power with a relatively small amount of total equity in the company. The dual class structure at Ford, for example, gives the Ford family control of 40% of the voting power while owning only about 4% of the company’s total equity. Berkshire Hathaway offers a B share with 1/30th the interest of its A-class shares, but 1/200th of the voting power.
Dual Class structures are so dangerous that many countries such as Germany, Russia, the UK and other commonwealth realms have laws/policies against multiple/non-voting stock. Continental Europe usually allow them under some limits.
The reason for their use in Silicon Valley is that there there is a history of CEOs being forced from their companies. Steve Jobs was famously ousted of Apple by John Sculley in 1985. Sean Parker got fired from Plaxo by its investors. Having these stories in mind, Google, Facebook, Zynga and others decided to follow a dual-class structure where their founder is in complete control of the company. Recently, Snap even issued a third class of shares that has zero voting control over the company.
In other words, startups are dictatures, not democracies. You’ve got one leader, usually male, usually white. The rest of the company obeys to him. His decisions are not open to discussion. In Silicon Valley parlance, being CEO amounts to being King these days.
And why not? As Peter Thiel explains, “We are biased toward the democratic/republican side of the spectrum. That’s what we’re used to from civics classes. But the truth is that startups and founders lean toward the dictatorial side because that structure works better for startups. It is more tyrant than mob because it should be. In some sense, startups can’t be democracies because none are. None are because it doesn’t work. If you try to submit everything to voting processes when you’re trying to do something new, you end up with bad, lowest common denominator type results.”
But this is all cool as long as the dictator is benevolent and efficient.
And as scandals multiply in the Valley, investors, journalists and the public in general will begin to question the current state of things.
Supporters of dual-class governance feel that the structure allows strong leadership to put long-term interests first while seeing beyond the near-term financial situation. Opponents of dual class structures feel it allows a small group of privileged shareholders to maintain control, even when they’re not successful or innovative.
It will be interesting to see how investors will react when some will understand that they invested billions in people who got lucky but don’t have as much vision and management skills as they thought.
In other words, there might be a governance bubble in the making, something different from the 2001 bubble, or the 2007 financial crisis, but with no less impact.
About the Author
This article was written by Jean Baptise Soufron, a Lawyer in Paris, and a former General Secretary of the French National Digital Council, he works in tech, media, public policy.
There’s nothing resembling a “sharing economy” in an Uber interaction. You pay a corporation to send a driver to you, and it pays that driver a variable weekly wage. Sharing can really only refer to one of three occurrences. It can mean giving something away as a gift, like: “Here, take some of my food.” It can describe allowing someone to temporarily use something you own, as in: “He shared his toy with his friend.” Or, it can refer to people having common access to something they collectively own or manage: “The farmers all had an ownership share in the reservoir and shared access to it.”
None of these involve monetary exchange. We do not use the term “sharing” to refer to an interaction like this: “I’ll give you some food if you pay me.” We call that buying. We don’t use it in this situation either: “I’ll let you temporarily use my toy if you pay me.” We call that renting. And in the third example, while the farmers may have come together initially to purchase a common resource, they don’t pay for subsequent access to it.
In light of this, we should call out Uber for what it is: a company in control of a platform that originally facilitated peer-to-peer renting, not sharing, and that eventually transformed into the de facto boss of an army of self-employed employees. And even as “self-employed employee” might sound like a contradiction, that’s the dark genius of the Uber enterprise. It took the traditional corporation, with its senior managers responsible for controlling workers and machines, and cut it in two — creating a management structure that need not deal with the political demands of workers.
So, how exactly did we get to the point where business executives at conferences can talk about Uber as a “sharing economy” platform with straight faces? How is it that they don’t feel a deep sense of inauthenticity? To understand this, we must return to the roots of the actual sharing economy. It is the only way we can wrest it back from those who have hijacked it.
Our everyday economic life is characterized by three things. First, you get a job at a company — or you start a company — and you produce something. Second, that company goes to market to exchange its product for money. Third, you use that money to get goods or services from others who are also producing. Zoom out, and a market economy is a large-scale network of interdependent production. We cannot survive without accessing the products of other people’s labor.
Monetary exchange takes the form of, “If you give me money, I will give you a service.” There’s always potential for rejection in market offers, which creates uncertainty, and some people fare better than others. Those who undertake the heaviest burden of production don’t necessarily get rewarded commensurately. Individual competition appears to be — at least at first glance — the defining mark of monetary exchange.
There are, however, three major but inconvenient truths that seem to get glossed over when we talk about the market economy. The first is that market systems feed off an extensive, underlying gift economy in which people transfer ideas, goods, services, and emotional support to each other without requesting money. Unpaid childcare is one example. If your mother watches your two children while you’re at a job, that’s the gift economy in action. In fact, without friends and family it’s unlikely that you could even maintain the desire to go to work. Even in professional settings we share common resources with business colleagues. Companies rely upon this internal collaboration to produce the very products they then competitively exchange in markets.
The second inconvenient truth about the market economy is that its products are not really desirable unless we can use them within non-market systems. What’s the point of all this stuff getting produced if we can’t share it, compare it, gloat about it, or enjoy it with others? Friends, family, and various community systems make having material goods meaningful.
And third, many commercial market exchanges are actually hybridized with non-commercial elements that add richness. Take, for example, flirting with a bartender as they serve you drinks, or having a discussion about politics with the stylist you’re paying to cut your hair. Not only do market systems rely on non-market influences in order to work, but their products feel pointless and empty without them. Recognition of this, however, is uneven.
In small community settings it’s often easy to see a balance between market and gift economies. The shop owner gives a spontaneous discount to a retiree, or allows friends to lounge in a coffee shop long after they’ve finished drinking. Commercial exchange is but one element in a broader set of relationships, and this means the exchange takes longer. Economists call this inefficient; we call it enjoying life.
Meanwhile, in megacities such as London or New York there’s a tendency to strip all non-commercial elements from market interactions. This is the hallmark of what we refer to as commercialization. The large-scale mall and corporation are designed to maximize exchange while offering only a shallow appearance of sociability. The McDonald’s employee is forced by contract to smile at you, but prohibited from taking time to have a true conversation.
This phenomenon is even more acute in faceless internet commerce, where clinical, transactional precision dominates. While hyper-efficient exchanges play into our short-term impulses — initially feeling exciting, convenient, and modern — they gradually begin to feel empty. Sure, it’s frictionless commerce, but it’s also textureless.
When detached from a community foundation, markets can bring out people’s most anxious, petty, arrogant, and narcissistic sides, encouraging them to fixate on their individual strands of the overall economic picture, as if it were the whole. The defining qualities of a market economy — like uncertainty and unequal monetary reward — get exalted, and in this frame, everyone else is either a stranger to do battle with or a temporary ally to assist in your personal gain. Socializing becomes “networking.” Non-commercial ties such as friendship, sex, love, and family are either rendered invisible, or presented as kitsch advertisements designed to promote more commercial exchange.
It was in this context that the original sharing economy platforms emerged. Amid the competitive, individualistic rhetoric of the corporate state, people looked to use technology to foreground sharing, gifting, and community activities that were otherwise overshadowed.
One aim was to extend activities between trusted friends to strangers. Friends have long crashed on each other’s couches, but the Couchsurfing site wanted it to happen among strangers. Freecycle allowed you to give gifts to people you didn’t know, while Streetbank let you lend items to strangers in your neighborhood. These platforms encouraged sharing between people who might otherwise be isolated from each other.
All of this was built using the infrastructure of the internet. The ubiquity of interconnected computers and smartphones in the hands of ordinary people allowed them to cheaply advertise their locations and showcase offers. To catalyze a digital platform, all someone needed to do was set up a website as a central hub for aggregating and displaying offers for others to accept. It makes sense to centralize similar information, rather than having it scattered in fragmented locations. This, in turn, builds network effects, meaning that the platform becomes more useful — and thus more valuable — as more people use it.
Attempting to introduce sharing principles into networks of strangers isn’t easy. Our lives are built around large-scale market economies, and many people have internalized the principles of monetary exchange. In the context of huge global supply chains, the rural idyll of community production is long gone, and attempts to reverse-engineer authentic sharing relationships between people we don’t know can feel stilted.
While we might be willing to let a friend borrow our car for the day, we generally don’t trust strangers enough to share our most crucial possessions with them. We may, however, be game to share things that we don’t often use, like a basement that’s only half full or the backseat of a car that could have someone in it while we’re driving to work anyway.
We’ll probably be even more willing to offer this idle capacity to a stranger if there is some third-party assurance that they are legitimate, or will experience some consequences if they behave badly. Likewise, we may be more open to acceptinggifts from strangers if such assurances are in place. This is, in effect, why sharing economy platforms developed identity and reputation-scoring systems, adding layers of formality and quantification into non-monetary gifting.
Herein lies one source of corruption, as the very act of earning quantified reputation for gifting adds a feeling of market exchange. But it was building technology to identify and quantify spare capacity that really set the stage for undermining the sharing economy. “Why not get the stranger to pay for the gift as a service?” was a question that couldn’t be far off.
The move from sharing spare, underutilized assets to selling them can be subtle. In hitchhiker culture, a person offering lifts might reasonably expect a fuel money contribution from someone getting a ride — and if the hitchhiker leaves the car without offering it, the driver may be a little irritated. The money though, is never a condition, and until they explicitly say, “If you give me fuel money, I will drive you,” it’s not a commercial relationship. Note, though, how easily the phrase—once uttered—can become generalized into, “If you pay me, I will drive you.”
A new wave of “sharing economy” startups bet on just this concept, as their businesses came to be characterized not by sharing, but by showcasing spare capacity for rent, with the platform taking a cut as broker. So, too, began a hollowing out around the language of sharing. New entrepreneurs feebly hung onto the sharing story with the claim that market mechanisms could re-engineer the very community ties that the markets themselves had eroded. In reality, they were doing nothing more than marketizing things that previously hadn’t been on the market. If anything, this only undermined existing gift economies. A friend calls to ask if she can stay with you, but gets told, “Sorry, we have Airbnb guests this weekend!”
Ah, but there’s another twist. Far from merely facilitating the renting of spare capacity, these platforms grew to such a size that sellers of “normal” capacity started using them—as in, people running professional bed-and-breakfasts migrated to the Airbnb platform, and so on. The irresistible lock-in of network effects dragged the old market into the new, and voilà, the platform corporation emerged.
Let’s be unequivocal here: A platform corporation really only owns two things. It owns algorithms hosted on servers, and it owns network effects—or people’s dependence. While the old corporation had to get financing, invest in physical assets, hire workers to run those assets, and take on risk in the process, a corporation like Uber outsources its risk to independent workers who must self-finance the purchase of their cars, while also absorbing losses from their cars’ depreciation or the failure of their operations. This not only separates corporate managers from ground-level workers, it places the major burden of financing and risk on the workers.
This is a venture capitalist’s wet dream. Give a startup minimal capital to hire developers and run media campaigns, and then watch as the network effects ripple over the infrastructure of the internet. If it works, you’re suddenly in control of a corporation built with digital tools, but extracting value from real-world, physical assets like cars and buildings. The entity holds itself together not via employment contracts, but rather by self-employed workers’ dependence on it to access the market they rely on for their survival.
So, now here you are, staring at your Uber app with irritated sighs because the driver is two minutes late. This is a market transaction. To the driver, you’re just another customer. There is no sharing. You’re as isolated as you ever were.
We have a hard time seeing systems. We find it easier to see what’s tangible and in front of us. We see the app, and we see the driver’s car icon moving along the streets on their way to pick us up. What we can’t see is the deep web of power relations that underpins the system. Instead, we are encouraged to fixate on the flat and friendly interface, the shallow surface layer of immediate experience.
If you’re a driver, that interface doubles as your boss. It doesn’t shout at you like the jerk boss of old corporations. In fact, it shows no emotion at all. It’s the human-readable incarnation of a robotic algorithm that calculates the optimal profit-path for Uber, Inc. As a driver, you have no colleagues and no union. There’s no upward mobility. Uber wants you to leave as soon as you build any expectations of progress. You and thousands more eke out enough to survive, if you’re lucky. This all while the owners of the platform get richer and richer, no matter what.
Of course, if you want to put a positive spin on this kind of work, you can call it flexible, decentralized micro-entrepreneurship. But pan out, and it looks more like feudalism, with thousands of small subsistence farmers paying tribute to a baron that grants them access to land they don’t own.
So, what is to be done? For one, let’s first understand the problem. Innovation and change are pointless unless they’re coming from a real analysis of what’s gone wrong—especially when we’re being made to believe we’ve actually gained an asset. Only then can we rebalance the power.
If we are going to turn ourselves into a sprawling network of micro-entrepreneurs, micro-contracting via a feudalistic platform, let’s at least cooperatively own the platform. In doing this, we might even retain one definition of sharing — the common usage of a shared resource pool, like the farmers who collectively manage a reservoir.
This is the origin of the platform cooperativism movement, one possible counterforce to the rise of platform capitalism. In principle, it’s not that complicated. Spread the ownership of the common infrastructure among the users of that infrastructure, give them a say in how it’s run and a cut of the profits that emerge from it.
The platform cooperativism movement is a new one, with many of its proposals still on paper and yet to be released into the wild. Many have seen the potential to use blockchain technology, whose original promise was to provide a means for strangers to collectively run a platform that keeps track of their situation relative to each other without relying upon a central party. Some, like the blockchain-based ride-sharing platform La’Zooz, have already released apps and are iterating away in the background. Others, like the blockchain-based proposal for an Uber-killer called Commune, are still in their conceptual stages. Arcade City, another attempt at an Uber alternative, has been dogged with controversy—and a split in the team has led to the creation of Swarm City.
Meanwhile, big corporates have increasingly encroached on blockchain technology with an eye toward using a pacified version of it within closed and controlled settings. There are, of course, plenty of talented and idealistic blockchain developers looking for opportunities beyond corporate life.
Either way, fancy technology isn’t a magical recipe. The equally important work involves building a community willing to back new platforms. A Dutch proposal for an Airbnb alternative called FairBnB is making a start as a Meetup group, and food couriers are organizing gatherings to discuss how they can set up cooperative alternatives to Deliveroo.
In the face of massive commercial platforms, aggressively backed by venture capital money, these initial attempts might seem idealistic. But as digital serfdom only expands, we have little choice but to start small with underdog pilot projects that galvanize action.
It’s a new mentality that needs building. In a world where we’re told to be grateful receivers of products and the opportunity to work on them from heroic, demigod CEOs allegedly “democratizing” the workscape, we need to see straighter and expect more. The entrepreneur is still nothing without the underlying people who make their enterprise work; and in this case, their wealth comes directly from skimming money off vast collectives. Let’s fuse the two forces into one, and build collectives with actual sharing in mind.
About the Author
This submitted article was written by Brett Scott, Author of The Heretic’s Guide to Global Finance: Hacking the Future of Money. Brett Scott explores urban ecology, economic anthropology, P2P tech & alternative currency.
The short answer is 64% of companies use AI to be the next big industry disrupter — like Uber, Amazon, and AirBnb. Meanwhile, 36% want to prevent their disruption. Believe it or not, the main mission of companies using AI is not to spy on you, create the next Terminator or automate you out of a job. That is not to say they do not do some of these things in pursuit of their goal. But it is not their main intent. Instead what these companies are doing is focusing their AI efforts on areas of strategic advantage. If snatching the best candidates away from competitors is their biggest advantage, then they focus AI on recruiting top talent. If it is connecting people with stuff they need, they focus on creating AI-based digital platforms.
Can some of these strategic advantages entail spying on you? Yep. They call it “personalization” and it is used to make life easier. Is it 100% ethical in the way it gets executed? Ha! That is a question I will discuss plenty in future posts. Might they deploy drones? Yep. But guess what? Drones are not just for military applications. Drones can also be paired with visual recognition to assess hail damage on roofs, and downed power lines in flood areas where it is not safe for people. While military groups might love a Terminator, it would still take better robotics, several data centers, and Artificial General Intelligence.
AI Frames of Reference
There are thousands of ways to use AI in business. It can be overwhelming to consider them all. To help you, I offer the frames of reference I used when standardizing AI services. They are 1) department, 2) industry and 3) developmental commonalities.
By Department
In IBM’s Accelerating Enterprise Reinvention” CEOs were surveyed to find out where they intended to focus their AI efforts. The top five areas included: 1) Information Technology (IT), 2) Sales, 3) Information Security, 4) Innovation and 5) Supply Chain.
1)Information Technology priorities included management of finances, procurement, and vendors as well as operations, IT architecture and engineering. Information technology can be a huge advantage when done well inside companies or it can be catastrophic when poorly executed. Many business leaders feel IT spends too much and takes too long getting important business projects done. It is no surprise that IT would turn to automation, machine learning, and chatbot-staffed help desks to reduce costs and manage out-of-control numbers of vendors. If you think about how technology solutions are bought within the company, it is often best-in-class, fastest, coolest, easiest or relationship-driven for the department that has the dollars. This amounts to a nightmare for IT vendor management and IT architecture — not to mention the engineering resources required to cobble all the siloed systems together and ensure everything can run properly without one errant bit of code bringing down operations. Just think about how reliant we all are on our IT systems.
2)Sales prioritized AI to connect dots between customer information and customer desires to supply chain operations such as raw materials procurement, logistics, and fulfillment. If you can just imagine, a tennis shoemaker wants to respond to a special marathon that, at the last minute, became an overnight sensation on social media. They have only days to respond to fans with a shoe that could commemorate this super-exciting event. They plan to emboss the logo of the event on the shoe, and thereby immortalize the brand with their customers for reacting so fast to demand.
3) Information Security priorities are to prevent, detect and respond to security breaches. Fraud, hacking, theft of identities and goods are the longest-running applications of machine learning algorithms. Exponentially enhancing previous AI capabilities are always-on location data (via smartphones), social media feeds, individual voice detection, and facial recognition pouring in from the continual posting and tagging of faces in online videos and photos.
4) Innovation is the most aspirational of all uses for AI. It can serve as the platform that enables employees to develop new ideas, source patents against those ideas, and uncover the right entrepreneurs to bring ideas to life. At IBM there was a company-wide competition that centered around an AI innovation platform. The platform came with tools to help employees think outside of the box and then check their ideas to see if it had been done before. Employees were able to leverage the tool kit to help them put the first straw man ideas to paper. More than half of the company participated because it was fun and because it was based on new tech that everyone wanted to learn. The applications they came up with in that 3-month period ranged from anti-bullying apps to systems that could help people maintain a healthy weight through the use of food pictures that auto-converted to food log entries. It is pretty amazing what companies can do when they have their entire workforce focused on innovating new solutions. Not to mention the excitement and energy the competition created that fostered the culture of innovation well beyond the competition period itself.
5) Supply chain AI programs are expected to deliver goods when they are wanted and how they are wanted. Supply chain operations can be the hardest thing to do in companies because there is so much uncertainty about what can impact the raw goods development, the distribution of raw goods and produced goods, and hence the price and constitution of the goods. Of course, you have to know where the goods are and if there are any changes in the midst of everything. Having enough of something versus not having enough, ensuring you have that something right when it’s wanted despite snow, rain, hail, tornadoes, unexpected highway shutdowns — these are ever-changing situations that supply chain leaders have to deal with and still get the products out, still buy the things employees need, and keep the company running. In consideration of these, the priorities for most supply chain leaders include using AI for demand planning and forecasting, risk and security management, and asset management.
By Developmental Commonalities
1) Personalization. Anything that is customized to individuals involves personalization algorithms — from ads to jobs to diet and fitness recommendations. The theory is that if I can predict and offer you the exact products and services you want, when you want them, how you want them, then you will buy more volume, more frequently, for longer (loyalty) into the future. Essentially, I will have formed a relationship with you by demonstrating that I know you and can predict your needs. This type of AI relies on social media data, location data from mobile phones (turn off location services and weather apps in your mobile phone if you do not want this tracked), web cookies, and direct digital or in-person interactions. Personalization is the AI use case that can often cross the creepy-factor line. This is where an individual’s data privacy gets traded in for coupons, convenience, and connections. The benefits of providing personalization capabilities need to be weighed heavily versus the brand risks, liabilities, and potential regulatory (e.g. GDPR) violations.
2) Preserving expertise, augmenting knowledge. Shortages of talent in the hiring pipeline and impending mass retirements of the most experienced personnel are the top reasons for the adoption of AI expert systems. These situations have been playing out at numerous firms as top financial advisors, actuaries and seasoned oil rig engineers are coming of retirement age. Company leaders need to quickly preserve the knowledge of these experienced workers to ensure they do not lose revenues during transition periods to new people or full AI automation of those jobs. These types of AI projects entail rigorous training of systems by numerous and varied experts over long periods of time. They need as many scenarios as possible to do their job correctly. Any scenario that the AI has not encountered before could be disastrous because the AI system will not know what to do. Because data scientists and experts cannot account for every scenario the AI may encounter, it is imperative that human experts oversee the AI decisions especially when involved in high-impact situations such as life, death, firings, sentencings, and driving.
3) Answering questions, giving support. Customers need answers and they want to call in or conduct a quick chat via mobile phone, tablet, or computer whenever they feel like it. AI is good at reading tons of text. It could be given volumes of digitized warranty manuals on automobiles or insurance claims policies or frequently asked questions and customers could ask it questions all day and night. It never gets tired. What’s even better is that each customer interaction — including the type of question and its wording, the AI answer, and the immediate customer reaction/ feedback, help the machine learning algorithm get better over time. This question-and-answer capability is where chatbots have their roots and they are fairly straightforward to set up.
4)Compare and comply. These AI systems are similar to ledger systems. If you have one thing the AI must read through on one side, then on the other you have some sort of equivalent action that brings about. An example of this would be when there are thousands of invoices coming in with payments due in 30–90 days and not enough people to read through them all in that time. The invoices have paragraphs of text explaining the work that was done and the rate charged. On the other side of the equation, you have negotiated amounts that you will pay for specific services. If you are overcharged for a service, then you will refute your bill and ensure payment does not go through. These types of AI projects use some form of Optical Character Read to digitize invoices, content discovery, and management tools, algorithms that compare invoice text to contractual agreements, automation to interact with payment systems and human experts to oversee the decisions to refuse payment. Compare and comply types of AI are used for all kinds of billing, procurement, warranty, and regulatory reasons. If it has a contract, agreement, laws, or guidelines, and incoming claims, invoices, or violations, then it’s a good candidate for compare-and-comply style AI.
By Industry
Industry-specific uses for AI are as numerous as the new products or services that businesses can create and the regulations that govern them. In the utilities industry, for example, a start-up uses visual recognition programs in drones to identify downed power lines in places that energy companies could not physically get to because of snow and flood waters. An industry-specific regulatory example includes using AI for the banking industry’s Anti-Money Laundering compliance.
The goal of this article was to help simplify ways to think about the overwhelming number of AI initiatives that are announced daily. I hope it helped you.
About the Author
This article was written by Cortnie Abercrombie of A.I. Truth. See more.
The legal sector has operated in the same way for decades and technological advancements that have become central to the development of other industries have now left it falling behind its competitors.
The sector is not unaware of the potential for advancements like artificial intelligence and blockchain to increase the efficiency and accuracy of internal systems, but the enthusiasm tends to fade when it comes to practical implementation.
With technological innovation having now reached a stage where it can provide a legitimate vision for how the sector can be modernised to improve the delivery of services, it is important for law firms and corporations alike to stop ignoring the potential benefits.
The arrival of advanced security means law firms can now concentrate their efforts to innovate with more confidence. One crucial reason why they haven’t to date is that trust is imperative in the justice system and weak security presents too much of a risk. Most will still remember the first years of the Internet, which were plagued by viruses and fraud because companies persevered without properly considering the associated risks.
Many now believe blockchain can be a game-changer, working to increase the transparency of cases, whilst improving the delivery of services through the secure use of digital evidence software. Blockchain can be used to collect data on every transaction that occurs in the storage of an item, providing a full audit trail combined with a secure cloud-based platform for sharing sensitive data.
This means that once a piece of evidence has been entered into the online system it cannot be altered or falsified. This is down to a unique combination of cryptography which renders the data immutable, and then its openness, which is how it is distributed among a peer-to-peer set of participants. Crucially, whist blockchain is a public artefact, inspection of blockchain wouldn’t reveal evidence, only IDs and hash codes, creating an incorruptible digital ledger.
As a result, it’s possible that going forward there will be no doubt whether the evidence presented in a court room is the same as the original file that was initially uploaded into the digital evidence software system, removing questions of legitimacy from the trial process. This eliminates any possibility that evidential material submitted to court can be repudiated as it is not possible to photoshop a picture or splice a video.
Similarly, courts are now recognising the possibilities of digital evidence management systems, which have the potential to lead to the elimination of paper in the trial environment. A key focus has fallen on paper as it becomes increasingly burdensome. While the initial purchase price of a piece of paper is well below 1p, once other factors are taken into consideration such as storage and transport, it can end up costing as much as 25p per page. When court room bundles regularly contain thousands of pages, it’s no wonder digitising the process of bundling becomes a priority.
When a case is taken to court numerous bundles have to be produced and individually amended throughout the trial which is an expensive and time-consuming process. Switching to e-bundles not only addresses environmental concerns, but also reduces this significant expense and makes the process of amending bundles and keeping them up to date far easier. By referring to a centralised bundle, all parties in court can rest assured that they are referring to the same page in a trial, with the most up to date evidence — a process that is not always possible when juggling ten different lever arch files.
As with all sectors when a period of change has dawned, many are reluctant to alter the established system and become hesitant to innovate. However, the legal system is under pressure from budget cuts and increased competition between firms. A digital drive can transform the shape of law firms and how they operate, increasing the efficiency and accuracy of proceedings, enabling them to remain competitive and match growing demand.
Establishing a hybrid workforce and augmenting legal experts with automated processes means that firms will be able to use technology to free up lawyers from tedious tasks. It is this automation of the more mundane, and time-intensive tasks, that will accelerate processes and reclaim a firm’s resources. This not only increases efficiency in law offices, but also in court — using a digital system allows for quicker access to evidence, remote working and no fuss over bundles, cutting down trial time and freeing up court room space in an overstretched system.
Through adopting a unified user-friendly system that meets the needs and requirements of all parties involved, legal proceedings and trial processes will become more efficient, accurate and reliable, positioning the UK’s justice system as one of the most advanced in the world.
Keynes once remarked that the “difficulty lies not so much in developing new ideas, as in escaping from old ones”. For the legal sector, overcoming that difficulty will be its next test.
About the Author
This submitted article was written by Alice Bonasio, technology writer for FastCo, Quartz, The Next Web, Ars Technica, Wired.
Ok, I have a confession. Last Thursday, I became yet another Pokémon Go-related statistic when I fell into a hole while walking around my neighborhood.
I began playing the new GPS-enabled, augmented reality game, Pokémon Go the minute I learned that I could realize my childhood dream of hunting Pokémon IRL (in real life, for those not familiar with the acronym). I was hooked. So hooked, that while glued to my cell phone screen and searching for an elusive Bulbasaur, I fell directly into a not-so-elusive Houston pothole. Don’t worry, I caught the Bulbasaur (and I’m OK).
However, during my weekend search for Pikachu, Snorlax, and the other 248 Pokémon, it dawned on me how right the developers of the game got it when it came to building a technology that motivates and inspires users to get hooked and stay that way, even when the searching process gets more difficult. I think about this — how to motivate people and keep them motivated — often, although typically within the context of subject area that most fourth graders (and, lets be real, most thirty-year-olds) find less exciting than the hunt for mythical, magical beings: education.
Here are five lessons education technology could learn from Pokémon Go:
1. Pokémon Go’s augmented reality is cool, brings relevance to the experience, and encourages users to explore and engage with their environment in novel ways.
One of the novel features of Pokémon Go is its ability to meld the fantasy world of Pokémon into the user’s every day environment. In order to find Pokémon, users are required to travel around their community to different locations. Once at these locations, the game uses cell phone cameras to project a Pokémon into these real-world environments.
The use of augmented reality makes the game feel more exciting by integrating itself and making itself relevant to the user’s particular community. Of course, Pokémon fans could simply break out the old Gameboy and catch Pokémon within the privacy of their home, but doing so is nowhere near as fun as going out into their communities to explore. Augmented reality in Pokémon Go connects the game to the real world and encourages users to go out and explore places they might have never gone.
In education, the ideas and topics covered in a textbook or a lecture often fail to make the leap from abstract concept to real-world application. Drawing upon the use of augmented reality in Pokémon Go, education technologies could devise ways to authentically engage users in real-world situations. For example, they could encourage the examination of biological or physical processes as they occur in nature.
2. Pokémon Go engages players at their Optimal Level of Difficulty.
When beginning the game, a novice player encounters challenges that are “just right” for their skill level, while more experienced players receive more difficult challenges. For example, a new player will go out and encounter an easy-to-capture Pokémon, like a Pidgey. After leveling up, however, Pokémon become more difficult to capture. These Pokémon require, for example, that the player “release” or “throw” a Poké Ball (a device used for catching wild Pokémon) at just the right time, with an ever-so-perfect a spin, or you have no chance of catching an elusive Wigglytuff.
The game is quite good at finding the Goldilocks zone of being neither too hard, nor too easy. This ability is important, especially when it’s related to how students approach educational assessments. If a question or challenge is too hard, students get discouraged and quit; if it is too easy, students get bored and disengage.
3. Gradual exposure to new elements and new ideas keeps Pokémon Go interesting and doesn’t overwhelm the novice.
Just like educational technologies, Pokémon Go is a very complex system. Users can catch Pokémon, explore various real-world landmarks designated as Poké Stops, put spin on Poké Balls to increase the likelihood that they will successfully catch their target, and battle at gyms (physical places where players can virtually engage in sparring matches or battles with other Pokémon enthusiasts). However, unlike much tech used in the classroom, Pokémon Go reveals these features to users gradually. In some cases, such as battling at a gym, a player is barred from entry until they reach a level 5. In Pokémon Go, not only does gradual exposure to more advanced features prevent the need for training, it also keeps the game interesting. Players have something to work towards, capabilities to unlock.
In edtech, faculty members are often thrown into the technological deep end. They are presented with myriad options and toggles — they get all the features of a level 20 Poké Master, despite the fact that they’ve yet to learn how to snag a baby Rattata. I’m not saying that it would be wise to make faculty who are accustomed to choosing edtech products based on their feature richness wait to have access to, say, an advanced analytics dashboard. But I do think the edtech industry needs to abandon the feature arms race in favor of user-friendly technologies and tools that make it easy for teachers, students, and administrators to get what they need, when they need it.
4. Growth mindset is implemented throughout the game, which encourages users to improve their performance by strengthening their Pokémon and increasing their avatar’s experience points.
Growth Mindset is probably one of the hottest buzzwords in education right now. The idea simplified is that individuals fall into two different mindset categories:
1) A growth mindset, where an individual believes that they can improve their performance with strategic practice and hard work, and
2) A fixed mindset, where an individual believes that their skills are based on innate talents and gifts cannot be changed.
Throughout Pokémon Go, developers have encouraged a growth mindset. Players see their experience and skill level increase with each Pokémon they capture or each battle they win and say, “If I just keep trying and practicing, I can get stronger.”
Unfortunately, the same often does not hold true in the world of education. Students typically believe that they fall into a specific, unchangeable camp, such as the “I’m just not good at math” camp. High-stakes testing and assessments, as well as many education technologies can discouraging users and make them less likely to try to increase their understanding in their problem subject areas. Assessments have become synonymous with something that is bad, even though a ton of research shows that “long-term memory is increased when some of a student’s learning period is devoted to retrieving to-be-remembered information through testing or assessments.”(Wikipedia) Education technologies must figure out how to encourage users to view assessments and quizzes as a good things, then use this technique to encourage students to level up as they would when battling in Pokémon Go.
5. Pokémon Go uses technology to create Community IRL.
Yesterday evening, I was out playing Pokémon Go with friends. We were walking around our neighborhood, searching for rare creatures, when we stumbled upon another group of similarly aged individuals, their heads buried in their iPhones, their fingers making the distinctive flicking motions associated with trying to capture a Pokémon. We inquired about each other’s catch and immediately bonded over our mutual pursuit of prized Pokémon. The other team shared with us that “The really good Pokémon hang out at the Zoo,” and “If you do this, you are more likely to catch that Psyduck.” Since then, my Facebook feed has been filled with invites to Pokémon hunting themed meet-ups. A new community has grown up around this game practically overnight.
Pokémon players at the University of St. Thomas in Houston. Credit: Daniel Williamson
In a time when technology is often thought to discourage participation in real life, Pokémon Go has done something unique; it has brought people together and formed communities. With education technologies, more often than not the learner and, in many cases, the educator, are isolated. The technology creates a barrier between them and can prevent communities from spontaneously forming. New models of educational technology must figure out how to break down these barriers, create communities, and use technology to bridge the gap between our virtual worlds and our real lives.
I’m not sure if Pokémon Go is a passing fad or something that is here to stay. However, whether you are a technologist working on the next great learning app or a teacher trying to figure out how to engage your students, Pokémon Go offers a powerful model that has quickly changed the way users behave and engage with digital content.
Just one warning, don’t forget to watch out for the potholes.
About the Author
This article was written by Daniel Williamson, managing director at OpenStax. Daniel is passionate about education equity and has dedicated the past ten years of his life to developing and leading education startups from idea to implementation. see more.
Cities are rapidly expanding in size, wealth and power, with some now larger than nation states. Smart city solutions and strong global urban networks are developing to manage massive urban growth. However, cities exist within a wider system and it may take more than technological advances, innovation and city autonomy to develop a sustainable urban future.
As the world moves towards a global population of nearly 10 billion by 2050, most of which will be urban, the pressures on our cities already facing challenges of urban health, climate change, social justice and urban governance, are likely to increase. The challenge is almost overwhelming and will require innovative policy solutions way beyond ‘business as usual’1.
A range of urban policy solutions have recently been pursued, including national urban strategies for better cities, city deals between national and local governments and most recently the concept of smart cities—‘the effective integration of physical, digital and human systems in the built environment to deliver a sustainable, prosperous and inclusive future for its citizens’2. Furthermore, the involvement of national governments in urban policy varies significantly between countries3, as does the consistency between city and national government agendas, at times at odds, for example, over environmental goals.
The future development of mega urban regions, a number of which are already larger than some nation states, will wield significant wealth and power, and with that more responsibility to develop and implement their own futures (e.g., Hong Kong-Shenhzen-Guangzhou, Nagoya-Osaka-Kyoto-Kobe, Rio de Janeiro-São Paulo)4. The necessary investment in infrastructure alone to cater for urban growth will be enormous, estimated to grow globally from $4 trillion per year in 2012 to more than $9 trillion per year by 2025 (ref. 5).
As cities grow they will individually achieve greater wealth and power in decision-making and influence with their reach extended further as they coalescence into global networks. The C40 global cities network (http://www.c40.org/) is an example where capital cities are collaborating on major challenges and sharing knowledge and experience on critical issues such as renewable energy, urban health and well-being. Singapore is a good example of where benefits have been reaped as a result of a clear vision to become more sustainable (economically, environmentally and socially) through innovative urban design and development based on sustainable principles. For example, Singapore has managed to increase its urban density and ‘greenery’ at the same time, and stands as a leading example to other growing cities (ref. 6 and see pages 133–134 in ref. 7). Yet for all their wealth and power, cities remain dependent in the wider context. Singapore, for example, is highly reliant on Malaysia for water resources.
Smart cities
The concept of ‘autonomous cities’ is not new in the sense that informal settlements have existed for millennia, and many independent indigenous communities live in remote locations around the world. However, the concept is remerging as cities increasingly set their own agendas. An example is the United States where, in contrast to national policy, sub-national governments with a wide range of non-government and private sector partners have created an alliance of action on climate change: We Are Still In8. Action taken includes green infrastructure, smart climate design and the building of more resilient neighbourhoods for the impacts of climate change (heat, sea level rise and extreme events).
Many of the above urban challenges were at the centre of discussions at the recent World Urban Forum (WUF)—held every 2 years and hosted by UN Habitat to advance implementation of the recently adopted UN New Urban Agenda (Quito 2016). WUF 9 displayed the groundswell of urban innovation occurring across the globe and showcased the embrace of new technology including localised renewable energy, smart and green infrastructure, integrated public transit and e-democracy designed to improve community input to local city plans. There is no doubt that the urban data ecosystem is expanding with digital mapping, smart asset management and urban mobility and that these developments have the capacity to rapidly improve urban management9.
A key driver for smarter cities is planning for the impacts of climate change and the expected increase in urban heat island effects and extreme events (droughts, floods and coastal storms). In this context, the policy of smart cities has the potential to make a major contribution. The inaugural IPCC conference on Cities and Climate Change came to a similar conclusion, as stated by Debra Roberts, Co-Chair of IPCC Working Group II ‘Business-as-usual will not save the world. This conference disrupted the traditional story of the world’s cities to show how science can partner with policy and practice to transform the world’s cities into climate-smart, equitable and sustainable homes for all’10.
Beyond smart cities
The concurrent global trends of urbanisation and climate change will require very smart and innovative solutions. However, it will take a lot more than a smart cities agenda to provide a more sustainable urban future. As the WUF 9 declaration concluded, it will require collaboration between all levels of government and partnerships to tackle the scale of the change ahead11. As cities grow, so does their consumption of natural resources and their dependency on Earth’s natural systems to thrive and prosper. Red alert days for extreme urban pollution in mega cities, recently in Beijing and Delhi signal a complex relationship between mega cities and regions12. For example, while urban pollution can be urban generated, it can also be the product of fires from logging in the region to provide resources for consumption by the growing city (e.g., soybean production in the Amazon, palm oil in south-east Asia and smallholder farming in central Africa) (see page 28 in ref. 7).
In the lead up to the inaugural IPCC Conference on Cities, six research priorities were identified for cities and climate change including the need for better urban data and a global network of ‘urban observatories’13. I could add an important seventh research priority being ‘urban governance’ including a better understanding of the multiple ways the developing science of cities can be incorporated into the planning, design and management of cities to activate the systems approach to the urban future. An example of such collaboration has been the series of reports and advice by the New York Panel on Climate Change bringing together the best scientific and planning expertise from the national to the local level on coastal flood risk, rising temperatures and rainfall to better prepare New York City for the impacts of climate change14.
A sustainable urban agenda
Our urban future within the twenty-first century and beyond will inevitably impact the role and function of cities. As cities grow they will become stronger and more independent and autonomous economically as a result of their wealth. However, cities will remain, in my view, part of a complex set of environmental and social systems and as a result will continue to be influenced by the actions of higher levels of government. While cities can undertake significant local action on urban sustainability such as the 100% city renewable energy alliance15, they will continue to rely on national governments for investment in critical infrastructure including defence, energy, water supply, communications and rapid transit.
Where national strategies are aligned to local action, a great deal more can be achieved, as demonstrated by the removal of the major inner freeway in Seoul to restore life to the river Cheonggyecheon, which has resulted in environmental and community benefits and created one of Korea’s most popular tourist destinations, bringing national economic return16.
While cities grow economically more powerful, collaboration and partnerships will remain central to achieving green urban growth and the transformation required for a more sustainable future. So too will cooperation between nation states increase as urban city-regions spread across national borders.
Based in part on a recent series of interviews of urban leaders throughout the world, I put forward seven sustainable pathways for cities and regions in the future—(i) Planning within planetary boundaries, (ii) Long-term vision with targets, (iii) Adaptive integrated planning, (iv) National sustainable development strategies, (v) Net zero carbon precincts, (vi) Innovative platforms for collaboration and evaluation and (vii) Green growth, i.e., planning. Importantly, all seven steps require effective coordination at all levels of government for successful implementation (see page 149 in ref. 7). Again, the key message emerging from the interviews was the importance of a shared vision by all levels of government for our cities with clear targets for sustainability over both the immediate and longer term.
In conclusion, cities will become economically stronger and nation states will need to develop a more mature partnership with cities as they become integral to national performance, the health and well-being of citizens and global environmental outcomes. The performance of mega urban regions in relation to action on climate change and the environment more broadly (water, energy, air and land) will be critical to meeting the Paris Agreement targets and the United Nations Sustainable Development Goals. However, cities are an urban system within wider systems including Earth systems (water, energy, carbon, biodiversity and so on), social migration, global capital and more. In my view, we are in fact more connected than ever before. While cities will increasingly do many things very well at the urban level and through city networks, collectively we will develop a much more sustainable urban future when we are working together from the local to the global scale.
About the Author
This article was written by Barbara Norman, the Foundation Chair of Urban and Regional Planning and Director of Canberra Urban and Regional Futures (CURF) at the University of Canberra. Professor Norman is Chair of the ACT Climate Change Council and a Visiting Fellow at the Australian National University.
“Music is about human beings communicating with other human beings,” said Andrew Dubber, professor of music industry innovation at Birmingham City University and director at Music Tech Fest.
Live music has existed for as long as humans have been communicating—that is, since the dawn of man. Here’s a quick history.
100,000 years ago: First prehistoric performances. Humans “perform” by mimicking sounds in nature, meteorological phenomena, or animal calls.
40,000 years ago: The first musical instrument is made of animal bones. Theearliest-known flutes are thought to have been used for “recreation or religious purposes,” experts say.
8th century B.C.–6th century A.D.: Ancient musical performances. Inancient Greek and Roman societies music performance becomes ubiquitous at marriages, funerals, other religious ceremonies, and within theatre. Persianand Indian classical music is used in comparable fashion.
Middle Ages: Churches become the main music venues in the Western world. Pipe organs are installed in big cathedrals with natural acoustics, adding a spiritual and imposing character to the music.
Baroque Era: Multiple-sized music venues. Composers such as Bach do a lot of their playing in churches smaller than a Gothic cathedral. In the late 1700s, Mozart performs his compositions at events in grand, but not gigantic, rooms. People now dance to the music, too.
1700s: Opera emerges as a new form of entertainment. Big music halls, such as the still very popular La Scala (1778) in Milan, are constructed. Musical ensembles — by then called orchestras — grow gradually throughout the 18th and 19th centuries.
Early 1900s: Jazz develops alongside orchestral music. Originally played and danced to in smoky bars and public houses, jazz paves the way for the modern concert as we now understand it.
1910s — The PA is born. Magnavox’s Edwin Jensen and Peter Pridham begin experimenting with sound reproduction using a carbon microphone; soon afterward they file the first patent for a moving-coil loudspeaker. With their “Sound Magnifying Phonograph,” the modern public address system (PA) is born — a device we still use today at nearly every live concert.
Early vacuum tube public address system. Image credit: Public Domain
Early 1930s: The first electric amplifiers for single instruments appear. The introduction of electrolytic capacitors and rectifier tubes make it possible to produce economic, built-in power supplies that can plug into a wall socket.
1931: The “Frying Pan” guitar goes electric. Built by George Beauchamp and Adolph Rickenbacker of Electro String (later Rickenbacker), the amplified lap steel Hawaiian guitar becomes the first electric-stringed instrument. Legendary models by Leo Fender and Gibson’s Les Paul follow suit.
1941: Rickenbacker sells the first line of guitar combo amplifiers. Although rather tame by today’s standards, the amplifiers are capable of making big, unprecedented noise, and became hugely popular and influential.
1950s: Rock ’n’ Roll is born. Several groups in the United States experiment with new musical forms by fusing country, blues, and swing to produce the earliest examples of what becomes known as “rock and roll.” The rock concert grows into an entertainment standard around the world.
Woodstock Festival opening ceremony. Image credit: Public Domain
1960s: The modern concert format emerges. American promoter Bill Graham develops the format for pop music concerts. He introduces advance ticketing (and later online tickets), modern security measures, and hygiene standards.
1960s–1970s: Live music exerts a major influence on popular culture. Large-scale amplification facilitates the expansion of massive music festivals — the prime example being 1969’s Woodstock Festival, attended by over 400,000 people.
1970s: Pink Floyd pioneers concert visuals and special effects. The British rockers incorporate huge screens, strobe lights, pyrotechnics (exploding flash pots and fireworks), and special effects (from helium balloons to a massive artificial wall). The band also uses quadrophonic speaker systems and synthesizers.
Since the 1970s, the basic format and technology behind the rock concert have remained unchanged. Sure, sound systems have gotten louder, and light shows now incorporate 3D projections and massive holograms — with DJ Eric Prydz’s hologram at his 2014 Madison Square Garden concert the largest to date — but no matter the genre, the setup is the same.
So, will the concert of the future look any different at all? At the rate technology’s changing—yes. Since the 1980s, the MIDI protocol has allowed for triggering computer sounds via MIDI controllers that resemble traditional keyboards, guitars, mixers, etc.
Laura Kriefman creates music by dancing at Music Tech Fest
Today, musicians can perform in completely new ways, further unbound by the constraints of conventional instruments. Professor Dubber points us to the direction of British sound designer Ross Flight who uses Microsoft’s Kinectmotion sensing device to perform live by moving in space. Choreographer Laura Kriefman creates music by dancing; producer Tim Exile invented his own electronic instruments so as to perform “exactly how he wanted”; and beatboxer Beardyman built a real-time music production machine that “doesn’t have any onboard sounds” to allow him to create loops and layers from just the sounds he makes with his voice.
Beardyman’s machine creates loops and layers from just the sounds of his voice.
Interactivity
Interaction between fans and performers is becoming more intricate — as is interaction between members of the audience themselves.
Social media is an obvious facilitator here. Snapchat’s “Our Story” feature for live events creates a compilation of shared “snaps” within certain physical spaces, uniting people while broadcasting their collective experiences to the rest of the world.
U.S. event technologies companyCantora is developing an as-yet-unnamed software that “takes the ebbs and flows” of an audience to alter the physical venue, while London technology company XOX has developed a wristbandthat claims to track audience emotions by “evaluating the electrical characteristics of a wearer’s skin in real time and processes this to identify changes.”
Beyond social and software, innovation is happening at the artist level as well. British songwriter and composer Imogen Heap has relied on technology extensively to interact and collaborate with her fans. In 2009, she used Vokle, an online auditorium to hold open cello auditions for her North American tour. Later, she opened the virtual auditions to singers and choirs, inviting them to submit videos on YouTube. The winners ended up with her on stage.
Heap is also a pioneer in the use of wearable technology in live music performance. She has been working for years toward a more flexible live setup that would enable her mobility while performing multiple musical production tasks on the fly.
In 2011, she unveiled a pair of high-tech gloves that allow her to amplify, record, and loop acoustic instruments and her voice; play virtual instruments; and manipulate these sounds live.
The MiMu gloves combine ground-breaking technology with sensors and microphones. Heap used them to record and later perform live “Me the Machine,” the first song she created solely with the gloves.
“The thing that has irritated me over the years is the lack of flexibility in music remote controllers,” Heap told me in an interview. “Technologically, I’ve been trying to free up the performer and enable them to control every kind of nuance and detail (e.g., bending a note or moving through different octaves very quickly with your hand), like playing through stars or playing a baseline like a basketball instead of having to be very rigid.”
Pianos and guitars are amazing instruments but their use is predetermined. “Often when you’re producing, especially electronic music, you’re using sounds which don’t have a body or a physical presence. It’s about bringing those digital sounds to life on the move, wirelessly, on the stage,” she said.
Alongside performance tools, wearable technology has infiltrated the audience experience, too. Here’s a few examples:
Smart earbuds, like those developed by San Francisco’s Doppler Labs, let users customize live music via a smartphone app that enables them to adjust the volume, EQ, and apply effects to the sounds of the environment around them.
Nada, a wearable smart ticket combining a cashless payment method and paperless tickets, captures real-world attendee behavior at large scale.
The Basslet,a wristband developed in Germany, lets listeners take the live gig experience anywhere by connecting their music players and making them “feel the bass and depth of music through their body.”
Paperless tickets are slowly replacing traditional ones.Una, a British startup, is on a mission to eradicate scalping by providing users with a plastic membership card with embedded chips to be scanned at venues. The card works in conjunction with an online account and can also be used for cashless payments.
The first cash-free festivals held in the United States (Mysteryland), Canada (Digital Dreams), and the Czech Republic (Majales) took place in 2014 with the help of smart-ticketing pioneer Intellitix.
Meanwhile, a subscription-based service has been making waves in the ticketing world. Jukely is essentially a Spotify for live gigs — you can pay $25 per month and attend as many concerts as you like from the roster the company offers.
Live streaming
A now-broken boundary, it was once a requirement that you be physically present at a concert to experience it. Live streaming has since enabled people from all over the world to gather into a single online “room.”
Artists like U2 have used applications like Meerkat and Periscope to broadcast their concerts to fans at home, and the activity has only grown in popularity since Coachella Festival streamed on YouTube in 2011.
The idea of a concert in virtual reality isn’t new. Several South Korean record labels introduced V-concerts as early as 1998 — with varying rates of success.
More recently, Spotify CEO Daniel Ek said that the company plans to roll out a virtual reality concert system that will enable fans to enjoy live concerts from the comfort of their living rooms. Ek hinted at virtual merchandise booths linked to the users’ Apple Pay accounts and joked that a virtual bouncer may also be included.
Artist payments and Mycelia
With profit margins for newer artists and concert promoters notoriously low, and many festivals shuttering their doors over the past few years, the live concert market is an unpredictable one.
The challenge of creating a sustainable and equitable artist remuneration system for the gig of the future is crucial.
Here, Imogen Heap might have the answer again. In October, she released her single “Tiny Human” on Mycelia. Mycelia is a revolutionary transaction system using the Blockchain — the architecture that underpins the popular electronic currency Bitcoin — that has the potential to transform how live musicians are paid.
Mycelia brings together artists, developers, and coders in a formal movement to shape the direction of the music industry in favor of the artist.
“With live for instance, if somebody covered one of my songs during a big show, sometimes it happens that that song doesn’t get registered properly and as a result [I] wouldn’t receive any royalties from the song’s performance,” said Heap.
“But there are devices — little gizmos sitting in a corner of the club — that find algorithmically who the songwriter of any performed song is and help calculate the amount of payment due — according to venue and audience size,” she explained.
“At the moment, any transaction would go through different collecting societies. In the future, it would just come directly to me as a songwriter. It’s becoming possible to break down the barriers of where institutions have previously got in the way of the flow of information and the flow of creativity. I’m excited about this, more than ever,” she said.
About the Author
This article was written by Vas Panagiotopoulos, he is a freelance journalist based in Athens & London. Bylines in Politico, Quartz, Vice, CityMetric, openDemocracy, European Journalism Observatory, WIRED, Wallpaper*. See more.
For most non-technical folks, “hacking” evokes the notion of using sophisticated technical skills to break through the security of a corporate or government system for illicit purposes. Of course, most folks who were engaged in cracking security systems weren’t necessarily in it for espionage and cruelty. In the 1990s, I grew up among teenage hackers who wanted to break into the computer systems of major institutions that were part of the security establishment, just to show that they could. The goal here was to feel a sense of power in a world where they felt pretty powerless. The rush was in being able to do something and feel smarter than the so-called powerful. It was fun and games. At least until they started getting arrested.
Hacking has always been about leveraging skills to push the boundaries of systems. Keep in mind that one early definition of a hacker (from the Jargon File) was “A person who enjoys learning the details of programming systems and how to stretch their capabilities, as opposed to most users who prefer to learn only the minimum necessary.” In another early definition (RFC:1392), a hacker is defined as “A person who delights in having an intimate understanding of the internal workings of a system, computers and computer networks in particular.” Both of these definitions highlight something important: violating the security of a technical system isn’t necessarily the primary objective.
Indeed, over the last 15 years, I’ve watched as countless hacker-minded folks have started leveraging a mix of technical and social engineering skills to reconfigure networks of power. Some are in it for the fun. Some see dollar signs. Some have a much more ideological agenda. But above all, what’s fascinating is how many people have learned to play the game. And in some worlds, those skills are coming home to roost in unexpected ways, especially as groups are seeking to mess with information intermediaries in an effort to hack the attention economy.
It all began with memes… (and porn…)
In 2003, a 15-year-old named Chris Poole started an image board site based on a Japanese trend called 4chan. His goal was not political. Rather, like many of his male teenage peers, he simply wanted a place to share pornography and anime. But as his site’s popularity grew, he ran into a different problem — he couldn’t manage the traffic while storing all of the content. So he decided to delete older content as newer content came in. Users were frustrated that their favorite images disappeared so they reposted them, often with slight modifications. This gave birth to a phenomenon now understood as “meme culture.” Lolcats are an example. These are images of cats captioned with a specific font and a consistent grammar for entertainment.
Those who produced meme-like images quickly realized that they could spread like wildfire thanks to new types of social media (as well as older tools like blogging). People began producing memes just for fun. But for a group of hacker-minded teenagers who were born a decade after I was, a new practice emerged. Rather than trying to hack the security infrastructure, they wanted to attack the emergent attention economy. They wanted to show that they could manipulate the media narrative, just to show that they could. This was happening at a moment when social media sites were skyrocketing, YouTube and blogs were challenging mainstream media, and pundits were pushing the idea that anyone could control the narrative by being their own media channel. Hell, “You” was TIME Magazine’s person of the year in 2006.
Taking a humorist approach, campaigns emerged within 4chan to “hack” mainstream media. For example, many inside 4chan felt that widespread anxieties about pedophilia were exaggerated and sensationalized. They decided to target Oprah Winfrey, who, they felt, was amplifying this fear-mongering. Trolling her online message board, they got her to talk on live TV about how “over 9,000 penises” were raping children. Humored by this success, they then created a broader campaign around a fake character known as Pedobear. In a different campaign, 4chan “b-tards” focused on gaming the TIME 100 list of “the world’s most influential people” by arranging it such that the first letter of each name on the list spelled out “Marblecake also the game,” which is a known in-joke in this community. Many other campaigns emerged to troll major media and other cultural leaders. And frankly, it was hard not to laugh when everyone started scratching their heads about why Rick Astley’s 1987 song “Never Gonna Give You Up” suddenly became a phenomenon again.
By engaging in these campaigns, participants learned how to shape information within a networked ecosystem. They learned how to design information for it to spread across social media.
They also learned how to game social media, manipulate its algorithms, and mess with the incentive structure of both old and new media enterprises. They weren’t alone. I watched teenagers throw brand names and Buzzfeed links into their Facebook posts to increase the likelihood that their friends would see their posts in their News Feed. Consultants starting working for companies to produce catchy content that would get traction and clicks. Justin Bieber fans ran campaign after campaign to keep Bieber-related topics in Twitter Trending Topics. And the activist group Invisible Children leveraged knowledge of how social media worked to architect the #Kony2012 campaign. All of this was seen as legitimate “social media marketing,” making it hard to detect where the boundaries were between those who were hacking for fun and those who were hacking for profit or other “serious” ends.
Running campaigns to shape what the public could see was nothing new, but social media created new pathways for people and organizations to get information out to wide audiences. Marketers discussed it as the future of marketing. Activists talked about it as the next frontier for activism. Political consultants talked about it as the future of political campaigns. And a new form of propaganda emerged.
The political side to the lulz
In her phenomenal account of Anonymous — “Hacker, Hoaxer, Whistleblower, Spy” — Gabriella Coleman describes the interplay between different networks of people playing similar hacker-esque games for different motivations. She describes the goofy nature of those “Anons” who created a campaign to expose Scientology, which many believed to be a farcical religion with too much power and political sway. But she also highlights how the issues became more political and serious as WikiLeaks emerged, law enforcement started going after hackers, and the Arab Spring began.
Anonymous was birthed out of 4chan, but because of the emergent ideological agendas of many Anons, the norms and tactics started shifting. Some folks were in it for fun and games, but the “lulz” started getting darker and those seeking vigilante justice started using techniques like “doxing” to expose people who were seen as deserving of punishment. Targets changed over time, showcasing the divergent political agendas in play.
Perhaps the most notable turn involved “#GamerGate” when issues of sexism in the gaming industry emerged into a campaign of harassment targeted at a group of women. Doxing began being used to enable “swatting” — in which false reports called in by perpetrators would result in SWAT teams sent to targets’ homes. The strategies and tactics that had been used to enable decentralized but coordinated campaigns were now being used by those seeking to use the tools of media and attention to do serious reputational, psychological, economic, and social harm to targets. Although 4chan had long been an “anything goes” environment (with notable exceptions), #GamerGate became taboo there for stepping over the lines.
As #GamerGate unfolded, men’s rights activists began using the situation to push forward a long-standing political agenda to counter feminist ideology, pushing for #GamerGate to be framed as a serious debate as opposed to being seen as a campaign of hate and harassment. In some ways, the resultant media campaign was quite successful: major conferences and journalistic enterprises felt the need to “hear both sides” as though there was a debate unfolding. Watching this, I couldn’t help but think of the work of Frank Luntz, a remarkably effective conservative political consultant known for reframing issues using politicized language.
As doxing and swatting have become more commonplace, another type of harassment also started to emerge en masse: gaslighting. This term refers to a 1944 Ingrid Bergman film called “Gas Light” (which was based on a 1938 play). The film depicts psychological abuse in a domestic violence context, where the victim starts to doubt reality because of the various actions of the abuser. It is a form of psychological warfare that can work tremendously well in an information ecosystem, especially one where it’s possible to put up information in a distributed way to make it very unclear what is legitimate, what is fake, and what is propaganda. More importantly, as many autocratic regimes have learned, this tactic is fantastic for seeding the public’s doubt in institutions and information intermediaries.
The democratization of manipulation
In the early days of blogging, many of my fellow bloggers imagined that our practice could disrupt mainstream media. For many progressive activists, social media could be a tool that could circumvent institutionalized censorship and enable a plethora of diverse voices to speak out and have their say. Civic minded scholars were excited by “smart mobs” who leveraged new communications platforms to coordinate in a decentralized way to speak truth to power. Arab Spring. Occupy Wall Street. Black Lives Matter. These energized progressives as “proof” that social technologies could make a new form of civil life possible.
I spent 15 years watching teenagers play games with powerful media outlets and attempt to achieve control over their own ecosystem. They messed with algorithms, coordinated information campaigns, and resisted attempts to curtail their speech. Like Chinese activists, they learned to hide their traces when it was to their advantage to do so. They encoded their ideas such that access to content didn’t mean access to meaning.
Of course, it wasn’t just progressive activists and teenagers who were learning how to mess with the media ecosystem that has emerged since social media unfolded. We’ve also seen the political establishment, law enforcement, marketers, and hate groups build capacity at manipulating the media landscape. Very little of what’s happening is truly illegal, but there’s no widespread agreement about which of these practices are socially and morally acceptable or not.
The techniques that are unfolding are hard to manage and combat. Some of them look like harassment, prompting people to self-censor out of fear. Others look like “fake news”, highlighting the messiness surrounding bias, misinformation, disinformation, and propaganda. There is hate speech that is explicit, but there’s also suggestive content that prompts people to frame the world in particular ways. Dog whistle politics have emerged in a new form of encoded content, where you have to be in the know to understand what’s happening. Companies who built tools to help people communicate are finding it hard to combat the ways their tools are being used by networks looking to skirt the edges of the law and content policies. Institutions and legal instruments designed to stop abuse are finding themselves ill-equipped to function in light of networked dynamics.
The Internet has long been used for gaslighting, and trolls have long targeted adversaries. What has shifted recently is the scale of the operation, the coordination of the attacks, and the strategic agenda of some of the players.
For many who are learning these techniques, it’s no longer simply about fun, nor is it even about the lulz. It has now become about acquiring power.
A new form of information manipulation is unfolding in front of our eyes. It is political. It is global. And it is populist in nature. The news media is being played like a fiddle, while decentralized networks of people are leveraging the ever-evolving networked tools around them to hack the attention economy.
I only wish I knew what happens next.
About the Author
This article was written by Danah Boyd, researcher of technology & society | Microsoft Research, Data & Society, NYU | zephoria@zephoria.org.
The new year has come and 365 days gone by since my first visit to Japan. Japan is a showcase of disparity and paradox. Every city visited, it was clear that Japan is at a crossroads. From a foreign vantage point, Japan is continuously at odds with herself. An innovative country yet conforms to established practices; socially ridged but accommodating; economically sluggish for decades but socially stable; obsessed with the foreign yet reluctant to change. From ritualized tea ceremony to drunken karaoke; I’m grateful for these experiences and elated to finally disseminate my reflections.
Going into Japan, the facts were simple. Through the 1980s, Japan was significant in global competition, largely by shaping global technological trajectories, transforming major global industries, and contributing to fundamental innovations in industrial production processes, creating enough wealth along the way to propel Japan to the world’s second-largest economy. However, after the burst of the economic bubble in the early 1990s, Japan’s economic growth faltered. Its industrial competitiveness declined sharply as other places such as Silicon Valley, moved to the forefront of transforming technology, industries, and production, creating vast wealth along the way. While Japan’s role in global competition seemingly became largely irrelevant from the 1990s onward, my experiences within the country revealed Japan’s quiet, gradual, and significant transformation. While the world’s attention was focused elsewhere in the 2000s, most notably the rapid technological sophistication and breakthrough growth of China, Japan had been developing in significant ways, bringing them back into the global conversation. With that backdrop, my question for Japan didn’t focus on how far its entrepreneurship ecosystem had come or what today’s reality tells us but was there evidence of optimism regarding its future and how gradual change in a culture of stability and corporate loyalty, a long-dominant social and business value, can chart a new trajectory for Japan’s entrepreneurial ecosystem.
Since mid-2016, Japan’s startup ecosystem has developed considerably as many characteristics of the overall economy and social norms transformed to create a new environment for aspiring entrepreneurs. After several conversations with those embedded in the ecosystem, themes emerged that explains to Japan’s new startup environment:
1. Growing VC industry, the rise of independent VCs
2. Increasing labor mobility. Lower prestige and opportunity with large firms
3. Active efforts by universities, venture capital, and government to spin off successful startups with university technology
4. Firms more interested in open innovation, participation in corporate venture funds, and partnerships with early-stage startups
5. Rising attractiveness of entrepreneurship as large firms enter a competitive crisis
Japan’s venture capital industry has developed significantly. While fund size remains far smaller than that of the US biggest ecosystems (Silicon Valley, Boston, and New York), it still plays a relevant role in Japan. However, most VC funds are backed by big Japanese corporations, specifically financial institutions, and manufacturers. These players drive the startup boom, who sit on large amounts of cash to invest. These companies, with roots spanning decades if not centuries, have long dominated industries but now are warming up to startups signaled by their investment in outside VC funds and even launching internal corporate venture funds. But though Japan’s VC funds were up 450% in 2016, investors number one problem, they can’t find the innovators to invest. There is more money to go around in Japan, where young, daring risk-takers are still relatively scarce. Japan, since their technological rise in the 1980s, believed their focus should be on areas they’re inherently best, consumer electronics (hardware), gaming, and amine and discourage industries, no matter their profit potential i.e. software development, they can’t adequately compete. This philosophy is revealed in Japan’s history, investing in “Monozukuri”, the act of making things. Using Design for Manufacturing (DFM) and “Shisaku”, experimenting or prototyping has allowed Japan to sustain a competitive advantage but doesn’t provide an opportunity for disruptive innovation. A country where the culture of risk aversion is prevalent and entrepreneurship as a career is frowned upon, finding a home to park capital is the most difficult challenge Japanese VCs and corporations face. While places like the US and EU welcome failure, social pressure erupts from failures in Japan, discouraging its youth from pursuing entrepreneurial ventures. To further exuberate the issue, Sushi Suzuki, an Associate Professor at the Kyoto Institute of Technology and advisor to Makers Book Camp (https://makersboot.camp), the Japanese education system does not accommodate students’ desire to pursue entrepreneurship as a career. “Education in Japan is about memorization and repetition,” say Suzuki, thus students who decide to pursue entrepreneurship aren’t well equipped with the necessary skills and mindset required to succeed as value creators. Suzuki spoke to startups’ recent despair when raising capital where consensus is that language presents the number 1 barrier preventing startups from raising funds to back their ventures.
Another theme present was the increased interest in entrepreneurship by those with business background and experience working for foreign consulting firms but their STEM counterparts that see entrepreneurship as a career is in lower numbers. Both said to be caused by (1) students’ acceptance to prestigious universities considered the most challenging part of their lives. Once accepted, students slack off because their career path is seen as being paved. Secondly, those with engineering degrees receive the highest paid jobs. Therefore, if one is admitted to Japan’s top STEM university, what are their incentives to pursue entrepreneurship post-graduation? Lastly, universities resemble corporations in Japan and possess an enormous amount of power. Japan’s history shows that employees are married to their company, putting their company above things like family and well-being. Similarly, there is a marriage between Japanese startups and universities. As active efforts are underway by universities, venture capital, and government to spin off successful startups with university technology, students are hesitant to pursue entrepreneurial endeavors during their studies because of the lack of trust in relationships between student-launched ventures and the university.
We shouldn’t fool ourselves, Japan is changing. A change in its long, rich culture and tradition. Slowly, but deliberately, they are embracing innovation and disruption. The value the Japanese place on hard work has long been their competitive advantage but what creates innovation is not keeping hard at work but deciding what needs to be done, by whom and what not to do. I believe Japan is gradually learning this concept. I am grateful to Makers Boot Camp, specifically Sabrina Sasaki, Tugi Guenes, and Nikolas Schreiber, for their time in helping me understanding Japan’s ecosystem. To truly understand the influence Japanese culture has on the development of its entrepreneurial ecosystem, it’s pivotal to hear multiple perspectives that make up that ecosystem, from incubation, government to academia and venture capital. Nikolas, an American entrepreneur working with Japanese manufacturing partners, Tugi, as he prefers, a Swiss native of Turkish decent bringing a unique viewpoint to the venture capital space, and Sabrina of Markers Boot Camp, a Japanese Brazilian living in Kyoto who understands immensely the intricacies of her home. Time spent wasn’t enough to fully embrace the country’s offerings but nevertheless, further conversations with these amazing individuals brought learnings full circle and provided a picture of Japan’s rich and influential history, the complacency perception of its present, and how these forces create optimism for Japan’s creative and innovative future.
This article has been written by Corbin Norman, the chief Growth Officer at Bits which is currently leading the world’s principal consumer generation, Millennials, towards wealth and financial freedom. See more.
At The Moment, we close our innovation studio to all client and regular internal work during three separate week-long strategic planning and organizational development “retreats” every year.
When we tell people about this practice, they are amazed, impressed and often question how and why we make this kind of investment.
We have a long history designing and facilitating strategic planning offsites for our clients. Often these would be annual or even less frequent. The strategic planning horizon might be 3–5 years, with 1 year business plans as the primary output. It used to be a big part of our work. We know what works, what doesn’t, and we know what’s possible in one, two or three days facilitating groups of leaders through activities that are designed to move their attention from the day-to-day up and out to the horizons of the future.
We also know that strategic plans are useless without action, and that many a change agenda has crashed and burned shortly after being created when plans meet the realities of teams who may not be ready, or leaders who aren’t able to take up the work, or when assumptions are proven wrong by the unfolding of events.
So we have come to understand that a strategic planning retreat can be powerful, but is not sufficient. That doesn’t mean that they aren’t valuable investments in the increasingly busy world of work; on the contrary, they are so valuable that we need more of that kind of intentionally reflective and co-creative time together as the world around us changes at an accelerating pace.
Knowing all this, when our team takes time out from our busy schedules to do this work on ourselves, we know that the five days we dedicate are precious and that they provide a unique opportunity: to take ourselves through a deep process adapted from Otto Scharmer’s Theory U.
This (long) post is intended to illustrate that process by sharing how we used it during our most recent Strategy Week (“Strat Week”).
Why 3 Times a Year?
Our work as an innovation consulting and design firm historically follows a regular rhythm and business cycle. We have known this for a while, but it wasn’t until we read about how our friends at August Public Inc (formerly of Undercurrent) organized their calendar that we really saw the opportunity.
Our business cycle begins in January, with a number of new projects starting up in the new year. Many of those projects close or shift into a new phase around May, which launches another wave of busy project work before the doldrums of summer vacations. Then in late August our clients are getting ready to launch new initiatives in the fall.
We schedule our Strat Weeks for these periods: the first week in January, the first week in May and the last week of August — long in advance. We let our clients know that this time is sacred to us, and we plan our project work around them. We also organize our business planning around these trimesters, rather than conventional and arbitrary fiscal quarters.
Our strategic planning weeks sit at these three natural pivot points in our business cycle, where we have completed a lot of work we can learn from and share, and at a key moment when new work is being set up for the next period.
Why Five Days?
As a small services firm working in the rapidly changing field of innovation design, with clients who themselves are going through rapid change, disruption and transformation, our team has a lot to process. We need to be constantly adapting and reinventing ourselves to meet these rapidly changing conditions.
We also accumulate enormous amounts of learning through our work. Given this amount of learning, the space required to share, internalize, integrate and mobilize all the new knowledge takes time.
Part of our expertise lies in designing and facilitating collaborative processes for groups. We know that it takes time for people to work with not just the business and learning content, but also to connect to their own emotional selves in a way that supports the dynamics of the team as a whole. If you try to rush these processes to get the work done without attending to the human needs of those involved, we know that there is risk of wasting all that time and effort by leaving people behind.
This is often where plans fail to transform into committed action. If you can’t give enough space to the human dynamics at play, then you probably shouldn’t even bother trying to effect change. Trust, commitment and shared clarity are essential to the kind of strategic action that will need to follow through on any plan.
Who is Involved?
In our case, answering this question is easy: Everyone is involved. We are an 8-person team, and so strategy has always been a full team practice. With our growth plans, this will no doubt shift, but we know from our process design and facilitation work that this kind of group collaboration work can scale to much larger groups. Groups of 20, 60, 120 — and even larger — can engage in this kind of process, but there is a unique power that exists in the small group format.
As we grow, we will adapt the design of our Strat Weeks so that we can alternate between small group and large group activities, but I can’t imagine it not being a time for everyone in the organization to engage in this kind of reflection, reinvention and strategic thinking. It’s in our DNA.
Preparation
Erika and I led the initial design of the week, and then we invited other members of the team to contribute to and lead different modules. Part of our preparation came in the form of background research and the preparation of presentation material. But other team preparation came from an unusual source: Gimlet Media’s amazing podcast series, “Startup”.
We made listening to Startup required pre-work for the whole team. We recognized ourselves in many of the amazing moments, challenges and triumphs that the Gimlet team documented of themselves starting their own business. We were inspired by the transparency and power of storytelling to make sense of the uncharted territory of creating something that has never been done before. (Alex Blumberg and Matt Lieber, if you ever want help facilitating your own internal planning week, let’s talk!)
Day One: Evaluation and Problem Definition
Learning, Reflection and Evaluation
Our May 2016 Strat Week came at an important inflection point in our business. We had pivoted our value proposition 8 months prior, and now we had a great opportunity to evaluate our work and share our lessons learned. We focused our learnings on how our practice is evolving, how the business side of our work is doing, and how our clients are being impacted by that work. The morning of Day One was all about sharing these learnings from key projects, with team members presenting to the rest of the team.
A New Financial/Operating Model
We also needed to pay particular attention to our financial and operating model, with full transparency to all. Transparency is essential for the full team to have all the context to guide their decisions. So the Operations team prepared a simplified spreadsheet-based model of our business that would make sense to everyone.
This model was designed around our customer journey and business lifecycle, showing how marketing and business development leads to delivery to clients and how delivery to clients is supported by many of our internal operational activities and infrastructure. Think of the model as a Service Design approach to understanding our business financials.
This approach was much more useful than sharing accounting reports that seem to obscure rather than clarify things for those who didn’t know GAAP accounting principles or have lots of experience reading financial statements.
Problem Definition
This model was used to generate a number of different scenarios of our present and future. By working with scenarios in the context of what we had learned in the morning about our most recent project work, the problem we were there to solve became a clear and burning platform for change:
How might we create the conditions and set priorities for sustainable growth in pursuit of our purpose?
The scenarios we developed became reference points and common language for the rest of the week. For example:
How might we let go of X and start doing Y in order to move from Scenario 1 to Scenario 3?
All of our subsequent conversations were anchored in this shared understanding of how the business works now, or could work in the future, as well as a clarified set of problem definitions.
We left Day One feeling emotionally uncertain, and pretty raw. The size and scope of our challenge and opportunity was being felt by all, and many of us went home concerned about the implications: Were we up to the challenge of rapid growth? Would we all be ok through the change?
Simon walks the team through our current state service blueprint
Day Two: Deconstructing and Deep Reflection
Deep Dialogue in a Fishbowl
Rather than get to work right away solving problems, creating strategies or addressing specific opportunities, Day Two was spent working on us. Who are we, as humans, as a team, in the work we feel called to do? Our original plan for this day was to tackle more concrete work, but after Day One we knew that we needed to change our plan and spend more time processing and making sense of what we had learned about ourselves the day before.
The morning of Day Two was perhaps the most powerful moment of the week – at least it was for me. We needed to hold space for each other to connect into how we were feeling and making sense of the previous day. We did this using a modified version of a liberating structure we often use with clients, called Fishbowl.
We have a simple rule: we invite the whole person in to work. That means that when we feel insecurities, or pressures at home, or fears about the future, we invite that in and work with it not as off-topic material or a distraction, but as material that is essential for our work. We know that if we don’t work with the whole person in this way, the work we need to do with our clients and on ourselves won’t be done as well.
In this case, we decided that Daniel, Greg and I as the co-founders and owners of the business needed to hold the space for our team in order to hear all their voices. So we sat in the outer circle of the fishbowl, following the rule of Fishbowl to not react or speak to what we were hearing, while our team had a conversation in the inner circle.
It was an incredible moment for us as leaders. By sharing our business challenge with our team with full transparency, we had already created a shift. And now we got to hear how they thought about the challenge from a variety of very personal perspectives. We heard and felt how committed they were, their hopes as well as their fears.
Then we switched, and our team held space for us as co-founders and team members to be in the inner circle so we could share our own hopes, dreams, fears and uncertainties. There were tears. Building a business, especially a purpose-driven business like ours, is a very personal, intimate experience and we don’t shy away from the emotional side of it.
The final step was to collapse the fishbowl and return to one team sitting in a circle, having one conversation from a new place of authenticity and openness.
In this case, our dialogue process took the entire morning of Day Two. Is that a lot? Maybe. We’re heartful, open people who enjoy talking and we are unafraid of surfacing and unpacking tensions in order to resolve them. We know that when we create the space for this kind of dialogue and give it our full attention, the thing that happens is always the right thing.
Power, Authority and Decision-making
Rather than get right down to more strategy or planning work in the afternoon, Erika presented a piece on power, authority and decision-making. This was always part of our plan, as we knew we had some challenges in this area. We co-founders were holding onto too much even after having grown a more complete team, and many of our practices for decision-making were not explicit or clear to all. So we spent some time exploring different models, and opted to use a combination of the advice and consent decision-making modelsfor the vast majority of the things we do.
This was an important step in our journey to being more of a Teal organization as we grow. If you’re not familiar with this concept, read Reinventing Organizations author Frederic Laloux’ summary article for more background on the history, principles and methods. (Erika will be writing a separate piece that will go further into our journey into Teal.)
For the purposes of this post, it is enough to say that we are looking to become more adaptive, more responsive, more self-managing and more purpose-driven in all aspects of our organization’s structures and processes. Decision-making practices was the most important place for us to start.
Deconstructing and Unlearning
In retrospect, I believe that this deep reflection work on Day Two was key to our being receptive to new thinking. We had unconsciously over the first two days been moving down the U, downloading, unlearning, deconstructing and letting go of our old ideas in order to be able to sense from a new and more open place.
Day Three: Sensing and Strategy
Sensing into Our Environment
Turning our attention to some of the more traditional work of strategy began with a classic SWOT analysis, as well as an evaluation of the current state product-market fit of our three core offerings using the Value Proposition Canvas. We integrated our learnings from Day One while drawing upon previous work continually scanning our competitive and market environments, and the larger environmental trends and drivers we operate within.
We believe strongly that this kind of scanning of the environment is an ongoing and continuous activity: gathering signals, looking for trends. We spend much of our time working on these kinds of activities with our clients, so our team’s sense-making skills are highly developed, which is a great asset for this kind of work.
Sensing into Our Purpose
Before beginning to climb our way back up the U, the most important and powerful part of the process needed to be attended to. Otto Scharmer refers to this stage as “presencing”, and it is always a difficult thing to describe because it is not really observable.
Instead, it takes place from a mysterious source within each of us. The work of unlearning, deconstructing and letting go left each of us individually (and the team collectively) in an extremely uncertain, ambiguous but open state of receptivity to signals, having successfully suppressed our habits of reacting or defending our everyday beliefs and self-images.
I believe that presencing was happening in the shared space we created for ourselves, as well as in our dreams at night. We know from working with many teams on multi-day collaborative sessions that “sleeping on it” is a real method not to be underestimated. Our brains kept working on the challenge even while we slept. This was exhausting, but you could literally feel it happening.
Regardless of where it comes from, all this hard work of preparation put the team into the right state to allow us to sense into our purpose with far more clarity.
For us, our purpose is the “Why” of what we do. It is our central organizing principle. In a Teal organization, purpose trumps profit. That doesn’t mean that we don’t pursue profits, but it does mean that when push comes to shove, we will not allow short-term profit considerations to damage our purpose.
Purpose and Value Proposition
At Greg’s suggestion, we used the following framework to develop a Theory of Change in order to refine our purpose:
If we do this… (activities)
for these people… (stakeholders/customers)
which enables them to… (outcomes)
then we will… (impacts)
which serves our purpose to… (aspirational goal)
We drew sketches of our theory of change and how we understood how our programs created real impact, and how that expressed itself in our value propositions.
The result of all this was later combined into a new unifying purpose-driven value proposition:
The Moment’s purpose is to contribute to the development of sustainable, thriving and prosperous organizations, communities and economies. [purpose]
We accomplish our purpose by working with leaders and teams in organizations facing disruptive change. [customers]
We help these leaders and teams to build the strategies, projects, capabilities, knowledge and culture that will enable them to continuously create new value in fast-changing times by positively impacting the lives of people. [outcomes and impact]
Our long-term innovation programs accomplish this goal by providing leaders and teams with the capacity, knowledge, expertise and ongoing development they need to become sustainably innovative. [activities]
To be clear, these words were not the output of Day Three. Rather, they were synthesized from our raw material after having more time to reflect. At the time things felt much more ambiguous and unfinished. But we knew that the seeds were on our boards in the studio. And we are used to sitting with the resulting ambiguity longer than most teams normally can bear.
Day Four: Restructuring into Role Clarity
Jobs-to-be-Done
Erika told a story explaining how she learned that caterpillars transformed into butterflies through metamorphosis. As she described it, rather than shrinking legs and sprouting wings, the caterpillar dissolved into a kind of primordial goo which was then reshaped into a new form.
The previous days of hard work had really been about transforming ourselves into goo. Jobs-to-be-Done were the structural DNA of our new form. We used this framework to generate all the jobs that needed doing in our organization: in client projects, in day-to-day functions, in leadership…everything.
The jobs-to-be-done framework is:
When (some situation occurs), I need to (do a job), so that (outcome).
We hadn’t done this so comprehensively before, so it took some time, working in pairs to generate, review, cluster and theme the hundreds of jobs we discovered we were doing (and many we weren’t). But this was essential for the next step of our work: creating new roles.
Day Five: Roles, Project Teams and Plans
New Value Proposition: 12–18 Month Innovation Programs
On Day Five, it was time to divide and conquer to get our primary deliverables done. We needed a new business plan, centred on our renewed purpose and a new unifying value proposition.
We had realized that the way we could make our greatest impact on the world was to help our clients integrate a human-centred and adaptive approach to innovation into their organizations and teams; that work takes time, clarity of purpose, the right relationship with leaders and our commitment to being there through the whole journey until the new practices are “just how we do things around here”.
We developed our 12–18 month innovation program product concept to a draft that we believe reflects all our learnings and experiences in a way that our customers are going to love. The test will be in the coming weeks, as we bring this new value proposition to a select group of clients and prospects to see how well this more impactful offering will match their needs.
12–18 month innovation program sketch
Organization Design: Role Descriptions and Casting
While that work was underway, another team was refining our role definitions from the jobs-to-be-done, and making some initial attempts at casting those roles from among the team.
In this Teal approach, a role is not a job title. Each individual holds multiple roles. Within our client project work, roles are cast at project inception and different team members will play different roles depending on the mix of capabilities and the needs of the project. For our internal functions (finance, staffing, marketing, etc) we cast roles for the coming trimester, knowing that we would probably revisit them again at our next Strat Week, ready to make changes and rotate roles where it makes sense.
We also needed to cast these roles with our decision-making process in mind. For example, as Marketing Lead, Simon is the one who will be making decisions after following the advice process. Where another role, like Carolynn in Finance, feels they cannot consent to a decision (spending a lot more money than budgeted, for example), then a conflict resolution process would be triggered to address the objection or resolve the tension.
The real work remains as we enact and embody these new principles and processes into a new way of working and making decisions. This end to our Strat Week really was a prototype of this new structure, trying on the new roles and processes for size while we were in the safe space we had created for ourselves.
Role assignments board
Impact
A bit more than a week later, we know the investment of five days and lots of effort to go deep is already paying off. This has happened before. After last summer’s Strat Week, we saw almost immediate impact.
We started talking differently, and people started responding differently. When we talk about who we are, what we do and why, we are now much clearer and bolder. That clarity and boldness is compelling to people, and they respond very positively, which immediately reinforces the new behaviours, turning plans into action right away.
Our newly enabled team is stepping into their new roles with great aplomb and confidence. Watching this is affirming to us as co-founders, and it reinforces the trust we have placed in this team to do the right thing in pursuit of our purpose, knowing that we have their backs and they have ours.
I can feel that a shift has already happened. I already know that this five days was a remarkable achievement, and will be seen as a major milestone in the future history we are writing for ourselves.
For me personally, feeling liberated from much that I had been holding onto is allowing me to step into my leadership in a whole new way. It is also allowing me to refocus on my own well-being as a person, knowing that my team needs me to not be a burned-out control freak. And I know that my team will have my back and help me achieve my purpose, just as each member of the team will be supported in theirs.
It is a remarkable feeling. It’s worth every penny of that five days investment. And the return on that investment is going to be very significant, if the early signals are any indication.
References and Inspirations
We couldn’t have done the work we did without the contributions of several important supporters, inspirations and allies. Our tribe of people who are reshaping the future of work are incredibly smart people you should pay attention to:
Special thanks to Susan Basterfield, Open to Grow for being a sounding board and sanity check on the other side of the world.
About the Author
This article was written by Mark Kuznicki, co-founder and Innovation Designer, The Moment. Strategist, advisor and change-maker focused on making the world better for people.
The topic of Purpose has become one with increasing salience in the world of business. You can read about it in Forbes, HBR, Inc. and Fast Company. But it appears that the mainstream business press hasn’t really taken on the subject with much depth.
If you know Simon Sinek, you are familiar with his concept from “Start with Why”. He describes simply the role of Purpose in business: “People don’t buy what you do, they buy why you do it.” It’s a useful start to the conversation.
Purpose can be an abstract topic, so it’s important to clarify terms.
Our purpose is our reason for being. The pursuit of one’s purpose is about the desire to make meaning and achieve fulfillment; to live a life that fully realizes our talents and to look back upon our life’s work with satisfaction. It is sometimes described as our vocation, that space that lies at the intersection of the realization of our greatest talents and what the world needs most from us.
Every individual has the potential to discover and pursue their purpose. One of the best people I know and talk to on this topic is Sean Howard . He experienced his own difficult journey to discover his purpose. He dropped out of the marketing agency rat race and now pursues his passion for art photography while helping others seek and find their own purposes. In a recent conversation over drinks (which often feel like sitting with a sage) he asked me important questions, less about my business, more about my self. What feeds my creative soul? I came away realizing that without being clear and centred in my personal purpose, I wasn’t much use to my team and the business we are building. Seekers can check out his book and his podcast, Taking the Leap.
While a clarity of purpose (with luck and hard work) exists within us as individuals, it also can combine and connect us within purpose driven teams and organizations. I’m not only referring to social enterprises or social purpose businesses, not only those who incorporated as B Corps, and not only those that have a specifically social, public or environmental good objective. All organizations should have a purpose.
This is a conversation that The Moment co-founders Greg, Daniel and I have been having ever since we began in 2011. We felt called to change the world while building a business around our passion for collaborative innovation design tools, methods and process.
Today, The Moment’s purpose is to help organizations build the capabilities to design better services, business models and products that positively impact people’s lives.
We believe that the tools of human-centred design, systems thinking and collaborative work will transform organizations and make life better for the people those organizations serve and the people who work within them.
While pursuing this work, we also help leaders and teams connect with their own purpose, their own why, and help them keep that purpose front of mind while they follow their unique and personal journey through the world of innovation.
When we at The Moment think about our organizational and team purposes, we are inspired by another good friend, Mark Raheja and his cofounders at August, formerly of Undercurrent. For August and other organizations at the leading edge of the future of work, models such as Holacracy are employed that use purpose as a central organizing principle of governance at all levels of the organization in a way that is regularly adapted to changing conditions.
Among the things that purpose driven teams need to devote ongoing care and attention to is how they negotiate and articulate purpose for themselves while connecting their team purpose up to the organization level and down to that of the individual players. Crafting purpose statements becomes a core practice, something that demands time and attention, but is rewarded with clarity and motivation from everyone on the team.
At The Moment, purpose connects our talent to our customers and back again. Our customers are intrapreneurs and change agents. Our team is made of the kinds of thoughtful creative people our customers love to work with.
Living and breathing our purpose beyond some words on our website connects to other practices and simple rules we live by, including:
We welcome the whole person at work
Nobody works alone
We don’t have managers, we have coaches
When we have tensions, we address them directly and work through them
We know that our focus on purpose presents us with decisions that other firms wouldn’t ever worry about. We wouldn’t hire anyone that isn’t aligned with our purpose and our core values. We’ve had to say goodbye to clients when their goals started to diverge from our purpose or go against those values. We approach our growth with careful deliberation as opposed to wild abandon.
In the end, we believe that purpose will win. At the end of our journey together we will look back and feel pride and satisfaction that we created something special, a business that fed the source of our creativity and created unique value in the world.
About the Author
This article was written by Mark Kuznicki, co-founder and Innovation Designer, The Moment. Strategist, advisor and change-maker focused on making the world better for people.
There is no doubt that data is behind of any successful company.
Data have been around for decades. Companies were getting benefits from these data by applying different “statistical methods”.
After some years, with the growth of data and the revolution of technology, companies started extracting patterns from data which lead to “data mining”.
Similarly, after few some years, due to new mathematical and statistical models, companies can now perform more accurate forecasts which lead to “predictive analytics”.
Revolution of Data Science
Get closer than ever to your customers. So close that you tell them what they need well before they realize it themselves. Steve Jobs.
Explosion of data
The arrival of internet, social media and the digitization of everything around the world have led to massive amount of data generated every second. For example:
Retails databases, logistics, financial services, healthcare and other sectors.
computers’ capabilities to extract meaningful information from still images, video and audio.
Smart objects and Internet of Things.
Social media, personnel files, location data and online activities.
Machine generate data, computer and network logs.
Accordingly, Big Data is defined by 3Vs (Volume, Variety and Velocity).
Volume: amount of data (Terabytes, Petabytes or more)
Variety: types of data (Text, Numbers, Files, Images, Video, Audio, machine data…)
Velocity: speed of data processing (Real-time, Streaming, Batching, uncontrollable…)
Additional Vs can be added to Big Data definition such veracity, variability, visualization and value.
Veracity: trustworthiness of the data. For example outdated contact numbers are inaccurate and the business cannot rely on it.
Variability: focuses on the correct meanings of row data that depends on its context. For example the word “Great” gives an positive idea, however “Greatly disappointed” gives negative impression.
Visualization: refers to how the data is presented to business users (tables, graphical views, charts…)
Value: unless turning data into value, it is become useless. Businesses expect significant value from investing in Big Data.
Big Data Challenges
Big data is so big and complex that traditional computer solutions, relational databases, data processing methods and traditional analytics are not scalable to deal with it.
Accordingly, for getting value from Big Data, organizations have to deal with Data Pipeline and Data Science.
The infographic below illustrates the process:
Big Data Science Cycle
What is a Data Pipeline — ETL?
At the beginning of any analytics, data-driven decision require well-organizedand relevant data stored in a digital format. To get there, Data Pipeline is needed.
A Data Pipeline, also known as ETL (Extract — Tranform — Load), is a set of automated sequential actions to extract data from “different sources” and load it into a “target databases or warehouse”. During this process, data needs to be shaped or cleaned before loading it into its final destination.
Data Streaming Process
Extract, Transform and Load (ETL) is considered the most underestimated and time-consuming process in data warehousing development. Often 80% of development time is spent on ETL. J. Gamper, Free University of Bolzano
ETL process involves the following actions:
Extract: Connecting to various data sources, selecting and collecting the necessary data for further processing.
Transform: Applying various business rules and operations such as filtering, cleaning, sorting, aggregating, masking, validation, formatting, standardizing, enrichment and more.
Load: Importing the extracted and transformed data into warehouse or any target database.
What is Data Store?
After Extract Transform Load process, data will be stored into a ready-to-consume format for analytics. But due to the variety, volume and value of data, different technologies and methods should be considered.
Accordingly, a Data Store is a repository for persistently storing and managing collections of data which include not just repositories like databases, but also simpler store types such as simple files, emails etc. Wikipedia
Data store may be classified as:
Warehouse: is a technology that aggregates structured data from one or more sources so that it can be compared and analyzed to provide greater executive insight into corporate performance. #structuted #relational #performance #scalable
Data Lake: is a centralized storage repository that holds a vast amount structured and unstructured data at any scale. Data can be stored data as-is, without having to first structure the data, and run different types of historical and real-time analytics
MDM “Master Data Management”: is a comprehensive method to link all critical data to a common point of reference. It’s a pillar to improve data quality.
For example, suppose a customer is presented in many systems within the organization, but his name, address might not be same in all the systems. For this reason we need methods for cleansing the data, match the data and then create a unique Master version of the existing data.
Extract Business Value
Big Data Analytics is a combination of scientific methods, processes, algorithms and systems required to extract business value, knowledge, insights, intelligence, analytics and predictions from data.
Extract Business Value — Data Analytics vs Data Analysis
Data Analytics covers different areas and goals such:
Business Intelligence — BI: is a combination of technologies and methods that use current and historical data to support strategic and tactical data-driven business decisions. The analyzed data will be presented in the format of metrics, KPIs, reports and dashboards.
Advanced Analytics: works beyond of traditional business intelligence (BI), to discover deeper insights, make predictions and forecasting “Predictive Analytics”. Also it enables businesses to conduct what-if analyses to predict the effects of potential changes in business strategies. It includes different techniques such:
Data mining, pattern matching and forecasting
Semantic, sentiment, network, cluster, graph and regression analysis
Multivariate statistics, simulation, complex event processing and neural networks
Machine Learning — ML: is creating an algorithm, which can be used by computers to find a model that fits the data as best as possible, and makes very accurate predictions based on that.
The concept is build a “Model” by implementing algorithms to train the “Machine Learning” using data. Accordingly, the ML tries to categorize data based on its hidden structure. Roughly, training algorithm can fall into three categories Supervised, Unsupervised and Reinforcement.
About the Author
This submitted article was written by Peter Jaber, a solutions Architect with over 20 years of experience. Contact.
Tech companies are facing a growing tsunami of social and ethical risks, and it’s one that they’re desperately ill-prepared to face. Yet by embracing new and emerging ideas around risk and responsibility, there’s no reason why they can’t learn to ride this wave.
As new technologies become increasingly intertwined with emerging social trends, technology companies are having to learn how to navigate an increasingly complex and convoluted risk landscape. And as companies like Facebook, Uber, Apple, and others are learning, this is not easy.
It’s exactly this challenge that the Palo Alto-based Institute for the Future and the Omidyar Network set out to address with the just-released “Ethical OS Toolkit”. The toolkit is designed to help tech innovators “anticipate the long-term social impact and unexpected uses of the tech we create today” by identifying emerging areas of risk and social harm, sparking conversations and prompting ethical actions.
“To navigate a radically shifting risk landscape, businesses, governments, not-for profits, consumers, and others, need equally radical innovation in how to think about and act on risk”— ASU Risk Innovation Lab
The Ethical OS Toolkit is a much-needed resource in a sector that’s behind the curve when it comes to social and ethical risks. But it still falls short of providing everything tech companies need to survive in today’s shifting social environment.
Rethinking Risk
For many years now, my work has focused on emerging risks associated with technologies that span nanotechnology and gene editing to self-driving cars and artificial intelligence. And over this time, it’s become increasingly clear that developers and manufacturers need a total reboot in how they think about risk.
It’s no longer enough for tech companies to simply state that their products are “safe enough”, and that they comply with relevant regulations. Rather, they are increasingly expected to affirm that their products don’t threaten livelihoods and lifestyles; that they don’t disadvantage and discriminate against minorities and others; and that they don’t threaten people’s beliefs and aspirations. These are critical threads in the fabric of society, and they present social risks that can make or break a company if they’re ignored.
Yet they are often poorly understood.
The ways tech innovation affects our lives are becoming more complex. Photo by ev on Unsplash
Of course, being socially responsible and financially successful is a daunting challenge — especially for entrepreneurs desperately trying to find a foothold in a fickle market. Yet the harsh reality is that companies that do not attempt to rise to this challenge, or that cynically put on a shallow façade of social responsibility, risk failure.
It’s social risks like these that the Ethical OS toolkit begins to address. But to be successful, tech companies are going to have to look even more broadly at how they potentially threaten what’s important to others within society — and in doing so bump up against social risks — and how this may in turn threaten their business.
Connecting Expertise to Emergent Challenges
The good news is that there’s a wealth of expertise available that entrepreneurs, startups, established businesses, and others, could and should be tapping into to navigate this increasingly complex social risk landscape. Much of this resides in organizations that study responsible innovation, including my own academic home in the Arizona State University School for the Future of Innovation in Society.
Here, researchers are exploring how businesses can innovate responsibly while remaining competitive, and how partnerships and collaborations can lead to products that combine technological and business savvy with social responsiveness. This includes research in my own Risk Innovation Lab, where we’re developing innovative ways of thinking and acting on risk that can help technology companies be successful and socially responsible.
Looking to the future, the social and ethical risks that tech companies face are only going to get tougher. Yet if these businesses begin to innovate in how they think and act on risk — whether by using tools like Ethical OS, partnering with experts, or recalibrating their corporate mindset — there’s no reason why they cannot ride this wave of social risk.
They may even find that a risk reboot gives them the competitive edge they need to thrive in today’s socially complex world.
About the Author
This article was written by Andrew Maynard, director of the ASU Risk Innovation Lab & author of “Films from the Future” — a unique take on future tech & ethical innovation http://bit.ly/filmsfromthefuture
Ray the Scientist, Egon the Engineer, Peter the Charmer and Winston the Muscle. Different competencies putting on matching overalls and embarking on a shared mission: saving NYC from ghosts.
Here’s how you can inject the same level of awesome to your Agile organization.
Don’t worry, ma’am. We know Scrum.
As I wrote earlier in The #1 Reason to do Agile, one of the key stinks of an Agile-hostile environment is organizing teams by competency: Tech teams, QA teams, UX teams etc. This setup is ripe for bottlenecks, handoffs and weird cross-prioritization exercises that kill autonomous end-to-end value creation.
The Ghostbusters didn’t have these problems. They were fully autonomous and cross-functional!
But what if they didn’t successfully eradicate the ghost problem in one fell swoop like in the movie, and had to scale up their operation? Should Egon hire and lead a team of engineers, and Ray a team of scientists? How will they organize when multiple ghosts need to be dealt with simultaneously?
The natural benefit of Egon setting up Ghostbusters Engineering is that every professional deserves to be managed by someone who understands their craft. Someone who can guide them to learn new skills, take their expertise to a new level and guide their career growth.
This isn’t possible when you’re managed by someone whose expertise isn’t the craft of your choice.
But you should also be able to exercise your craft in a group containing all the required skills to execute on their mission. Four Winstons would be useless in catching ghosts, but having exactly one Winston is critical for successful ghostbusting missions.
Decoupling Work and People Management
What the Ghostbusters need to do, is consider the simple split between product and competencies outlined in it:
Product: Do the right things Competencies: Do things right
What this gives us is a simple way of splitting the concept of “management” right down the middle.
Work
What things are we working on?
Why are we working on these things?
When are we working on a thing and when do we expect to deliver it?
Why is thing X more valuable / important than thing Y?
Who is impacted by our work and how?
ie. “Do the right things”
People + Competency
Who works for us?
Are our people getting better at what they do every day?
Are our people happy and motivated?
Is person X solving problems with the same tools, approaches and methodologies as person Y?
Are we staying current with the industry’s progress and best practices?
Are we managing the shortcuts we take?
ie. “Do things right”
Allowing a do-the-right-things specialist to take over the first bucket and a do-things-right specialist take over the latter, everyone wins!
The Spotify Model
The most famous real world implementation of this split is Spotify. Their essential presentation (part 1, part 2) on their Squads & Chapters model is the gold standard of setting up guidelines for a well-managed truly Agile environment that scales beyond a single team or product.
The foundation of this organization model is two views into one organization: by competency and by mission. Squads and Guilds.
Guildmembers gonna guildmember
Guilds
Everything starts with the classic competency-based org chart as the basis of your reporting structure and chain of command. As usual, it is built 100% based on crafts.
The teams at the branches of the org tree should be centered around a certain competency, like QA, front-end engineering, back-end engineering, UX and dev-ops.
Every team should be managed by a people + competency manager. UX designers reporting into a UX Manager, front-end engineers into a front-end engineering manager… and paranormal scientists into Ray Stantz.
This creates the backbone of your organization. Spotify calls them “chapters”, but I personally prefer to call them “guilds” as that’s a far more accurate description.
The word “guild” starts making sense if you view your organization as a classic fantasy role playing game (who doesn’t, duh!) To go do fun stuff, like slaying dragons or raiding dungeons, you’ll need a balanced party. To get that party, you need to assemble it from the various guilds of different character classes: warriors, mages, priests, thieves and so on.
This competency-based org chart is exactly that: your “character classes”. This redefines the role of the competency manager (or “guildmaster”) as the maintainer of the barracks.
The master mage makes sure their guild attracts all the right mages, develops the most potent new spells and recognizes obscure new wands and staves.
The front-end guildmaster makes sure his guild stays on top of the latest javascript frameworks (there’s always one), defines best practices for front-end engineering and makes sure all developers in the guild are leveling up.
Different competencies coming together for autonomous end-to-end value creation.
Squads
While having guilds with proper barracks in town is the backbone of your operations, they’re useless unless the members of those guilds embark on valuable missions organized into balanced parties, or squads.
To kill a dragon, you need a certain composition of a squad, to rescue prisoners a different one. What makes a balanced squad depends completely on what talents are required to execute on its mission.
To succeed, a squad also needs a “squadleader”. Somebody who can guide the squad members coming from different backgrounds to unify into a holistic team.
Enter: the “do the right things” people. Product!
I allow myself one sports reference per year. This is it.
Just for the record, if RPGs aren’t your jam, I can also do this with an ice hockey analogy. Guilds are positions (wingers, centers, defenders and goalies) and squads are lines. To become a great player, you need to master your position and be trained by someone who knows how that position is played. To win games, you need to come together with a group of people of different positions and learn to pool your complementing skills.
All Together Now: Squads and Guilds
Example org of Squads and Guilds in action
This model instantly removes the inherent overlap in having multiple managers for each team: a tech manager and a product manager. When both forms of management have their own unique view into the organization and clearly defined responsibilities, they can focus on what their strengths are in harmony.
This format allows competency specialists to focus on managing their craft and the people executing it. Instead of dates, commitments, deliverables, change and other boring stuff competency managers usually hate.
It also empowers Product Managers to take full control over the backlogs and commitments of their cross-functional Scrum teams. Us product folk are weird enough to enjoy that stuff.
Additionally, it gives clarity, purpose and identity for team members. Their guild defines who they are and their squad what they do.
The real secret sauce of the system is the interplay between squads and guilds, especially the communication between squadleaders and guildmasters. Guildmasters need to respect the sovereignty of the squadleaders to manage the work of their squad members and squadleaders need to view guildmasters as stakeholders of the highest order.
Squadleaders & Guildmasters, unite and take over!
When done right, this structure provides the most fertile ground for doing Agile and Scrum in a larger organization. It provides responsible autonomy through a clear chain-of-command, not just for the competencies, but also for the outcomes of the work. It helps abolish handoffs and dependencies while elevating accountability. That sounds like true Agile to me!
When you couple this structure with executing Scrum meticulously in every squad, you also get clear visibility into what the organization is doing and where it’s headed.
But most importantly, it fosters an environment that gives focus, clarity and a sense of purpose for the actual people doing the work. It surrounds them with two equally important peer groups: their guild to support their growth as a practitioner of their craft and their squad to help them complete missions successfully.
Even if their mission is busting the Marshmallow Man.
About the Author
This article was written by Tommi Forsstrom, CPO-in-Residence at Produx Labs. Part of the CPO growth program of Insight Venture Partners and PxL. see more.
