My notes on Crashed by Adam Tooze

I have just finished Crashed - How a Decade of Financial Crises Changed the World by Adam Tooze and can recommend it.

I came across Tooze on the excellent Talking Politics podcast where David Runciman, Helen Thompson and Tooze discussed the connections between the 2008 financial crisis, the eurozone crisis and more generally, how futile it is to talk about economics and politics as separate topics. I bought his book in the hope that it would level up my understanding of monetary policy and the connections between geopolitics and financial markets. It is a dense, long read (600+pp) but I found it a real page turner.

Some of the things that I found most interesting:

  • How liquidity works in globalised financial markets and the connections between central banks, investment banks, money market funds, and other major financial actors like sovereign wealth funds, the IMF and insurance companies.

  • With the historical perspective of writing in 2019 about about the 2008 crisis, Tooze captures how the credit crisis of 2008 rumbled on for almost a decade and arguably with the 5th round of QE is still affecting financial markets.

  • How, In the lead up to the 2008 crisis most macroeconomists misallocated risk. Most were concerned about interstate economic relations and a potential sovereign debt crisis (primarily China’s accumulation of US treasuries, broadly as a result of China’s mercantilist trade policies and the cost of Bush’s tax cuts ($1.35 trillion over 10 years) and the Iraq and Afghanistan wars ($1-3 trillion)). This obscured the tension building up in the interbank system - in particular the exposure of European banks to both the US housing market and the balance of interbank dollar flows. Connections between banks based in different countries create systemic risks in the same way as international trade imbalances. However, unlike trade imbalances these relationships are harder to scrutinise, and can mutate much more rapidly in the event of a ‘global bank run’. Tooze gives the example of how Larry Summers “slapped down” Raghuram Rajan at Alan Greenspan’s farewell party (!) for flagging the new systemic risks as “luddite” and “misguided”.

  • How the financial crisis was fundamentally an Atlantic crisis “America’s securitized mortgage system had been designed from the outset to suck foreign capital into US financial markets….overall, two thirds of the commercial paper issued had European sponsors, including 57% of the dollar-denominated commercial did European banks end up owning such a large slice of American mortgage debt? The answer is that European banks operated just like their adventurous American counterparts. They borrowed dollars to lend dollars...Indeed, in 2007, roughly twice as much money flowed from the UK to the United States as from China...hundreds of billions of dollars...flowed out of the US from the branches of foreign banks in New York to the head offices of European banks, from which they returned for investment in the US, sometimes by way of an offshore tax haven...European banking claims on the US were the largest link in the the process the European financial system came to function, in the words of Fed analysts, as a ‘global hedge fund’, borrowing short and lending long...the entire structure of international banking in the early twenty-first century was transatlantic...52% of the mortgage-backed securities sold to the Fed under QE were sold by foreign banks, with Europeans far in the lead”.

  • How ‘market based insurance’ aka financial derivatives totally failed to stabilise the system and how instead “it turned out that we lived in an age not of limited, but of big government, of massive executive action, of interventionism that had more in common with military operations or emergency medicine than with law-bound governance...the decision made by the American crisis fighters to take those questions off the table and to give absolute priority to saving the financial system shaped everything else that followed. It set the stage for a remarkable and bitterly ironic inversion. Whereas since the 1970s the incessant mantra of the spokespeople of the financial industry had been free markets and light touch regulation, what they were now demanding was the mobilisation of all the resources of the state to save society’s financial infrastructure from a threat of systematic implosion... Martin Wolf, the FT’s esteemed chief economic commentator, dubbed March 14 2008, ‘the day the dream of global free market capitalism died’...A conservative, free-market administration lead by businessmen was proposing unlimited state spending to nationalise a large part of the housing finance system”.

  • How despite a narrative of globalisation, the global financial system is fundamentally hierarchical with the dollar at the top, and how the availability of Fed swap lines (with 14 other central banks) during the crisis defined the next level of the hierarchy: “As two US analysts attached to the National Intelligence Council remarked at the end of 2009: ‘Artificial divisions between ‘economic’ and ‘ foreign’ policy present a false dichotomy. To whom one extends swap lines is as much a foreign policy as economic decisions”. The Fed broadly appears to have maintained this authority throughout the crisis through massive global intervention “every major bank in the entire world was taking liquidity assistance on a grand scale from its local central bank, and either directly indirectly by way of the swap lines with the Fed...what happened in the fall of 2008 was not the relativisation of the dollar, but the reverse, a dramatic reassertion of the pivotal role of America’s central bank. Far from withering away, the Fed’s response gave an entirely new dimension to the global dollar”.

  • How important differences between central banks can be - he analyses the fundamental differences in mandate and agency between the Fed, the Bank of England and the ECB and the consequences for how the US, the UK and the Eurozone handle the crisis: “What the ECB did not have was a mandate to concern itself with the economic health of the eurozone or its member states in any broader sense...The Fed never took such a narrow view. It had a mandate both to preserve price stability and to maximise employment”.

  • How geopolitical the ‘economic’ decisions are - one example is how Paulson described his rationale for bailing out Freddy Mac and Fannie May as a result of them being “too Chinese to fail”. The Ukraine crisis of 2013 emerges partly as a result of the unexpectedly weak support the IMF and EU offered to a Ukraine split between EU and Russian interests (“25% of Ukraine’s exports went to the EU, but 26% went to Russia”) and the resulting economic shock. Putin captures this pithily with his line “geopolitics is geoeconomics”.

  • How, in a era where ‘elite’ bashing has become part of populist political rhetoric, Tooze argues that ‘elite closure’ (where government treasury staff, heads of investment banks and central bankers share common backgrounds and are tightly networked) enabled certain countries (the USA, France) to more rapidly take aggressive, coordinated actions and move past a stage of the crisis faster than other where the financial actors found it harder to coordinate.

  • How poorly the various commercial rating agencies perform in determining the risk associated with different securities and one possibility as to why: “since the 1980s it was issuers of debt who paid the ratings agencies to make their classifications, not the subscribers to their information services. Payment by the issuer created a conflict of interest”. During the heart of the crisis “effectively the Treasury and the Fed would make themselves the credit-rating agencies in chief - the ‘United States of Moody’s’ - official arbiters of private creditworthiness”.

  • How quietly, subtly and profoundly regulation is be rewritten in favour of finance, “in July 2004, as subprime was really hitting its stride, the regulators agreed to provide a permanent exception that effectively allowed assets held in SIVs to be backed by only 10% of the capital that would have been required if the assets were held on the balance sheets of the banks themselves….It was following that regulatory shift that the ABCP market exploded from $650b to in excess of $1t....More than the grand gestures of deregulation, like the 1999 act, it was this kind of apparently small-scale regulatory change that unfettered the growth of shadow banking”.

  • How powerfully defaults can act: “automatic stabilisers are the unsung heroes of modern fiscal policy. In the US, no more than one third of federal government spending is discretionary. The rest is made up of mandatory expenditures required by existing ‘entitlements’....these tend to increase in a recession...Between 2007 and 2011, demand in the world economy was stabilised by the largest surge in public debt since WWII.”

  • How and why the Eurozone coped so poorly with the crisis - broadly because it was a “monetary union that unified financial markets but provided none of the institutions of governance required for a banking union”.

  • How major economic policy decisions are often driven by narratives grounded in sloppy, simplistic analysis. He gives the example of austerity decisions which were based on research from “ultrarespectable...former IMF economists Reinhart and Rogoff…In January 2010 they launched a research paper...this purported to show that as public debts passed the threshold of 90% of GDP, economic growth avoid this fate it was necessary to take action sooner rather than later. On closer inspection Reinhart and Rogoff’s analysis turned out to be riddled with errors. Once their Excel spreadsheet was properly edited, there was no sharp discontinuity at the 90% markt and the case for emergency action was far weaker than they made out. But in early 2010 their arguments ruled the roost...Earlier and more sharply than in any other recession in recent history, the fiscal screw was turned. On both sides of the Atlantic the result was to stunt the recovery...The Reinhart and Rogoff meme had reached Europe. Finance minister Schäuble invoked the menacing 90% threshold”. Another stark example is some dodgy economic modeling by the IMF that “systematically underestimated the negative impact of budget cuts. Where they had started the crisis believing that the multiplier was on average around 0.5, they now concluded that from 2010 forward it had been in excess of 1. This meant that cutting government spending by 1 euro, as the austerity programs demanded, would reduced economic activity by more than 1 euro…It was a staggering admission. Bad economics and faulty empirical assumptions had lead the IMF to advocate a policy that destroyed the economic prospects for a generation of young people in Southern Europe.”

I'm joining UCL as visiting professor

I have a bit of news to share - I’ve been appointed Visiting Professor (!) at UCL working with the wonderful Mariana Mazzucato at the department she founded, The Institute of Innovation and Public Purpose (IIPP).

I first came across Mariana back in 2015 when I read her book, The Entrepreneurial State. In it she examines how states have actively shaped technology markets by declaring ambitious missions (for example putting a man on the moon) and making visionary and significant investments in basic scientific research. It made my best books of the year list and the data and arguments she presented fundamentally shifted my worldview.

After I wrote my essay on AI Nationalism last year, I bumped into Mariana at an event and we hit it off. She is a formidable intellectual sparring partner and hugely fun to spend time with - she engages in serious issues without taking herself seriously. I greatly admire the way she has collaborated with politicians across the ideological spectrum (from David Willetts to Alexandria Ocasio-Cortez!) and is influencing policy in many different areas from pharma to the Green New Deal. I believe her UCL Institute for Innovation and Public Purpose (IIPP) will significantly influence the most ambitious policy makers in the years to come.

I’m also excited to spend more time within the UCL world - UCL has played an important role in recent developments in machine learning - two of the co-founders of DeepMind - Demis and Shane met while studying at UCL’s Gatsby Computational Neuroscience Unit.

Finally I’m looking forward to learning from the great group Mariana has assembled at her institute including people like Mike Bracken who lead the digitisation of UK government services at GDS, Carlota Perez who wrote a seminal book on technological revolutions that has greatly influenced investors like Fred Wilson and Josh Ryan-Collins who wrote a phenomenal book on the economics and housing.

I’m going to be using IIPP as my new base to work on the political and economic implications of machine learning. This will be a part-time thing and I’ll continue investing in start-ups and tracking the latest machine learning research with the rest of my time.

My favourite books, films, music of 2018

For the seventh year running….here’s what I loved the most in 2018:

1. The Making of the Atomic Bomb (Rhodes)
2. Capital (Marx)
3. Darkness at Noon (Koestler)
4. The Prize (Yergin)
5. The Heart is a Lonely Hunter (McCullers)
6. All Things Shining (Dreyfus & Kelly)
7. Jean Monnet, First Statesman of Interdependence (Duchene)
8. Stories of Your Life and Others (Chiang)
9. A Life’s Work (Cusk)
10. What Belongs to You (Greenwell)

Honourable mentions: Just in Time (Hoskyns), Around the Coast in 80 Waves (Bennett), The Value of Everything (Mazzucato), Destined for War (Allison), The Common Thread (Sulston), The Fabric of Reality (Deutsch). Thanks to Demis, Dom and Jeff for the strongest recs this year!

Films (released in UK in 2018):
1. The Square
2. Florida Project
3. Annihilation
4. Leave No Trace
5. Tully
6. I, Tonya
7. Loveless
8. A Prayer Before Dawn
9. Black Panther
10. Revenge

Music (for albums released in 2018 and standout track):
1. Pusha T (If You Know You Know)
2. Chance the Rapper (65th and Ingleside)
3. Anderson .Paak (6 Summers)
4. Jimothy Lacoste (Subway System)
5. Lily Allen (Trigger Bang)
6. Tierra Whack (Pretty Ugly)
7. Kendrick Lamar (X)
8. Cardi B (I Like It)
9. Mitski (Old Friend)
10. Low (Tempest)

AI Nationalism

For the past 9 months I have been presenting versions of this talk to AI researchers, investors, politicians and policy makers. I felt it was time to share these ideas with a wider audience. Thanks to the Ditchley conference on Machine Learning in 2017 for giving me a fantastic platform to get early feedback on my ideas. Thanks also to Nathan Benaich, Jack Clark, Matt Clifford, Jeff Ding, Paul Graham, Michael Page, Nick Srnicek, Yancey Strickler and Michelle You for helpful conversations and feedback on this piece.


The central prediction I want to make and defend in this post is that continued rapid progress in machine learning will drive the emergence of a new kind of geopolitics; I have been calling it AI Nationalism. Machine learning is an omni-use technology that will come to touch all sectors and parts of society. The transformation of both the economy and the military by machine learning will create instability at the national and international level forcing governments to act. AI policy will become the single most important area of government policy. An accelerated arms race will emerge between key countries and we will see increased protectionist state action to support national champions, block takeovers by foreign firms and attract talent. I use the example of Google, DeepMind and the UK as a specific example of this issue. This arms race will potentially speed up the pace of AI development and shorten the timescale for getting to AGI. Although there will be many common aspects to this techno-nationalist agenda, there will also be important state specific policies. There is a difference between predicting that something will happen and believing this is a good thing. Nationalism is a dangerous path, particular when the international order and international norms will be in flux as a result and in the concluding section I discuss how a period of AI Nationalism might transition to one of global cooperation where AI is treated as a global public good.


Progress in machine learning

The last few years have seen developments in machine learning research and commercialisation that have been pretty astounding. As just a few examples:

  • Image recognition starts to achieve human-level accuracy at complex tasks, for example skin cancer classification.

  • Big steps forward in applying neural networks to machine translation at Baidu, Google, Microsoft etc. Microsoft’s system achieving human-parity on Mandarin-English translation of news stories (when compared with non-expert translators).

  • In March 2016, DeepMind developed AlphaGo--the first computer program to defeat a world champion at Go. This is significant given that machine learning researchers have been trying to develop a system that could defeat a professional player for decades. AlphaGo was trained on 30 million moves played by human experts.

  • 18 months later, DeepMind released AlphaZero. Unlike AlphaGo, AlphaZero did not use any moves from human experts to train. Instead, it learned solely by playing against itself. AlphaZero was not only able to defeat its predecessor AlphaGo, but in what is known as ‘transfer learning’ it was also able to defeat best-in-class chess and shogi computers. Leading ML researchers I have spoken with have consistently remarked on the ‘uncanny’ significance of a simpler algorithm that used zero human data ending up being more competent and exhibiting more transferable intelligence. There is a huge gulf between the achievement of AlphaZero and Artificial General Intelligence, but nonetheless there is a sense that this could be another small step in that direction.

Beyond research, there has been incredible progress in applying machine learning to large markets, from search engines (Baidu) to ad targeting (Facebook) to warehouse automation (Amazon) to many new areas like self-driving cars, drug discovery, cybersecurity and robotics. CB Insights provides a good overview of all the markets that start-ups are applying machine learning to today.

This rapid pace of change has caused leading AI practitioners to think seriously about its impact on society. Even at Google, the quintessential applied machine learning company of my lifetime, leadership seems to be shifting away from a techno-utopian stance and is starting to publicly acknowledge the attendant risks in accelerated machine learning research and commercialisation:

“How will they affect employment across different sectors? How can we understand what they are doing under the hood? What about measures of fairness? How might they manipulate people? Are they safe?” - Sergey Brin, April 2018


Three forms of instability

So why does this matter to nation states? There are 3 main ways in which accelerating progress in machine learning could create instability in the international order:

  1. Commercial applications of machine learning will create vast new businesses and destroy millions of jobs. In the extreme case, the country that invests the most effectively may end up the strongest economically.

  2. Machine learning will enable new modes of warfare - both sophisticated cyber offense and defense capabilities but also various forms of autonomous and semiautonomous weaponry for example Lockheed Martin’s Long Range Anti-Ship Missile. In the most extreme case, the country that invests the earliest and most aggressively may end up in a position of military supremacy.

  3. Eventually more general purpose AI will enable a fundamental speedup in science and technology research. In my opinion, this might actually be the most profound source of instability. Consider for example the state whose leadership in AI enables them to be the first to develop a viable fusion reactor for power generation. Again, in the extreme case this might enable a country to achieve Wakandan technological supremacy.

Machine learning, to use Jack Clark’s term, is a uniquely omni-use technology that could impact almost every area of national policy. Human intelligence has shaped everything we see around us, so our ability to build machines with greater and greater intelligence could eventually have the same impact. Despite that, we can find some historical parallels to help us think through how things might unfold. Nuclear technology is a dual-use technology with both civilian and military uses (nuclear weapons, radiography, power generation) as is oil (the use of which expanded from lighting to heating, to an incredibly broad range of industrial and military uses). Both of these technologies have had enormous influence on geopolitics and relatively rapidly governments became primary actors and remain so today (consider America’s 6,800 nuclear warheads or the 695 million barrels of oil in the Strategic Petroleum Reserve).

Ambitious governments have already started to see machine learning as the core differentiating technology of the twenty first century and a race has already commenced. This race will come to bear some similarity to nuclear arms race of the last century and the geopolitical tensions and alliances between nation states and multinational companies over oil. Economic, military and technological supremacy have always been extremely powerful motivators for countries.


Industry mix, labour cost, demographics, domestic champions

While the broad threats and rewards of forward-thinking AI policy are common across states, the impact of machine learning is going to vary substantially by country:

Firstly, each country has a different mix of dominant industries and automation is not affecting all industries at the same pace. Compare for example the manufacturing and construction sectors. The construction sector has only recently started to be transformed by digital technologies like Building Information Modeling, whereas manufacturing has seen substantial applications of robotics and automation. That is clear when you look at their comparative productivity gains since 1995:

manufacturing vs construction.png

The impact on wages and jobs will be felt very differently by countries whose core industries are automated sooner. Consider for example Germany, where the automotive industry represents over 10% of GDP; they are going to be more affected by the dynamics around self-driving cars than, for example, the UK where the automotive industry contributes 4% of GDP.

Secondly, every country has a different labour cost that machines will compete against. I have seen this most clearly with a cleaning robotics company called Avidbots (full disclosure: I am an investor). The start-up is headquartered in Waterloo, Canada and produces industrial robots that use computer vision to clean large commercial spaces at a lower price than human cleaning teams in most developed countries. They are seeing orders for their robots from all over the world; however, growth is fastest in Australia due to higher labour costs in the cleaning sector there.

This chart captures well how the economic consequences of automation may vary by country:

wage against the machine.png

If the OECD’s analysis is directionally correct then Slovakia will face a greater challenge in the near term than Norway, with twice as many jobs at risk of automation.

Thirdly, as Kai-Fu Lee articulated very eloquently in his recent New York Times article, only America and China currently are headquarters for the largest AI companies - Google, Apple, Amazon, Facebook, Baidu, Tencent, and Alibaba. National industrial strategy is very different when you are the home of these companies vs. just a customer state. I discuss this in more detail in a later section on the role of national champions.

Finally, in a time when AI is going to materially impact the labour market, different countries have very different attitudes to redistribution, and this will significantly affect how they approach sharing the value created by automation. It is worth noting that while both China and America are home to the leading AI companies, they also both have levels of income inequality at or near their historic peaks.


Blurring line between public & private sectors

This is complicated by the fact that there are incredibly powerful non-state actors who are also competing furiously to develop this technology. All of the 7 most important technology companies in the world--Google, Apple, Amazon, Facebook, Alibaba, Tencent, Baidu--are making huge investments in AI, from low level frameworks and silicon to consumer products.  It goes without saying that their expertise in machine learning leads any state actor at the moment.

As the applications of machine learning grow, the interactions between these companies and different nation states will grow in complexity. Consider for example road transportation, where we are gradually moving towards on demand, autonomous cars. This will increasingly blur the line between publicly funded mass transportation (e.g. a bus) and private transport (a shared Uber). If this leads to a new natural monopoly in road transportation should it be managed by the state (e.g. the call in London for “Khan’s Cars”) or by a British company, or by a multinational company like Uber?

As Mariana Mazzucato outlined in her fantastic book The Entrepreneurial State, states have historically played a crucial role in underwriting long term, high risk research in science and technology by funding either academic research or the military. These technologies are often then commercialised by private companies. With the rise of visionary and wealthy technology companies like Google we are seeing more high risk long term research being funded by the private sector. DeepMind is a prime example of this. This creates tension when the interests of a private company like Google and a state are not aligned. An example of this is the recent interactions between Google and the Pentagon where over 4000 Google employees protested against Google’s participation in “warfare technologies” and as a result Google decided to not renew its contract with the Pentagon. This is a rapidly evolving topic. Only a week earlier Sergey Brin had said that “he understood the controversy and had discussed the matter extensively with Mr. Page and Mr. Pichai. However, he said he thought that it was better for peace if the world’s militaries were intertwined with international organizations like Google rather than working solely with nationalistic defense contractors”.


AI with Chinese Characteristics

In developing a national strategy for AI, China is way out ahead of everyone else. Call it ‘AI with Chinese Characteristics.’ For China over the past couple decades, protectionism has been a winning strategy in developing enduring domestic technology companies, and it has ultimately enabled China to be the only other country in the world with AI companies to rival America’s. Beyond this, China’s technology companies are far more coupled to national policy than in the UK or US, with talk of the Chinese government taking equity ownership in them via 1% ‘special management shares’.

Some notable aspects of China’s early efforts in AI nationalism:

  • China has an explicit goal developed at the highest level of government to make itself the global leader in AI by the year 2030. As Jeff Ding notes, China viewed themselves as behind the US in AI policy and this was a major effort to catch up.
  • China has committed to a $2 billion AI technology park in Beijing.
  • China has developed the ‘Big Fund’ (Credit Suisse estimates total investment at ~$140 billion) to grow the Chinese semiconductor industry. Semiconductor performance is a key driver behind progress in machine learning research and applications
  • The state appears to be explicitly focusing their domestic champions on key fields, for example Tencent in computer vision for medical imaging and Baidu for autonomous driving.
  • The Chinese state appears to have recognised the importance of data to its AI nationalism efforts. China’s latest cybersecurity law mandates that data being exported out of China have to be reviewed.
  • China is implementing specific incentives for key foreign AI talent to relocate to China

The effects of this are starting to be felt. Andrew Moore, Dean of Computer Science at Carnegie Mellon, has estimated that the percentage of papers submitted from China to big AI conferences has increased from 5% a decade ago to 50% today (discussed eight minutes into this interview). This assumes that China is openly publishing all its research. Quantity is obviously not the same as quality and for now researchers based in North America and Europe remain the most influential (for example see Google Scholar ranking by citation). It seems reasonable to assume that this gap will start to close.

Beyond research, Chinese AI startups accounted for an astonishing 48% of global AI funding to startups last year, up from 11% in 2016.

Arguably the weakest link in China’s AI strategy at present is in semiconductors, hence the centrality of that to both the Big Fund and China 2030 and the tension between the US and China in this area, e.g. the US blocking the $117 billion takeover of Qualcomm. China’s annual imports of semiconductor-related products are now $260 billion and have recently risen above spending on oil.

The following graphics illustrate the gaps that China is trying to close in semiconductors and how much smaller the Chinese companies are than the US, Taiwanese or South Korean market leaders. This would also suggest that Taiwan and the Korean peninsula will become an even more geopolitically fraught area for US and Chinese foreign policy.   

China semiconductors 1.png
China semiconductors 2.png



Key events in the arms race so far

While China has the most developed public position on AI Nationalism, there is a clear and growing competition between major countries to lead the world in AI. When referring to an arms race I am primarily using this term figuratively to describe a competitive dynamic between actors where the value they are creating is partly a function of their relative strength over a competitor. There is also a smaller component of this that is a literal arms race, where states are focused on autonomous and semi-autonomous weapons and machine learning enabled capabilities for cyberattack and defense. Here are the key events so far as I see them.


  • China launches the National Integrated Circuit Industry Investment Fund (aka the ’Big Fund’ with 138 billion yuan ($21.9 billion) to boost fledgling semiconductor industry.


  • Obama White House releases report on future of artificial intelligence. Report is widely read and discussed in China.
  • US gov spend of $1.2 billion on unclassified AI-related R&D.
  • AlphaGo as a ‘Sputnik Moment’ for China and AI. Sixty million people watch AlphaGo vs. Lee Sedol live. For Westerners who don’t understand the historical significance and popularity of Go in China, consider AlphaGo’s victory as analogous to a scenario where Tencent developed a team of humanoid robots that could play American football and then went on to defeat the New England Patriots at the Super Bowl. Given Go’s deep history as a vehicle for military strategy, the PLA also takes note. Workshops like “A Summary of the Workshop on the Game between AlphaGo and Lee Sedol and the Intelligentization of Military Command and Decision-Making” start to be held.
  • Partly in response to AlphaGo, South Korea announces investment of $863 million in AI research over the following 5 years.
  • Germany fails to prevent €4.5 billion Chinese takeover of industrial robotics manufacturer Kuka.


  • AlphaGo defeated world No.1 Kie Jie 3-0 in Wuzhen, China. Live video coverage of AlphaGo vs. Ke Jie was blocked in China.
  • China announces deeply ambitious plan to become the world leader in AI by 2030.
  • Pentagon publicly raises concerns around technology transfer from US to China in various AI related areas.
  • Increasing use of CFIUS (Committee on Foreign Investment in the United States) to block acquisitions and investments in US technology companies from Chinese companies or investors. Not limited to US companies--for example, CFIUS also used to block Chinese takeover of Aixtron (German chip equipment maker used in US weapons systems).

2018 so far:

  • January: France announces that foreign takeovers of AI companies will be subject to government approval.
  • March: France announces its AI plan - plan to invest €1.5 billion over 4 years. Meaningful vision for France’s role laid out by Cédric Villani. Trump uses CFIUS to block Qualcomm takeover.
  • April: The UK announces its AI plan to invest £600 million over the coming years (exact annual spend unclear). The EU Commission announces desire to invest €20 billion into AI by 2020. US considers using International Emergency Economic Powers Act to move beyond the blocking of Chinese investment and acquisitions to potentially blocking business partnerships between American and Chinese companies.
  • May: South Korea expands 2016 AI plan to $2.2 billion including 6 new AI institutes, a $1 billion fund for AI semiconductors and an overarching goal to reach the “global [AI] top four by 2022”.


AI Nationalism policies

It is helpful to consider the various fundamental actions a state can take in trying to advance its interests in AI. I am listing these roughly in order of how commonly taken these actions have been by governments over the past decade:

  • Invest money in research or academic institutions focused on machine learning.
  • Help to set standards/regulations so that the technology develops in a way that is most aligned/beneficial to the state’s domestic concerns and companies.
  • Indirectly invest money in the sector by subsidising venture capital.
  • Directly invest money in key companies.
  • Have the state become a key customer for your domestic champions e.g. the relationship between SenseTime and Chinese local and national government.
  • Block acquisitions of your domestic AI companies by foreign companies to preserve their independence.
  • Block investment into your domestic AI companies by foreign investors.
  • Block partnerships between your domestic AI companies and foreign companies.
  • Nationalise key domestic AI companies.

My personal belief is that we will see a lot more activity at the bottom of the list over the next few years. In particular, political leaders will start to question whether acquisitions of key AI startups should be blocked or perhaps even reversed. The canonical example for me is Google and DeepMind, which I will discuss more towards the end of this essay.


Domestic champions

Domestic champions are companies that are global commercial leaders in AI but are also headquartered in a specific country, for example Baidu and China or Google and the US. It is worth discussing domestic champions in more detail:

tax rates.png


This presents issues for the US and China and even bigger issues for other countries when it comes to redistributing the gains from automation and reducing inequality. If these companies continue to take a larger and larger share of the global economy the delta between tax revenues for China or America and everyone else becomes a bigger and bigger issue for politicians.

Kai-Fu Lee, formerly of Google China and now a leading venture capitalist in Beijing presents a bleak view on how this plays out for countries that are not the US or China,

"[I]f most countries will not be able to tax ultra-profitable A.I. companies to subsidize their workers, what options will they have? I foresee only one: Unless they wish to plunge their people into poverty, they will be forced to negotiate with whichever country supplies most of their A.I. software — China or the United States — to essentially become that country’s economic dependent, taking in welfare subsidies in exchange for letting the “parent” nation’s A.I. companies continue to profit from the dependent country’s users. Such economic arrangements would reshape today’s geopolitical alliances."

This kind of dependency would be tantamount to a new kind of colonialism.

We can see small examples of new geopolitical relationships emerging. In March, Zimbabwe’s government signed a strategic cooperation framework agreement with a Guangzhou-based startup, CloudWalk Technology for a large-scale facial recognition program where Zimbabwe will export a database of their citizens’ faces to China, allowing CloudWalk to improve their underlying algorithms with more data and Zimbabwe to get access to CloudWalk’s computer vision technology. This is part of the much broader Belt and Road initiative of the Chinese Government.

There are historical parallels in all of this with the development of the oil industry. As Daniel Yergin explains in his masterful history of oil:

“two contradictory, even schizophrenic, strands of public policy towards the major oil companies have appeared and reappeared in the United States. On occasion, Washington would champion the companies and their expansion in order to promote America’s political and economic interests, protect its strategic objectives, and enhance the nation’s well-being. At other times, these same companies were subjected to populist assaults against “big oil” for their allegedly greedy, monopolistic ways and indeed for being arrogant and secretive”.

My prediction is that domestic antitrust action against Google and Amazon will not materialise, because for now Washington will care more about strengthening its hand against China. The notes Mark Zuckerberg prepared for his Senate hearing capture this pithily:

“Break up FB? US tech companies key asset for America, break up strengthens Chinese companies.”


What can countries that aren’t China or America do?

To answer that question we need to consider the resources that are important to a country in the race to develop a leading position in AI:

  • Compute. The compute resources associated with machine learning progress are increasing rapidly. Consider for example this Open AI analysis. While compute costs run into the hundreds of millions for the leading machine learning corporations, this is still small compared to government budgets, so in theory smaller states like Germany, Singapore, the UK or Canada can compete head to head with the US and China.
  • Deeply specific talent. At present, progress in machine learning is very sensitive to a talent pool that is microscopically small compared to the world’s population. There are perhaps 700 people in the world who can contribute to the leading edge of AI research, perhaps 70,000 who can understand their work and participate actively in commercialising it and 7 billion people who will be impacted by it. There are parallels with nuclear weapons, where the pool of scientists like Fermi, Szilard, Segre, Hahn, Frisch, Heisenberg capable of designing an atomic bomb was incredibly small compared to the consequences of their work. This suggests that specific talent could be a huge determiner in any AI arms race. China certainly thinks so. In this regard, some smaller countries--notably the UK and Canada--punch massively above their weight.
  • General STEM talent. The alternative is that you don’t need a Fermi or an Oppenheimer, you just need a lot of competent engineers, mathematicians and physicists. If so, the balance tips in favour of the largest most-developed countries, with the US and China squarely at the forefront.  
  • Adjacent technologies. I have restricted this discussion to machine learning, but it is worth noting that there are various technologies that could contribute to progress in machine learning. For example if quantum computing enables a breakthrough in computing power, this would further accelerate progress in machine learning. A state’s ability to win an AI arms race will be partly enabled by a broader set of technology investments in particular software and semiconductors.
  • Political environment - clearly any state action around AI will consume a portion of the leaderships political capital and will trade off against other key issues consuming the country. If a country’s political leadership is absorbed by dealing with another form of instability - for example climate change or Brexit then it will be harder for them to focus attention on AI.


The strange case of the UK

My interest in this topic partly stems from my concern that the UK government is not getting its  AI strategy right.

The UK finds itself in a fortunate position of having DeepMind--arguably the most important AI lab on the planet--headquartered in London. DeepMind has the magical combination of visionary, exceptional leadership in Demis Hassabis, Shane Legg and Mustafa Suleyman as well as the greatest density of AI research talent in the world. If humanity builds Artificial General Intelligence, many of the deepest thinkers on the topic believe that it will happen in Kings Cross. If you were looking for a domestic champion for the UK, you would be hard pressed to find a better candidate.

However, DeepMind is no longer an independent British company. It was acquired by Google in 2014 for £400 million at a critical inflection point: after their success with Atari DQN, but before the big AlphaGo/AlphaZero breakthroughs. It was a brilliant acquisition. In general, it appears that Google has been an excellent parent company for DeepMind, providing substantial resources to increase both the compute spend and the talent base (reported by Quartz as $160 million in 2016) as well as being able to tap into Google’s existing talent in machine learning--for example the Google Brain team. For a pre-revenue startup, remaining independent would have required DeepMind to raise close to half a billion dollars between 2014 and now to execute a similar plan. Today, in the middle of an bull market for AI startups, that seems reasonable, but looking back at 2014--before SoftBank’s Vision Fund and the escalation in huge growth rounds for pre-revenue companies--it would have been a tall order. Ultimately, DeepMind probably chose the highest impact and ambition path available to them in 2014 by selling to Google. I have always had enormous respect for Google and the principled and visionary leadership there is likely a very good fit with the DeepMind culture.

However I find it hard to believe that the UK would not be better off were DeepMind still an independent company. How much would Google sell DeepMind for today? $5 billion? $10 billion? $50 billion? It’s hard to imagine Google selling DeepMind to Amazon, or Tencent or Facebook at almost any price. With hindsight, would it have been better for the UK government to block this acquisition and help keep it independent? Even now, is there a case to be made for the UK to reverse this acquisition and buy DeepMind out of Google and reinstate it as some kind of independent entity?

The two main political parties in the UK both struggle with this kind of question for different reasons. The Conservative MPs I have spoken to about this topic will always cite the troubled history of British Leyland; that spectre of failed market interference still looms large over their thinking. They remain convinced that the only path is laissez-faire economics.

The Labour party has a different challenge. They assert the importance of state action, for example Jeremy Corbyn’s desire to nationalise railways, water and energy companies. But this thinking focuses on those historic battles over privatisation and doesn’t look to the future. Corbyn and McDonnell today are more interested in Great Western Rail than DeepMind.

All of this is further complicated by the fact that the government is hugely distracted by Brexit.

DeepMind is not the only example of an exceptional British company working on cutting edge machine learning. The UK has made many fundamental contributions to the field of machine learning and is home to some of the world’s very best universities for machine learning research including Cambridge, Edinburgh, Imperial, Oxford and UCL. With the growth of the UK’s startup sector over the past decade, there are now many great teams working to combine the UK’s expertise in building great technology companies like Arm, and its academic talent in machine learning.  Prowler is applying reinforcement learning to the general field of decision making. Graphcore is building a new type of processor for machine learning. Ocado is arguably the most sophisticated global player in warehouse automation after Amazon. DarkTrace is one of the leading companies applying machine learning to cybersecurity. Benevolent is doing pioneering work in applying machine learning to drug discovery. All these companies are growing incredibly quickly, doing transformational work in their fields and building deep talent pools. They are all still independent startups. What will the UK government do when Amazon, Google or Tencent make them a multi-billion dollar offer? At present, nothing. This is a good thing if you’re Google, Amazon or Alibaba looking to further cement your position and indirectly a good thing for the US or China. Is it a good thing for the average UK citizen?


Rogue actors

Most of this essay has focused on the national interests of countries. There are other non-state political actors who also have to be considered - for example terrorist cells or rogue states. This is most relevant when it comes to machine-learning-enabled cyberattacks and autonomous weaponry. For those interested to learn more about these risks, they were covered well in this report on malicious uses of AI. The key question for me is the extent to which key labs, corporations or nation states ‘go dark’ in terms of publishing AI research to avoid enabling malicious actors. The risk is well captured by Allan Friedman in Cybersecurity and Cyberwar:

“To make a historic comparison, building Stuxnet the first time may have required an advanced team that was the cyber equivalent to the Manhattan Project. But once it was used, it was like the Americans didn’t just drop this new kind of bomb on Hiroshima, but also kindly dropped leaflets with the design plan so anyone else could also build it, with no nuclear reactor required… the proliferation of cyber weapons happens at Internet speed”

This is also complicated by the fact that cyber attacks may not be as easily identified:

“The problem is that, unlike in the Cold War, there is no simple bipolar arrangement, since, as we saw, the weapons are proliferating far more widely. Even more, there are no cyber equivalents to the clear and obvious tracing mechanism of a missile’s smoky exhaust plume heading your way, since the attacks can be networked, globalized, and of course, hidden. Nuclear explosions also present their own, rather irrefutable evidence that atomic weapons have been used, while a successful covert cyber operation could remain undetected for months or years”

The most likely outcome here is that certain key machine learning research ceases to be shared in the public domain to avoid enabling malicious actors. This thinking is captured most clearly in OpenAI’s recent charter:

“We are committed to providing public goods that help society navigate the path to AGI. Today this includes publishing most of our AI research, but we expect that safety and security concerns will reduce our traditional publishing in the future, while increasing the importance of sharing safety, policy, and standards research.“

If we do see key research labs or countries ‘go dark’ on some of their research output, a Cold War dynamic could emerge that will reward the most established and largest state or corporate actors. Ultimately, this reinforces the AI Nationalism dynamic.


The great wall of money

So far the amount invested by states is an order of magnitudes lower than that of Google, Alibaba etc. McKinsey estimates that the largest technology multinationals spent $20-30 billion on AI in 2016.

I believe that the current government spending on AI is tiny compared to the investment we will see as they come to realise what is at stake. What if rather than spending ~£500 million of public money on AI over a number of years the UK spent something closer to its annual defence budget of £45 billion?

Consider again the parallel with nuclear weapons, where the US government went from ignoring key scientists like Leo Szilard to recognising the existential importance of nuclear weapons to initiating the Manhattan Project. The Manhattan Project went from employing zero people in 1941 to within 3 years spending $25 billion (in 2016 dollars), employing over 100,000 people and building industrial capacity as large as the entire US automobile industry. States have tremendous inertia, but once they move they can have incredible momentum.

If this happens, then the amount of investment in AI research and commercialisation could be 10-100X what it is today. It is not always the case that more funding enables more progress but nonetheless I think it is prudent to assume that if states substantially increase their investment in machine learning then progress is likely to speed up further. This only reinforces the importance of investing now in research that helps to mitigate risks and ensure that these developments go well for humanity.


Engineers without borders

It is also worth acknowledging that there are connections that transcend the state and nationalism as Jeff Ding notes in his excellent report “Deciphering China’s AI Dream”:

“It is important to consider the interdependent, positive-sum aspects of various AI drivers….Cross-border AI investments, with respect to the U.S. and China, have significantly increased in the past few years. From 2016 to 2017, China-backed equity deals to U.S. startups rose from 19 to 31 and U.S.-backed equity deals to Chinese startups quadrupled from 5 to 20. Moreover, what is often forgotten is the fact that both Tencent and Alibaba are multinational, public companies that are owned in significant portions by international stakeholders (Naspers has a 33.3% stake in Tencent and Yahoo has a 15 percent stake in Alibaba).”

It is also true that economies and fundamental science and technology progress do not neatly track state borders. Talent and capital are global: DeepMind’s initial investors were from Silicon Valley and Hong Kong, their team is extremely international and they now have offices in Canada and France. There is a weakness to viewing things too narrowly through a state-centric lense. However, I believe that overall the economic and military consequences of machine learning will be such a dramatic cause of instability that nation states will be forced to put their citizens ahead of broader goals around internationalism.

Up until now I have just tried to outline what I think will happen. Machine learning becomes a huge differentiator between states--economically, militarily and technologically--and triggers an arms race, which causes progress in AI to speed up faster.

However there is a difference between predicting that something will happen and believing this is a good thing. Nationalism is a dangerous path, particular when the international order and international norms will be in flux as a result. George Orwell writing on nationalism in 1945 captures the tension between a patriotism that is primarily defensive, and a nationalism that seeks to dominate:

“Nationalism is not to be confused with patriotism. Both words are normally used in so vague a way that any definition is liable to be challenged, but one must draw a distinction between them, since two different and even opposing ideas are involved. By ‘patriotism’ I mean devotion to a particular place and a particular way of life, which one believes to be the best in the world but has no wish to force on other people. Patriotism is of its nature defensive, both militarily and culturally. Nationalism, on the other hand, is inseparable from the desire for power. The abiding purpose of every nationalist is to secure more power and more prestige, not for himself but for the nation or other unit in which he has chosen to sink his own individuality.”

Personally, I believe that AI should become a global public good--like GPS, HTTP, TCP/IP, or the English language--and the best long term structure for bringing this to fruition is a non-profit, global organisation with governance mechanics that reflect the interests of all countries and people. The best shorthand I have for this is some kind of cross between Wikipedia, and The UN. One organisation that has made a step in this direction is OpenAI, which operates as a non-profit entity focused on AI research. This doesn’t solve many of the economic issues around machine learning that I have discussed in this essay, but it is a great improvement on machine learning research being primarily the economic domain of large technology companies and the military domain of nation states.

While the idea of AI as a public good provides me personally with a true north, I think it is naive to hope we can make a giant leap there today, given the vested interests and misaligned incentives of nation states, for-profit technology companies and the weakness of international institutions. I believe that we are likely to go through a period of AI Nationalism before we get to a place where AI is treated like a public good, and that, to use Orwell’s distinction, a kind of AI Patriotism is likely to be a good thing for smaller countries in the short term.

Taking the example of the UK again, I am in favour of a more expansive national AI strategy to protect the UK’s economic, military and technological interests and to give the UK a credible seat at the table when global issues around AI are being worked out. That will help ensure that the UK’s economic interests and values are considered. I believe that the stronger the position of smaller countries like the UK, Canada, Singapore or South Korea in the short term, the more likely we are to move in the longer term to AI as a global public good. For that reason I believe it is necessary for the UK government to take steps towards investing in and protecting its homegrown AI companies and institutions to allow them to play a larger role on the world stage independent of America and China. I have lived in both America and China, and during that time developed enormous respect and affection for both of those countries. That does not prevent me from believing the UK should protect the economic interests of its citizens and I would like to see the UK play a material role in shaping the future of AI. Once again I come back to DeepMind - I believe that the UK and the world would be in a better place were DeepMind to be an independent entity. Ideally, in the longer term as a non-profit, international organisation focused on AI as a global public good.

During the coming phase of AI Nationalism that this essay predicts, I believe we need a simultaneous investment in organisations and technologies that can counterbalance this trend and drive an international rather than national agenda. Something analogous to The Baruch Plan led by organisations like DeepMind and OpenAI. I plan to write more about that soon.


My favourite books, films, music of 2017

For the 6th year running I give you some arbitrary rankings of some media products from the last year.

1. The KLF: Chaos, Magic and the Band who Burned a Million Pounds
2. Reinventing Organisations
3. Dharma Bums
4. Let My People Go Surfing
5. From Third World to First: The Singapore Story 1965-2000
6. The Piano Teacher
7. All Out War
8.  Dark Money
9. The Institutional Revolution
10. Life 3.0 

Films (released in UK in 2017)
1. Mountains May Depart
2. American Honey
3. Toni Erdmann
4. Lady Bird
5. Moonlight
6. Get Out
7. 20th Century Women
8. I, Daniel Blake
9. I Am Not Your Negro
10. Elle

Music (for albums released in 2017 and standout track)
1. Kendrick (Pride)
2. Travis Scott (Butterfly Effect) 
4. E-40 (Choices)
4. Cardi B (Bodak Yellow)
5. Stormzy (Mr Skeng)
6. Vince Staples (Big Fish)
7. Future (Mask Off)
8. YFN Lucci (Everyday we Lit)
9. Jeremih (Oui)
10. 21 Savage (Bank Account)


My favourite books, films, music and meals of 2016

For the 5th year running I give you some arbitrary rankings of some media products from 2016.

New non-media categories added this year!

1. JR
2. Gilead
3. All the Presidents Men
4. 1984
5. Under the Volcano
6. The Adventures of Augie Marsh
7. The Recognitions
8. Submission
9. When Breath Becomes Air
10. Jerry Moffatt Revelations

Films (released in UK in 2016)
1. 45 years
2. Dheepan
3. A Bigger Splash
4. Kajacki
5. Wiener
6. Magic Mike XXL
7. Heart of a Dog
8. Leviathan
9. Carol
10. Eye in the Sky

Music (for albums released in 2016 and standout track)
1. Anohni (Watch Me)
2. Skepta (Konnichiwa)
3. Kanye (Real Friends)
4. Ariana Grande (Be Alright)
5. dvsn (With Me)
6. Beyoncé (All Night)
7. Rich Chigga (Dat $tick)
8. YG (Still Brazy)
9. ATCQ (We the People)
10. Lil Yachty & Dram (Broccoli)

1. Mugaritz in San Sebastián w stag ladz
2. Delhi food walk in Old Delhi w M
3. Bar Nestor in San Sebastián w stag ladz
4. channa masala at Amma’s Ashram w Laura & M
5. Roscioli in Roma w Jeff & Alex
6. mirchi bada at Shahi Samosa in Jodhpur w M
7. golgappa at Kashi Chat Bhandar in Varanasi w M
8. spicy fish bhaji & mixed samosa at roadside coffee shop in Oman w M
9. slow cooked pork carnitas w orange Chez Hog
10. Paolo’s Italian BBQ veg in Somerset w P&K crew

Top 10 active outdoor lyfe:
1. sending Lucky Luka in Kalymnos
2. climbing at Hadash
3. night surfing in Ekas
4. hiking round Telendos
5. walking over the Jean Claude & Christo golden piers installation at Lake Iseo
6. sending Marie Rose in Fontainebleau
7. climbing in Yangshuo
8. running London 10k
9. deer hunting in N Florida
10. Napali coast hike

New Year, new roles

As we start the New Year, I want to share some personal and professional news. I will be moving to a Chairman role and Matt Jones will be CEO. We’ve always known the co-CEO structure was temporary and best suited to the the first stage of our merger - but why make this change now? There are a number of reasons.

The co-CEO approach has been a helpful structure for Matt and me to combine our visions, strategies, products, organisations and cultures - everything that we have had to marry to make this merger a success. We’re now 7 months into the merger, and have created some great momentum, so can revert to a more orthodox leadership structure.

I have also been working towards this transition for very personal reasons. I’ve always tried to use this blog as a place to be real about some of the challenges of building a start-up and should share the other reason why this is the right time. A few years ago my younger sister was diagnosed with a brain tumour. The prognosis was not great, but there was room to hope. At the start of 2015 my sister’s prognosis abruptly shifted to terminal and she died weeks later. I’m a very private person when it comes to my family, so this isn’t easy to share in public, but reading essays by Paul Bucheit on the death of his brother really helped me this year, so I’m trying to be more open. This transition is also about me taking time to properly grieve after spending the last year head down and focused on making a success of our merger.

During 2015 we achieved some great things. We raised two rounds of financing, hired some great new people, launched new products, and saw growth we are all excited by. I’m also really proud of the progress we’ve made against one of the less tangible goals of the merger - to combine the DNA of CrowdSurge - a deep understanding of the needs of artists surrounding ticketing - with the DNA of Songkick - building scalable consumer products for fans. We didn’t expect to see the results of that within the first year, but when Adele’s team approached us to help them counteract what they expected to be unprecedented levels of scalping around their upcoming tour, we launched a new product that drew on all the strengths of the merged company. In the words of one industry commentator “Songkick has done more in one campaign to address the issue of touting than has been achieved to date by any other party in any other sector…the prospects are tantalising and, for once, both the artist and the fan seem to have won”. I’m really looking forward to us scaling this product up with more artists in 2016.

We also, like all start-ups, have challenges ahead and felt that the next phase of execution would benefit from a more orthodox and battle-hardened leadership structure.

Without a doubt, Matt Jones is the best CEO for the next chapter. Matt’s been a long-time friend and collaborator of mine, and our shared vision and mutual respect were big factors in wanting to merge our companies. He’s one of the most impressive people I’ve ever met, with a combination of insane levels of energy, infectious ambition, and downright relentlessness. If you spend a few minutes in his company you’ll see that he cares at the deepest possible level about the future of the concert industry, and has a single-minded determination to use technology to make it better.

His leadership is a huge asset to Songkick, and it’s been a big factor in our ability to continue to hire exceptional team members, raise funding from world class investors and earn the trust of artists like Adele, Metallica and Mumford & Sons. Matt’s vision has always been that an artist should be able to sell tickets wherever their fans are engaged, and that vision of distributed commerce is central to our plans for 2016. But as well as being a visionary, Matt’s also deeply pragmatic - a rare combination in our industry - and a leader dedicated to getting shit done.

I’m grateful to our board and executive team in supporting Matt and me with this transition, and in particular to my co-founder Michelle and my new co-founders Adam and Matt for their support throughout this year.

2016’s a huge year for Songkick, and now the merger’s fully complete we have the right team, technology and structure to make it a success. I’m more convinced than ever that we can have a transformative impact on artists, fans and the wider industry, and I’m excited to continue building towards this in the year ahead as Chairman.

My favourite books, films and music of 2015

Same format as last year, and the years before.

Books (most published before 2015):

1. The Grapes of Wrath (Steinbeck)
2. Moby Dick (Melville)
3. A History of Western Philosophy (Russell)
4. East of Eden (Steinbeck)
5. Any Human Heart (Boyd)
6. Between the World and Me (Coates)
7. H is for Hawk (Macdonald)
8. Stoner (Williams)
9. Barbarian Days: a Surfing Life (Finnegan)
10. The Entrepreneurial State (Mazzucato)

Other good things I read: Strangers Drowning (MacFarquhar), Speak, Memory (Nabakov), USA Trilogy (Dos Passos), Purity (Franzen), Antifragile (Taleb), Post Capitalism (Mason)

Films (released in the UK in 2015)

1. Starred Up
2. Whiplash
3. Still Alice
4. Birdman
5. Margin Call
6. Mommy
7. The Duke of Burgundy
8. Citizenfour
9. Girlhood
10. Creed

Artists (for music released in 2015 and standout track)

1. Kendrick Lamar (Alright)
2. Skepta (Shutdown)
3. Kanye (Only One)
4. Vince Staples (Jump off the Roof)
5. Hot Chip (Huarache Nights)
6. Dej Loaf (Back Up)
7. Nicki Minaj (Feeling Myself)
8. Rae Sremmurd (Throw Sum Mo)
9. D’Angelo (The Charade)
10. Grimes (California)

Full Stack Music: 1 Trillion Streams, 200 Million Tickets

(repost of guest blog post I wrote for TechCrunch)

At present, there are three distinct music industries: radio, on-demand music, and concert ticketing. However, we are starting to enter a new phase, where these industries will converge and produce one integrated experience for artists and fans. I’ve taken to calling this full stack music, because at heart it speaks to a holistic experience that integrates these industries through data.

The integration of these three, previously distinct industries will produce a richer experience for artists and fans, unlock a ton of additional subscription, ticketing and advertising revenue for artists and create a better experience for fans. It will resolve the central tension between fans, artists and technology companies that so much ink has been spilled about.

Three Distinct Music Industries

There are three main businesses of music:


Radio is where music discovery happens, and where the majority of casual music fans engage with music. Ninety-two percent of the U.S. population listens to radio at least once per week; on average, they listen for 15 hours. It is critical to artists because a radio station decides which track a fan listens to next, and so radio has an incredible ability to drive new artist discovery. Radio is primarily monetized via advertising, generating $45 billion/year. It also is the primary channel for marketing concerts.

On-Demand Music 

This is when the listener decides exactly which song comes next (unlike radio). It started with vinyl, migrated to CDs, migrated to iTunes and finally has migrated to on-demand streaming services like Apple Music, SoundCloud, Spotify and YouTube. Monetization used to be in the form of direct spend (buying a CD); it is now a mix of advertising and subscription.

Concert Ticketing

Paying to see your favorite artist live. This used to be a side business for the music industry. However, over the past 10 years this has expanded to become the main event, growing from $10 billion in 1999 to $30 billion in 2015 in gross ticket sales. It is where artists make the majority of their income — typically 70-80 percent. Most of the growth has come from increasing ticket prices — 50 percent of concert tickets go unsold and attendance has not increased anything like as fast as prices.

These industries have been loosely coupled in the past. Going back to 1999, the record company would use radio as a way to get fans to discover a new act, then monetize that investment, primarily via selling “on-demand” access in the form of CDs and, finally, drive additional discovery by subsidizing touring (known as “tour support;” a label would underwrite some of the cost of touring to help build an audience to whom to sell CDs). Touring represented a small percentage of artist income.

The industries were also coupled at a corporate level at one point, with ClearChannel. Over the course of many years, a massive roll-up of local U.S. radio stations resulted in ClearChannel. That rolled-up business exists today as iHeartRadio, with 850 local stations and 245 million monthly unique listeners. In parallel, a roll-up of local concert promoters produced a new touring behemoth, SFX Entertainment, and the two businesses — radio and concert promotion — were merged in 2000 to form a new conglomerate. The goal was to combine the No. 1 channel for concert discovery (radio) with the No. 1 promoter of concerts (SFX).

Eventually, these businesses were separated into Clear Channel Communications (iHeartRadio) and Clear Channel Entertainment (LiveNation). Subsequent to that, LiveNation embarked on a huge project of vertical and horizontal integration and, at this point, is the world’s largest artist manager (Maverick/ArtistNation), the world’s largest primary ticketing company (TicketMaster), the world’s second-largest secondary ticketing company (TicketMaster+) and the world’s largest festival owner, venue owner and concert promoter.

Internet Music: Radio And On-Demand Converge

We have seen a massive transformation of the recorded music landscape — with the growth of iTunes/Apple Music, Deezer, Pandora, SoundCloud, Spotify and YouTube — to the point where more than 1 TRILLION tracks are now streamed online across these services each year. The line between radio and on-demand is rapidly blurring across each of these services:

  • SoundCloud and YouTube both autoselect another track to play when the one you searched on-demand finishes — that is, a radio experience.
  • Pandora, historically a pure radio service, has started to enable whole album streams on-demand.
  • Spotify has shifted from a pure on-demand model to offer a radio experience very similar to Pandora, where you can select an artist and listen to a radio mix based on that cue. Every Monday now, Spotify will provide you with a personalized radio stream of songs you might enjoy, based on your listening history.
  • Finally, Apple Music has taken this integrated approach the furthest, with a live on-air radio experience seamlessly integrated with a library based on demand experience.

At the same time, discovery of local concerts has started to transform — rather than generic emails about all the tickets on sale in Los Angeles, new services like Bandsintown and Songkick (which I co-founded) will send you personalized alerts whenever the artists you listen to on these music streaming services announce a local show. These concert discovery apps now reach more than 20 million fans each month, and are more personalized and convenient way to find out about concerts.

The Next Phase: Streaming And Ticketing Converge

Leading artists have started to articulate the extent to which streaming music and ticketing are becoming joined at the hip. For example, Ed Sheeran:

“I’m playing three Wembley Stadium (shows) on album two. I’m playing sold-out arena gigs in South America, Korea, south-east Asia and Australia. I don’t think I’d be able to do that without Spotify or if people hadn’t streamed my music. My music has been streamed 860million times, which means that it’s getting out to people. I get a percentage of my record sales, but it’s not a large percentage, (whereas) I get all my ticket sales, so I’d much rather tour. That’s why I got into the business — I love playing gigs. Recording albums, to me, is a means to an end. I put out records so I can tour. For me, Spotify is not even a necessary evil. It helps me do what I want to do.”

Over the next few years we will see this connection between streaming and ticket sales become completely explicit. Streaming services will increasingly make it seamless for fans using their services to see when the artist has a local show; Songkick’s existing API partnerships with Deezer, SoundCloud, Spotify and YouTube are hints at what this could look like. It’s not impossible to imagine a time when you could possibly buy tickets directly from your favorite artist right inside your streaming service.

When that happens, artists will finally be able to see a connected picture of how their music is distributed and monetized. An act who gets 100 million streams will see that 10 million of those were monetized via paying subscribers, 90 million by ads and another 5 million fans via ticket purchases. The outcome will be a more seamless experience that results in casualmusic fans attending more concerts.

This is a big deal — only 20 percent of Americans attended a concert in the last year, and the biggest reason for not going is that they didn’t know when shows for their favorite act were happening. This will finally create firm alignment between artists and music streaming services — to the point where all acts will see the explicit and causal relationship between an ad-supported online radio stream and a paid ticket purchase, as Ed Sheeran does.

This is just the start, though. Along with joined up analytics for artists, fans will be offered new propositions that tie together live and recorded music experiences. For example, imagine if all streaming music subscribers were offered lower booking fees on ticket purchases — creating another reason for fans to subscribe rather than use an ad-supported service, and driving faster growth in subscription income for artists.

After the show, the set list will immediately be available on your streaming service of choice, further helping to reinforce the connection you have built to that artist and increasing the likelihood of buying merchandise from the gig. Finally, tour routing will be impacted by the data from streaming services similar to Spotify’s recent experiment with Hunter Hayes:

“Hayes turned to Spotify to help him route the tour. The online music streamer crunched its numbers and determined the college markets where the country star is the strongest. Hayes’ biggest fans in the target markets will receive pre-sale access. His top 21 fans in each market will receive such prizes as early entry, meet-and-greets, signed memorabilia and other goodies. The fan who streams Hayes’ music the most in each market will be awarded a one-year sub to Spotify Premium.”

The key point across all of this is that the central, most valuable asset of streaming musicservices will be the listener data they generate. As we shift from offline radio to online streaming, artists will know how those 1 trillion tracks of music were streamed — which fan listened to them, where they were based, which concert tickets they purchased in the past — and be able to tailor personalized and richer experiences to their fans.

That is an incredible shift compared to the data-poor ecosystem of 1999. The trend will only continue, as more and more offline listening (in particular, terrestrial radio) migrates to online streaming. Once that is fully complete, we will hit 5-10 trillion streams, and these shifts will be even more critical. Zoe Keating, one of the most visionary artists I have the pleasure of knowing, has been saying this for a few years now:

“I want my data and in 2012 I see absolutely no reason why I shouldn’t own it. It seems like everyone has it, and exploits it…everyone but the creators providing the content that services are built on. I wish I could make this demand: stream my music, but in exchange give me my listener data. But the law doesn’t give me that power. The law only demands I be paid in money, which at this point in my career is not as valuable as information. I’d rather be paid in data….The new model says that in the future I’m not supposed to sell music: I’m supposed to sell concert tickets and t-shirts. Ok fine, so put me in touch with the people who will buy concert tickets and t-shirts.”

There are signs that this integration is coming — Pandora appointed the former CEO of AEG Live, the world’s second-largest promoter, to their board, and have started to experiment with concert marketing — for example, campaigns to promote tours for the Rolling Stones and Odesza. Global Radio, the largest terrestrial radio company in the U.K., has expanded into artist management and concert promotion, again hiring key execs from AEG Live. AppleMusic is broadcasting on Beats 1 all the shows from their upcoming festival, and is encouraging the artists they book to share information about their performances on Connect.

We are in the early stages. Eventually we will know not just how many streams are generated per artist, but how many ticket sales resulted. If this deeper integration of streaming and ticketing results in one ticket sold per 5,000 streams, then we’d know that 1 trillion streamsgenerated 200 million tickets — at an average face value of $50, this would be $10 billion in ticket gross — equivalent to the revenue from 100 million subscribers paying $8/month. It would also have the consequence of making Apple, SoundCloud, Spotify, Pandora or YouTube new power players in ticketing.

Artists will start to focus on promoting the service, which in aggregate generates the most ad, subscription and ticket revenue, which in turn will drive further growth in online listening. We’re about to watch the next big shift in online music play out as we move from three separate music industries to Full Stack Music.

My favourite books, films and artists of 2014

Same format as last year.

Books (most published before 2014):
1. Anna Karenina (Tolstoy)
2. My Struggle books 1 & 2 (Knausgard)
3. Herzog (Bellow)
4. Island (Huxley)
5. Ham on Rye (Bukowski)
6. Post Office (Bukowski)
7. Superintelligence (Bostrom)
8. The Diversity of Life (Wilson)
9. White Girls (Als)
10. Steve Jobs (Isaacson)

Musical artists (new music released in 2014) & standout track:
1. Beyonce (7/11)
2. YG (Bicken Back Bein Bool)
3. Lil Wayne (Rich as Fuck)
4. Makonnen (Tuesday)
5. Future Islands (Seasons)
6. Vince Staples (Limos)
7. Young Thug (Lifestyle)
8. Ariana Grande (Bang Bang)
9. Sleaford Mods (Tiswas)
10. Bobby Schmurda (Hot N***a)

Films (UK release in 2014):
1. All is Lost (Chandor)
2. Le Weekend (Michell)
3. Boyhood (Linklater)
4. Bullhead (Roskam)
5. Under the Skin (Glazer)
6. A Touch of Sin (Zhang Ke)
7. Nightcrawler (Gilroy)
8. Interstellar (Nolan)
9. Blue Ruin (Saulnier)
10. Fury (Ayer)

(haven’t yet seen Ida, ‘71, Leviathan, We are the Best or American Sniper - suspect they will shake the list up)

Pitchfork and Songkick

We just launched a new partnership with Pitchfork to integrate listings from Songkick  into Pitchfork’s core review pages. I’m out in California working round the clock as usual but even if only for myself I wanted to take a minute out of the waves of meetings and emails to remind myself what this means to me.

I have been reading Pitchfork since I was a teenager. I have found more music that I have fallen in love with through Pitchfork than through any other online service. They have made my life as a fan an order of magnitude richer. Ever since we started Songkick in 2007 it has been a dream to work with them. We have some amazing API partners - Bandcamp, HypeMachine, SoundCloud, Spotify, YouTube - they are all examples of companies I find inspiring, but still there’s just such a rush from finally getting this partnership live.

After years of being a founder Pitchfork now stands for something different than it did when I was a teenager or when we were getting started. It stands for building something that endures and gets better every year. Pitchfork has been getting better EVERY YEAR SINCE 1995. Outside of VICE it’s hard to think of a brand and team that has shown as much commitment to online media. How many great online music services have died in just the years while Songkick has been active? iLike, Imeem, MySpace, Lala - this shit is hard. Let alone when you consider everything that has happened since Pitchfork was founded. And yet they endure, improve and prosper.

Michelle, the whole team and I are really proud they chose us as a partner and can’t wait to do more together.

My notes on Superintelligence by Bostrom

On the plane to the US I finished reading Nick Bostrom’s Superintelligence. I jotted down notes as I went and thought a few friends might be interested so posting here.

Bostom’s background spans philosophy (he is a professor at Oxford), computational neuroscience and physics - his breadth of knowledge makes this a broad reaching read. It’s particularly interesting if you have a basic understanding of machine learning and want to understand some of the philosophical and ethical questions raised by superintelligent machines.

A few things that stood out for me:

- various surveys of AI experts (who are plausibly at the optimistic end of the spectrum :) ) peg the likelihood that we will see machines with human level intelligence by 2040 at 50%, and 90% by 2075

- Bostrom convincingly argues that once human level machine intelligence emerges we may rapidly see an ‘intelligence explosion’ where the intelligent machines self-enhance their own software/intelligence at high speed. This leads to machines that are superintelligent. Since software can be copied the population of superintelligent machines can grow rapidly.

- He then argues that given the kinetics of such an explosion one entity may end up rapidly accelerating past other machine intelligence projects and forming a dominant position. This echoes the writing of Lanier and others on the increasing centralisation of power within the technology industry. He makes a particularly interesting point that digital agents may tend to greater centralisation of control due to reduced inter-agent transaction costs. For example the idea that firms or nations of machines could massively increase in size.

- the majority of the book focuses on what happens after a superintelligence emerges. He draws an interesting distinction between having more intelligence and more wisdom - and the risks of one developing without the other. He gives a hilarious example, worthy of Foster-Wallace where a superintelligent machine is tasked with producing 1000 paperclips. The machine, being superintelligent and supercapable rapidly produces 1000 paperclips. However, being a perfect Bayesian agent it is also aware that observational error may mean that it has actually produced fewer paperclips than this - there is a tiny but real chance it has only produced 999. So to remedy this it commandeers all the resources in the known universe to more accurately count whether it has actually produced 1000 paperclips or not. He lays out various types of superintelligence and various ways that things could go badly wrong for humanity from goal functions that on first glance seem to be bounded, but per the paperclip example are not. At lot of this seems to be the difference between programatic logic and 'common sense’ and the complexity in creating a bridge from one to the other.

- he draws an interesting parallel between the fate of humans in a world with superintelligent machines, and the fate of horses in a human world. The horse population grew massively through the 1900s as a complement to carriages and ploughs, but then declined with the arrival of automobiles & tractors. The population of horses was 26m in the US in 1915 but declined to 2m by the early 1950s. The flipside of this is that the horse population subsequently returned to 10m driven by economic growth that have allowed more humans to indulge in leisure activities involving horses. 

- He explores how superintelligent machines might acquire their values. This section on value loading techniques is very interesting and summarises some of the most interesting mathematical and philosophical challenges facing the AI space. For example in one unfinished solution to the value loading problem we have a subset of intelligent machines that are known to have values that are safe for humans. These machines are allowed to develop a incrementally more intelligent machine - where the step in intelligence between the first group of machines and the mutation is small enough that the earlier machines can still test the new, slightly smarter machine to see if its values remain compatible with humanity’s safety. He makes the terrifying point that if there is an arms race going on for one company or nation to develop superintelligent machines first, this kind of caution is unlikely to be on the path of the 'winning’ project - 'move fast and break things’ seems like a bad motto when you are playing with something this powerful.

- Having framed the challenges of loading a superintelligence with values, he then moves to what values we want this superintelligent to have. Bostrom argues that humanity may have made relatively little progress on answering key moral questions and is likely still labouring under some grave moral misconceptions. Given that are we in a position to specify a moral framework for a superintelligent machine? He introduces the concepts of Indirect Normativity and coherent extrapolated volition in response to this - a hedge against our own limited moral framework and a bet that the machine can do better:

“Our coherent extrapolated volition is our wish if we knew more, thought faster, were more the people we wished we were, had grown up farther together; where the extrapolation converges rather than diverges, where our wishes cohere rather than interfere; extrapolated as we wish that extrapolated, interpreted as we wish that interpreted” - Yudakowski

finally he asks how to ensure that the immense economic windfall resulting from superintelligence should be distributed to benefit all of humanity, not just a narrow set of people (or machines).

Overall I found it very stimulating and would recommend.

Save me money, save me time, or save me both

Consumer technology can find a mass market when it saves us time, or money. Every person has their own preference for how they spend time and money. While some people would prefer to save time by spending more money (for example paying to avoid a queue at an airport) others prefer to save money by spending their time (for example going down to the venue to buy a concert ticket to save money on booking fees). Sometimes technology can save us both - for example how at launch Amazon saved both time and money when purchasing books.

If you plot this out on a chart it seems that start-ups can successfully enter a market in one of three quadrants - saving time, saving money, saving both. The logos correspond to my understanding of the proposition that the initial users of these services were compelled by:


So for example Priceline offered a cheaper airline ticket if you were willing to be more flexible with your time. Uber offered SF residents a way to spend more money but save time by waiting for a cab. In both cases users traded time for money or vice versa. On the axes, Groupon saved their first users money without requiring extra time, and Google saved their users time by getting them to information faster, but without costing more.

In the ticketing space, the launch of TicketMaster (over 30 years ago now) offered a step change in convenience by saving you the time it took to go down to the venue, providing you were willing to pay a new service charge. Continuing this trend, Stubhub offered the ability to skip the onsale process entirely and buy from the resale market at a higher price later. Most of the concert goers I’ve spoken to who prefer to buy tickets from Stubhub are clear that they are deliberately spending more money to save time.

It would seem that to get initial growth a start-up needs to save users time, money or both. (That is unless you can solve some next level Mazlow need and get us all self-actualised a la Snapchat :-)

What surprised me is how these value propositions can shift over time as network effects develop. I’ve been thinking about this a lot recently as Uber has grown. When it first launched I saw it as somewhat similar to Stubhub - a service for affluent consumers to save themselves time in exchange for more money. What’s been fascinating to watch is how over time as more drivers and riders join the network it has become a multi-tiered service that can save both time and money with the introduction of UberX, greater taxi utilisation, and soon, ride-sharing. So over time you see:


The big takeaway for me is that a start-ups initial time/money proposition isn’t always a predictor of its eventual market impact. A service that started with “We just wanted to push a button and get a ride…And we wanted to get a classy ride. We wanted to be baller in San Francisco. That’s all it was about.” might end up reducing transportation costs and saving time for the mass market.

A Person Got to Have a Code (and a phone)

“A man gotta have a code” - Omar

The average person checks their phone 150 times/day. Setting aside whether that’s a ridiculous number or not, that got me thinking about the fact that we look at one image - our lock screen image - 150 times/day. 

There are probably quite a few interesting product opportunities that fall out from an image we look at 150 times/day or 55k/year. Other than a tattoo I can’t think of an image we looked at in the past as frequently or in as many contexts.

The specific thing I’ve been noodling on is how to use that image to reinforce behaviour. I have a few principles** I try to keep in mind as I go through life, but it can be easy to lose sight of them in the swirl of daily emotion. I’ve been experimenting with putting that list on my lock screen so I end up looking at it in passing 100+ times/day.

I’ve been doing it for a month or so now and have noticed that for the most part I’ll just tune out the image of the list en route to completing a task on my phone. But from time to time it does pull me in (often when I’m just getting out my phone to kill time). I’ll re-read them and perhaps something is reinforced or questioned.

I wonder what else could be done with an image we look at 150 times/day? 

** 1. YOLO, 2. FOMO, 3. BRB, 4. what would ‘ye do, 5. what would bill murray do

My talk from YC startup school

Hi I’m Ian Hogarth and I’m one of the co-founders of Songkick along with Michelle You and Pete Smith.


We started Songkick back in 2007 and were part of the summer ‘07 YC batch.


Songkick is the easiest way to find out when your favourite artists come to town and get tickets. If you’ve ever experienced the frustration of finding out that your favourite band was playing the day after the show then we’re for you. We’re the second most used concert service in the world after TicketMaster with about 10m unique fans/mo. We’re backed by Index and Sequoia. Given that the average artist makes 70% of their income from concerts we hope we will make a big difference to artists as well as fans.

One thing that comes from building the same company for 7 years is you get to watch waves of start-ups succeed and fail around you and your intuition about start-ups gets rewired. It’s kind of like rewatching the first series of 24 after seeing the final episode of season 8.


I remember being terrified by a competitor to Songkick that launched while we were only just getting started. They rapidly grew to millions of users. However over the next few years the foundation they’d built their growth on moved underneath them and they disappeared. That resets your sense of what to be scared by.

Similarly you will see startups that seem to have it all figured out, and when they become the talk of the town, it doesn’t surprise you. What may surprise you is what exponential growth looks like. That start-up that was doing well and just a bit better than you goes from 1m users to 10m in a year. And you’re still like ok we have a big year coming. But then you see them go from 10m to 100m the next year. And that resets your sense of how things can grow.

To put this in perspective, if I go back to that summer of 2007 only a few of the 22 start-ups in our YC batch are still in existence. I believe most of the others ended up being shut down or acquired in relatively small deals. Watching that play out really teaches you how hard it is. I remember being intimidated by everyone in our batch when we got to YC. So many people who were better technically, better product thinkers, and more experienced at building web products than Pete, Michelle and me.


One of the other start-ups in our batch that is still around is Disqus. If I massively oversimplify for a second, it’s plausible that Songkick and Disqus may end up being worth 100X more than start-ups in our batch that were sold early, so there may be something to be learned from what was different about our markets and our path.


But the more radical example is that other start-up that endured from our batch, Dropbox. Which is likely worth 100X more than us at this point.


What’s even more remarkable is that Drew and Arash remain two of the most humble and down to earth founders I know.


There is plausibly someone in this room who will go on to create something 10X bigger than Dropbox.


So hopefully you’ll take this as a bit of a disclaimer for all the advice that follows - if you really want to know the mysteries of the startup universe go talk to Drew and Arash! Also I am most interested in consumer products, so most of this talk applies to them.

So having appropriately caveated that I have at least 100X less insight to share about what goes into building the next Google than most other speakers who have spoken at startup school, I thought about what I would most like to have had someone explain to me in retrospect.


Firstly, on online music as an excellent way to take a beat down. Here are some exceptionally talented founders/builders who have, to a greater and lesser extent taken a beat down by doing a music start-up:


Dalton Caldwell now a YC partner and the former CEO of Imeem; Sean Parker; Geoff Ralston the creator of what became Yahoo Mail & former head of product of Yahoo; Ali Partovi founder of LinkExchange & iLike, Dave Goldberg CEO of SurveyMonkey and Launch Media, David Pakman Venrock partner & former CEO of eMusic - all drawn to the flame! I thought I could put a useful talk together summarising what I’ve learned from talking with some of these great people who have built music start-ups. I then realised that Dalton already did that talk.

Dalton’s YC talk is an excellent primer on the music industry so I’m not going to rehash his advice here. What I will offer you is a slightly more reductive view on the art and entertainment industry - film, TV, music, visual art - if you’re thinking about building a company in one of those domains here is what I think you need to understand:


1. everyone thinks they are more into music/film/art than their actual consumption reflects. it’s like that study you may have read about around the sub-prime crisis where most homeowners felt that average house prices would decline during the next 6 months, but also that their home would stay the same or increase in value

2. actually most people are into a much narrower set of things than they realise. And so a small number of creators drive the majority of activity and revenue for these industries

3. 99.99999% of artists struggle their whole lives without financial success, so when they break out they are often willing to trade future rights to their music or touring or merchandise in exchange for financial stability. Having artist friends who are still sleeping on friends’ couches after 10 years this isn’t selling out, it’s about getting stable and finally paying off your debts. Unlike tech where you can be a talented engineer at a start-up that fails & still go get a great job at Google, if you’re a great musician that fails to make it you can’t just go join Radiohead

4. this transference of rights from artists->middlemen leads to a small number of companies controlling a huge amount of the rights that are needed to innovate on behalf of the artist or consumer. In addition as the entertainment industry lurches from one model to another (for example CDs to downloads to streaming) the perceived threat to their survival means regulators allow even more radical consolidation - for example in the last 10 years the world’s largest record label Universal acquired EMI, who combined now represent something like 40% of popular music rights. The same thing happened in the live industry with the merger of the biggest global concert promoter, LiveNation with the biggest ticketing company, TicketMaster. And don’t be fooled into thinking that other parts of the music industry aren’t rights oriented markets, both merchandise and ticketing both have similar rights systems in place comparable to copyright in recorded music

5. that level of rights consolidation means that it’s almost impossible for a start-up to transform the entertainment industry without some permission from one or more powerful content owners, which means waiting till they are ready to embrace you. That in my opinion is one of the differences between Spotify’s success now and the nightmare that Dalton went through. The one caveat is that much much larger technology companies can force things to move faster - for example the way that Google protected YouTube from potential label annihilation and 8 years later owns the largest free streaming music service on the planet - or the way that Apple created a digital download market in one big move

6. So I think the biggest question to ask yourself as someone aiming to build a technology company serving fans and artists in the entertainment industry is why will the labels/promoters/agencies/studios/galleries be ready for this now? If pg said Kill Hollywood, I guess I’d say Grow Hollywood or fail

The broader point here is that the level of supply-side consolidation in your industry massively changes how you build your start-up. If the industry is heavily consolidated you are more likely to have to partner with the supply-side middlemen. Consider for example the work that Stripe did with the banks early on. If the supply side of the industry is more fragmented (e.g. vacation rentals or private hire vehicles) you are probably best off competing directly with the existing incumbents. 

The flip side of this is that it’s incredibly rewarding to work on a product that helps fans & creators in an area of culture you love. I’m proud of what we’ve done so far to improve concert-going and I’m inspired by what Netflix, Spotify and others have done for their respective industries. You just need to make sure that your timing is right for the middlemen, as well as fans/artists.

The second thing I want to share today is the importance of understanding the start-up game before you try and play it.


Start-ups aren’t a straightforward path to financial success. Particularly in consumer start-ups the level of randomness means that as someone who is good at building things you’re more likely to make money from joining a great company as it starts to take off (the first first 100 employees @ FB probably made more money than the founders of most successful start-ups).

So I think the reason to found a company is actually a less financially oriented one - you are really motivated to try and solve a particular problem and the satisfaction of building something to solve that problem is enough to balance out 5-10 years of high stress and a good likelihood of failure.

So if I haven’t deterred you yet, then here are the rules of the game as I see them. Hopefully internalising these challenges early on will help you be more successful. There are 3 engines that determine a start-up’s success:


A gratification engine, a growth engine and an economic engine. This insight came to us via the legend Sean Ellis about 3 years into Songkick’s life.


If I define a new variable ‘Unicornness™’, your level of unicorness will be roughly:

Unicorness = gratification^growth^revenue

You become full unicorn aka Airbnb, Dropbox, Google if you get all 3 right. Every engine that fails to start will reduce your unicornness an order of magnitude.


Let’s take the gratification engine, expressed in much less nerdy terms by YC as “make something people want”. In Songkick’s case figuring this out was a pretty brutal experience. We launched Songkick to solve the problem of knowing when your favourite artists were coming to town. Our first release combined a few different scrapers of ticket sites that generated an incomplete dataset of concerts in the US and UK and a mac plugin for iTunes that you had to download and install that would scan your itunes library. It was a pretty crappy first time use.


The return on the 5 minutes of your life that it took to sign up and install the plug-in was a list of upcoming concerts, personalised to your music taste. Some people were willing to do this and they liked it. But the amount of friction involved was too high for most regular fans. And Songkick ends up being most powerful for regular fans who at present go to ~1 show / year, but after getting Songkick might end up going to 4 or 5.

For a long time we felt that the reason that more people didn’t use our product was that it didn’t do enough and we added a massive array of additional features that resulted in very little additional usage.

The turning point came when my co-founder Michelle, inspired by Sean Ellis, started running surveys that really dug deep into why the users who loved our product loved it. And why the users who were kind of ‘meh’ found it lacking. The bottom line was that our simple idea of personalised listings and not missing another great gig was actually a gratifying enough experience for all types of music fans - people found shows they wouldn’t have gone to and had life changing experiences! but too few users were getting to it. We needed to make it radically easier for you to give us your music taste (which became possible with new APIs on mobile devices) and we needed to have better underlying data. That was a really big lesson for me. Engineers and start-up people cherish the idea of 80:20, or the idea of an MVP. But once you find something that works, the key is to do the 20:80, the grindingly incremental work that adds the final 20% of the value, but takes 80% of the time.


For us this has ended up being around data - getting more and more high quality, timely, more comprehensive concert data so we became the trusted authority for a fan. Your gratification engine will have many levels of refinement that compound on each other - onboarding flows, core experience, messaging etc and you should probably never stop trying to increase it.


The next engine is the growth engine - how new users discover your product. The first big point here is that your growth engine has no real chance of starting without a great product. I’ll talk more about that in a minute, but I think there are 4 main ways to drive substantial growth in consumer products:


These aren’t mutually exclusive. For example Yelp has a killer mobile app so they grow through both world of mouth and SEO. Airbnb also has strong WOM which means they can amplify a referral program with paid acquisition. They’ve also grown through M&A, PR & more creative growth hacking e.g. the alleged craigslist thing. So many different growth channels can combine effectively.

For us there were a number of different drivers of growth. The first was realising that there was no canonical page on the internet for a tour or concert - similar to what Yelp does for restaurants or IMDB for films. And when you build enough value to be the canonical page for something you see lots of different sources of growth, from social referrals to API/platform opportunities to SEO. That core insight lead to a flurry of things that caused us to grow from the BD partnerships we did with YouTube, Spotify, SoundCloud and others to our artist facing products. The second big growth factor came from mobile and WOM - in my opinion, mobile app stores reward a gratifying product more than any distribution platform in history and so much of the growth we saw there came just from making the product easier to use so more people would recommend it to friends. So we’ve benefited from 3 of these channels.


Finally the economic engine, how you make money from your users. I can’t say as much about this because it’s still a work in progress and one reason that we’re not (yet!) in the pantheon of unicorns. Initially we bootstrapped revenue by setting up affiliate partnerships with ticket vendors who pay us when we generate a ticket sale - similar to the model for Kayak or TripAdvisor. That has taken us to millions of dollars in revenue on gross ticket sales of over $100m. However it is unlikely to take us hundreds of millions in revenue. Our goal is to enable fans to buy tickets in the Songkick app as well as getting linked out to 3rd party sites. Per my earlier discussion on music start-ups this depends on being able to scalably access inventory in partnership with artists, promoters & venues, which is still a work in progress. It’s exciting though - in London you can now buy tickets to a huge number of gigs through our app (over 25% of all shows in London & growing fast), and we’re rolling out other geographies soon.


Finally there is the team you build and retain to solve all these problems. This is the Songkick team en route to a festival when our bus broke down!

Each of these are dependent on each other. If I had to express it mathematically it would be something like:


Firstly, gratification is a function of your team, your economic engine and your growth engine.

That’s because you need a great team to build a great product. And a powerful economic engine can be a big part of your gratified experience (e.g. saving users money). Finally in many instances a consumer product gets better with more users (e.g. a marketplace or social network), so you may also need growth to deliver a gratifying user experience.

So the gratification engine depends on the other two engines and your team.

Secondly growth is a function of your gratification engine, your economic engine and your team.

That’s because great execution on growth requires hiring great growth people, so growth is dependent on team. If you have a revenue engine you are also able to pay to acquire users and access a powerful source of growth. One key point to make here is that it requires more expertise to grow through free channels than by spending money on paid channels - compare the number of start-ups that figure out how to grow virally on Facebook with the number of start-ups who figure out how to buy FB ads. Most importantly without a great gratification engine you won’t get the most powerful and fundamental growth driver, word of mouth. As yet another example of this interdependence - our partnership with YouTube didn’t start via VC connections, or epic biz dev. It started from a product manager at YouTube reaching out because he loved our product as a regular user.

So the growth engine depends on the other two engines and your team.

You can make similar arguments for the interdependence of the team you can hire and retain and your economic engine on everything else.


unicornness = product*revenue*growth

growth = f(product, revenue, team)

team = f(product, revenue, growth)

product = f(team, revenue, growth)

revenue = f(growth, product, team)

AKA everything is connected and you’re watching the first season of True Detective.


I’ve laboured this point because it seems like the most important thing to understand about start-ups is that it’s all connected and you need to get all of these key pieces working in concert to build an exceptional business. The earlier you figure out the whole system, the earlier you get on the path to becoming the next Dropbox.


Finally, all of this takes time and is very hard and you can’t give up. So some thoughts on resilience and how to develop it.

Firstly - it does usually get better if you keep going. I remember the bleakest point in Songkick’s life was around december 2010. Nothing felt like it was working. We went into Christmas after a pretty brutal board meeting with a plan for some things we’d try in the new year.


When things get hard I go back to our growth graph since then and look at that same spot.


It usually does get better if you keep moving and trying new things. As pg says be “relentlessly resourceful”. As the founder of a great company told me once - survival can be a growth strategy. The best thing about surviving is that you get to see new platform shifts for example the shift to mobile that Songkick has grown through. Everyone likes to talk about how new start-ups get built when new platforms emerge. But things that are already working can suddenly work a lot better. For example Shazam and Pandora are two companies that were 8 and 7 years old at the time of the iPhone launch and had been great, but not total breakouts. The iPhone played a big role in changing that. I remember hearing from the Pandora team that the iPhone launch doubled growth for them overnight.

Platform shifts expand the set of start-up visions that can finally be fully realised. So let that be another reason to push through the hard times.

I would recommend trying to articulate why you believe you are doing important work. I think a good way to do that is to keep asking why until you get to the root. We wrote those down a few years back and here’s what we came up with:


Then when you are low you have something to remind you why you’re going to work through whatever todays flavour of crisis is. 

When you’re having a bad week, spend some time with your users. The happy ones will remind you of why you started. And the unhappy, disengaged ones should help you transform an abstract sense of impending doom into a practical feeling of something to fix. Our product team ended up knocking through a wall in one of our meeting rooms and creating a makeshift user research lab. That helps to set a regular tempo for having our whole team watch our users use your product as individuals, not in an aggregate Google Analyticsy way.


Start your company with people you can count on when shit is going sideways. I think it’s pretty hard to know that about someone without a real foundation of friendship so I thoroughly endorse YC’s thing about building on top of a long standing and trusted relationship. I have been very fortunate to have two amazing co-founders in Michelle and Pete and an amazing team, many of whom have been with us from very early on. I can’t imagine how I would have weathered some of the tougher moments without them.


So in summary:

- if you’re going to do a start-up in the entertainment industry or any industry where the supply side is highly consolidated, you’ll probably need to work with the existing middlemen. So start trying to understand how you can help them in addition to fans & creators.

- consumer start-up success seems to depend on getting 3 key fundamentals right: gratification; growth; economics. Understand how you’re going to do that as early as possible.

- even once you find something that people want, there will still be days when it feels hopeless. I’ve suggested a few ways to nurture your resilience when that happens. If you keep moving, you’ll find a way through!


What should Benedict Evans' business model be?


I started sketching out this post before Benedict joined a16z, so excuse the fact that his ‘business model’ is now resolved for a little while :). For 'Benedict Evans’ feel free to substitute any smart, public intellectual unaffiliated to an existing organisation with a wealth of insight to provide.

When Benedict and his blog emerged on the start-up scene, it was an interesting event: he had over a decade of experience in mobile & media and was rapidly building an audience of the most influential people in tech. The most entertaining feature to me was his weekly summary of how many more readers he’d acquired that week. I reached out to him when his blog was just getting started and we had a good conversation over breakfast. I really admire him and watched him build his audience with fascination. The question that kept coming back to me was - what should his business model be?

The value proposition as I see it is: world class insight into a particular domain (in his case, mostly the evolution of mobile/media) which he was providing for free. Normally such insights are clearly part of a broader free+paid model - Fred Wilson was eloquent and explicit about this when describing his blog: “It is the model behind this blog in fact. You get the content for free. Anything else, you have to pay for with equity in your company”. 

The ways to get paid that I could see at the time:
- put some of the content behind a paywall (e.g. similar to Ben Bajarin at Techpinions or Om Malik) ie charge a subset of the public for incremental insight
- get paid offline (e.g. Eric Ries’ book, events etc)
- build a product around his community (e.g. Sean Ellis & growthhackers that monetises in another way)
- get paid in cash/equity as an advisor to various start-ups, VCs, established businesses ie charge individual businesses for company specific insight
- get paid in carry as part of a VC firm

I have to say I was hoping that somehow this might represent the unbundling of VC and that Benedict would build a standalone business around advice/insight. But I suspect that what he’s doing now will probably be the best move he could possibly have made. It is interesting to consider what % of a16z he has vs the other partners/contributors. It would be a great data point on the value of insight.

Building start-ups from first principles


image credit

In my opinion one of the most helpful things YC has done for the start-up ecosystem is to apply a reductive mindset to start-up strategy: Make something people want. There is so much wisdom contained in that one sentence.

The thing it reminds me of most is studying mechanics at university. The way I was taught mechanics, your goal is to take a small set of first principles (mainly Newton’s laws of motion) and learn how to rigorously apply them to understand the motion of physical objects. The point in a classical mechanics course where you apply these simple laws and end up with a model of how a gyroscope works is breathtaking.

I watched Elon Musk speak at the Dublin Web Summit, and one of the most helpful points he made was that innovation is the product of reasoning from first principles (good summary here). Given his current set of companies he was mainly referring to the first principles of physics/chemistry, but I think there are some similar first principles for building start-ups that must be discovered and applied. YC’s motto feels like one of them. Like Newton’s laws of motion, the hard part isn’t reading the sentence once, the hard part is learning how to apply it to understand a gyroscope. Paul Graham’s essays on start-ups and their early customers often feel like the course notes that explain how to apply that first principle.

One of the hard things about being a start-up CEO is simultaneously building a product and a business from first principles whilst recognising the need to market your business through narrative and analogy. The classic example here is the “X of Y” pitch that drives most investor pitches. Narrative and storytelling is an incredibly important component of marketing and quickly explaining what you do. But if you want to build a great product & business, and you try to do that by analogy, you will fail. So the art is to learn how to use first principles when you are building, and analogy when you are selling.

Start-up inspiration at every age

Age 19 Matt Mullengweg co-founded WordPress
Age 20 John Collison co-founded Stripe
Age 21 Sophia Amoruso co-founded Nasty Gal
Age 22 Joe Lonsdale co-founded Palantir
Age 23 Daniel Ek co-founded Spotify
Age 24 Michelle Zatlyn co-founded Cloudflare
Age 25 Larry Page co-founded Google
Age 26 Julia Hartz co-founded Eventbrite
Age 27 Ben Silberman co-founded Pinterest
Age 28 Andrew Mason co-founded Groupon
Age 29 Bryan Johnston co-founded Braintree
Age 30 Jeff Bezos founded Amazon
Age 31 Perry Chen co-founded Kickstarter
Age 32 Victoria Ransom co-founded Wildfire
Age 33 Jan Koum co-founded WhatsApp
Age 34 Jessica Livingston co-founded Y Combinator
Age 35 Reid Hoffman co-founded LinkedIn
Age 36 Renaud Laplanche co-founded Lending Club
Age 37 Reid Hastings co-founded Netflix
Age 38 Jimmy Wales co-founded Wikia
Age 39 Martin Lorentzon co-founded Spotify
Age 40 Aneel Bhusri co-founded Workday
Age 41 Paul English co-founded Kayak
Age 42 Robin Chase co-founded Zipcar

…running out of time to Google people’s ages but here’s a start on 43+:

Age 46 Linda Avery co-founded 23andMe
Age 50 Andrew Viterbi co-founded Qualcomm
Age 52 Kenneth Lerer co-founded The Huffington Post
Age 55 Arianna Huffington co-founded The Huffington Post
Age 58 Satoshi Nakamoto (maybe) co-founded Bitcoin
Age 64 David Duffield co-founded Workday

Your age and experience (or lack of) will help you and hinder you in different ways. Ambition & opportunity, at the right time in your life, with a big problem you want to solve, is probably more important.

ps if you can help me fill out names for the blanks post age 43 then please leave in the comments and I’ll add.

pps some interesting empirical data here and here.

The best 'growth hackers' don't talk about 'growth hacking'


Over the course of building Songkick, and seeing it grow to over 9 million fans/month, I’ve had the pleasure to learn from and work with some exceptional thinkers on growth. I’ve learned the most from Dan Rogers who has lead our quantitative growth efforts since 2010, Sean Ellis and Andrew Hunter who was an early advisor to Songkick and Dan’s mentor. I love learning about marketing and have at various points in Songkick’s past tried to make sense of various types of distribution, primarily:

- quantitative growth channels e.g. viral loops, email marketing, widgets, SEO, SEM

- partnership or narrative driven growth channels e.g. BD partnerships, PR

Quantitative growth channels are now what’s termed ‘growth hacking’.

It’s a mix of creativity (finding novel channels to drive growth that competitors have not yet discovered) and technical/analytical skill (scalably exploiting those channels). The lifecycle is:

1. find a channel that works before your competitors (and if the channel is broad enough this may include all consumer apps)

2. exploit that first mover advantage

3. eventually competitors cotton on and you hit the Law of Shitty Clickthroughs, or the channel closes for some other reason (e.g. platform you’re building on decides to compete)

4. get creative & go find another novel channel. Return to step 1.

Another way of looking at this is that new marketing channels have a limited amount of new users they can supply, and there is infinite demand from start-ups who want more users. It’s a zero sum game, where the overall value of winning the game also usually declines over time.

So the best growth hackers shut the fuck up about what’s working & hope that they can keep the channel to themselves for as long as possible. Usually the only way you learn really novel marketing approaches is by spending time in person with a trusted peer, sharing your respective secrets & hoping the novel things you learn from them balance out the information leakage.

This is at odds with what’s going on in start-up land at the moment where there’s now even a forum dedicated to 'growth hacking’. I’m really skeptical you can learn anything there beyond what’s worked in the past (which is no longer relevant) and how to apply a quantitative approach to marketing. I have a theory that the growth hackers forum is actually just an elaborate growth hack by Sean Ellis to market Qualaroo!

So if you’re a start-up looking to grow, get creative, find your unique approach to growth and then keep it to yourself.

The 10 books, films and artists I loved most in 2013

I did this last year and friends seemed to enjoy it, so here’s the 2013 instalment. 

Books (most published before 2013):
1. Ulysses (James Joyce
2. In Search of Lost Time (Marcel Proust)
3. Catch 22 (Joseph Heller)
4. Average is Over (Tyler Cowan) 
5. The Sense of an Ending (Julian Barnes)
6. On Chesil Beach (Ian McEwan)
7. Who Owns the Future (Jaron Lanier)
8. … that was it. Ulysses & Proust took up most of my reading time but wow they were worth it

Musical artists (new music released this year):
1. Kanye West
2. Chance the Rapper
3. Pusha T
4. Kevin Gates
5. Indian Wells
6. The Haxan Cloak
7. Jon Hopkins
8. Tree
9. Arcade Fire
10. Future

Films (released in UK in 2013):
1. Amour
2. Blue Is the Warmest Colour
3. The Great Beauty
4. Zero Dark Thirty
5. A Hijacking
6. Frances Ha
7. Gravity
8. American Hustle
9. World War Z
10. Neighbouring Sounds