At CSC our goal is to identify and support companies that will experience significant increase in demand over the next 10 years (and then scale into the next 50). In support of this goal we have developed the A3 Thesis: Automation, Augmentation and Autonomy, which helps direct our investment decisions even at Seed. The A3 Thesis is deliberately broad and has applications in almost every industry vertical. It is focused on data generation, processing and performance enhancement for the industrial and commercial application of machine learning.
The A3 Thesis represents the large scale direction of technological innovation we believe will occur over the next 10+ years. In understanding how this technology will be adopted and deployed we rely heavily on Carlota Perez’s model: Technological Revolutions and Financial Capital, what Fred Wilson has called USV’s ‘bible’. Since we cannot see a deterministic future we attempt to learn from the past; the Perez model of technological deployment is based on the interplay of technology and capital in the last 5 technological revolutions.
It can be tempting to think current investment opportunities rely heavily on a third piece, namely timing. However, it’s more appropriate to look at the ‘window of opportunity’ as Bruce Gibney, Founding Partner at Founders Fund describes. Arguably Google’s window of opportunity has been open for the last 18 years. We focus more on transformational, thesis driven companies that present robust, long term, sustained value creation versus short term, exploratory and exhaustive applications of current technology.
In The Rise and Fall of American Growth, author Robert Gordon proposes
(in detail) that the technological advances of 1870 to 1970 (what he calls the ‘special century’) cannot be repeated and recent productivity declines will continue unabated. Gordon is not alone in questioning productivity stagnation. Morgan Stanley recently released a report showing U.S. and OECD productivity at or near decade level lows. The White House even referenced this debate in their recent report asking whether this is just the next chapter of automation or whether “AI will affect the economy differently than past waves of automation”.
Our distinction between the 3 A’s is an important one. Automation refers to labor replacement from machines for non-complex tasks traditionally completed by a human; it is low level value add. In the technological context, augmentation implies enhancement. It may also (and usually does) enable new solutions to be created for the complex problem being executed. Marc Andreessen described in an interview with Tim Berners Lee that with machine learning we’re seeing an “entirely new kind of product that wasn’t possible before”. This is where the value lies and forms the central theme of the A3 Thesis. It is what Joi Ito and the MIT Media Lab calls ‘extended intelligence’ or William Ross Ashby in 1954 called ‘intelligence amplification’; the deliberate combination of human and machine intelligence. Autonomy is still the most distant. It is complete human removal from a complex task while maintaining or (probably) enhancing performance.
Here at CSC we are what Gordon would call ‘techno-optimists’. We believe not only that the opportunities presented by the development and application of machine learning will influence and increase productivity and growth but also do so quicker than before given ready access to information and open data. Associate Professor of CS at Stanford Dr. Fei Fei Li recently remarked that advances in computer vision based on deep learning have the potential to set off a “Cambrian explosion” of innovation, Klaus Schwab, Founder and Executive Chairman of the World Economic Forum has already called machine learning the ‘Fourth Industrial Revolution’ and Head of AI at Baidu and the leader in deep learning applications Andrew Ng calls “AI the new Electricity”.
We believe augmentation (human performance enhancement through machine learning) is ready to influence almost every major industry in the industrialized economy. In 2012 Geoff Hinton’s team built neural nets that beat humans in the ImageNet Competition and DeepMind recently conquered the game Go. The value driver in machine learning applications is enhancingthe completion process of a task, be it time, cost, efficiency etc. We believe deliberate industrial and commercial applications will be key echoed by Satya Nadella and Microsoft who are building AI to solve the “most pressing problems of our society and our economy”.
We now turn to the deployment of the technology and the relationship between technological development and financial capital. As a primer on the Carlota Perez model, technological revolutions have 4 main phases; Irruption, Frenzy, (Crash) , Synergy and Maturity (for more resources on the Perez Model see ).
The Irruption phase (first) and Maturity phase (last) of the former revolution generally are concurrent. In Maturity there is a focus on M&A and oligopoly building and idle capital is searching for new opportunities even abroad (foreign investment from idle capital also occurs in the Frenzy phase, the difference being in maturity it is production seeking in Frenzy it is speculative).
In Irruption “huge successes, incredible rates of growth and even more incredible profit margins become potent magnet for still looming quantities of idle capital”. The difference in our current context (the new exploratory applications of the internet, on-demand etc.) is that the profit margins have not been anywhere near “attractive”. For this to be realized, it requires the installation and deployment of A3, specifically augmentation. Take Uber, once “autonomous” cars are widely deployed the incremental cost of the cars reduces to maintenance (of the hardware and software), improving the profit margin dramatically.
Frenzy brings financial innovations; new financial instruments (the CDS, the ‘private IPO’, secondary markets etc.) making money from money on idle capital and the expectation that all innovations will be equally successful (the exhaustive misappropriation of the technology).
So where are we today? This influx of idle capital and innovation of new financial instruments and increased M&A activity support the idea we are in the Maturity phase of however you characterize the previous 30 years. Marc Andreessen also identified the lack of new technology saying “my thesis is that we’re not in a tech bubble — we’re in a tech bust. Our problem isn’t too much technology or people being too excited about technology [Frenzy]. The problem is we don’t have nearly enough technology [Maturity]”.
We do believe however, that overlapping this we are now in the Installation phase of the machine learning revolution . Deep learning models have been around for nearly 25 years. But the combination of GPU processing power and open access to data from the Internet means the machine learning/deep learning revolution has now begun to provide economic opportunities and just last month, Jen-Hsun Huang called the 2012 ImageNet competition the “Big Bang” of the GPU based deep learning revolution. So if the life of a VC fund is 10 years, what are the opportunities today that will experience an increase in demand (to the point of realizing transfer of value from enterprise or individual)?
There are three main elements we believe in successfully deploying the potential of machine learning:
1. Data Assets
2. Data Utilization
3. Industrial and Commercial Application
NVIDIA, the leader in GPU driven deep learning lists them in a similar way as Training, Datacenters and Devices. These three elements can be also read chronologically: to have a successful application of machine learning (in a specific industry for example) there needs to be effective processing and analysis on appropriately and robustly attribute-tagged data assets. The huge leveragability of machine learning lies in the fact that the same processes can be used to train models for any different industry application. For this we need robust, attribute tagged data assets. We believe this is where the most attractive opportunities lie currently; building the infrastructure of machine learning in the generation, classification, storage and processing of large data assets .
Applications are high beta; either that specific application has product market fit and is adopted or it still needs time to effectively find a willing market. We believe the appropriate time to invest in applications layer of machine learning is Post-Frenzy (the timing of which is extremely difficult to forecast). We are excited by companies that focus on data asset creation.
There are some opportunities that we see that fall outside this thesis. We believe given the right set of conditions these present compelling investment opportunities. Examples of out thematic investments includes those in Virtual Reality, Consumer focused companies, New Media and Marketplaces to name a few.
The A3 Thesis was created to support our investment decisions. It is our probabilistic view on the direction of technological innovation and will therefore evolve over the course of the fund through the discussions with founders. The evolution of the A3 Thesis is not due to a lack of conviction but rather our attempt out optimization, a kind of supervised learning for our own investment model. We are patient capital, prioritizing long term value creation and look forward to learning from everyone involved in creating and contributing to the deployment of the machine learning revolution.
 It’s clear to see how the Perez crash could be created. Schumpeterian ‘creative destruction’ of traditional employment will most likely lead to socio-political unrest and an even increased focus on augmentation opportunities (rightly and wrongly) from speculative capital. A recent report from Oxford University found that nearly 47% of all current employment in the U.S. is threatened by the application of machine learning and Larry Summers recently wrote in the Washington Post that he estimates nearly 1/3 of all men 25–54 in the U.S. could be out of work by mid-century. With the pace of augmentation increasing, the U.S. extending ZIRP driving more speculative, idle capital and productivity stagnating the Perez crash could in fact come sooner than expected.
 Carlota Perez Model primers: AVC post: http://avc.com/2015/02/the-carlota-perez-framework/, AVC post: http://avc.com/2011/10/required-reading-for-the-carlota-perez-interview/, Fred Wilson Carlota Perez Interview: https://www.youtube.com/watch?v=hmesHdCcXn4
 In fact, an argument could be made that the entire electronics revolution has formed the installation phase for the machine learning revolution. It has allowed us to create, store and process actionable data and develop systems to augment human limitations in solving complex problems.
 SoftBank recently announced the launch of a $100bn fund dedicated to technology investment in conjunction with the Saudi Arabian sovereign wealth fund and many public investment firms have been entering the private technology investment asset class.
 An analogy can be made here to the process of extracting commercial value from exploratory oil and gas drilling. The quality of the oilfield is unknown until after the exploratory drilling. If oil is found it is extracted, refined and sold to readily available consumers. In our machine learning context, data is the oilfield. But the major difference here is we have control over how ‘valuable’ that asset can be (how well attributes or features are tagged in the data). In effect we can create our own high quality oilfields. However, we do not have readily available consumers; this comes in the Synergy phase and is the applications layer of the technology. Ironically, there already companies applying machine learning to oilfield exploration.