New Tech produces economic and political disruption at scale. This was the effect of Naval fleets to project power, electricity, the steam engine, and rail.
AI can be the most disruptive tech of the 21st century because it is a general-purpose tech (not limited to say hauling loads). AI also has zero distribution costs (being digital). But the value AI brings to the world is distributed unevenly. Let's consider AI's geopolitics.
Geopolitics is the study of the effects of Earth's geography on politics and international relations. It provides context and improves decision making at the macro (and sometimes not so macro) level. Geopolitics focuses on states and countries, but for the geopolitics of AI, Companies are a better unit than countries. Why? Because in AI we care about talent and data. Both are mobile and malleable (when compared to mountains and rivers) but still have some geo-specificity (German data may not be very useful in North Korea).
According to Kai-Fu lee's 'AI superpowers' book, there are seven AI giants (the equivalent of nation powers): Google, Facebook, Amazon, Microsoft, Ali Baba, Tencent, and Baidu. Beyond those seven, there's a dramatic drop in companies' AI capabilities. All world companies outside the 'seven giants' put together have fewer AI capabilities than any single giant. Let's call them the AI-poor.
The gap between AI-rich and AI-poor is widening extremely fast, because AI is a winner-takes-all game: if one company cracks the autonomous vehicle problem, it wins the entire market. There’s no point for an AI-poor to ‘clone’ it, and have an ‘also ran’ technology that is second best: if ‘number one’ gets a successful trip 99,9% of the time, and ‘number two’ gets only 95%, that makes ‘number two’ not viable. A car company will buy ‘number one’s’ product because we are talking about human lives. They need to buy the technology from an AI-rich. We will explore the consequences of this technology dependency in this article.
What makes these AI-rich companies different? The seven AI giants have (1) Talent (2) Data and (3) infrastructure. Plus the seven AI giants are all platforms. The Platform business model is the most successful business model in the 21st century. A platform is a business model that creates value by facilitating exchanges between two or more interdependent groups, usually consumers and producers. In order to make these exchanges happen, platforms harness and create large, scalable networks of users and resources that can be accessed on demand. Platforms create communities and markets with network effects that allow users to interact and transact. Platform companies have far higher profits and growth than any other. For example, Google around 2016 (according to a VP of search, personal communication) had 21% yearly growth and 20% profit. These numbers were similar for Microsoft, and completely out of range for most non-platform companies, particularly enterprises. Platforms are also very difficult to displace, their network effects building an effective moat.
AI, with its zero distribution cost (digital), plugs perfectly into platforms. The seven giants use their AI and data advantage to try and enter every industry: health, HR, finance, retail, banking. It’s important to understand that even before we consider AI, these seven giants are completely different animals compared to the incumbents that reign in every vertical. The incumbents are often ‘linear companies’, not platforms, and often they are not very far in the digitalization scale. Their business model didn’t change from what was successful in the 20th century. This makes it very hard for them to take advantage of AI.
To answer this question Take DeepMind, a British AI company that was bought by google in 2014. DeepMind's algorithms saved enough of Google’s data center electricity costs to pay back the purchase price in the first year. Since then DeepMind has been in the news because they solved problems that most considered impossible, including beating the human champion at the game of Go.
DeepMind’s last breakthrough helps finding 3D structure of proteins. Scientists have identified more than 200m proteins but structures are known for only a fraction of them. Traditionally, the shapes are discovered through meticulous lab work that can take years. Alphafold, DeepMind's algorithm, managed to find structures and nearly two-thirds were comparable in quality to experimental structures. This was one of the grand challenges in biology. Alphafold matters because proteins define and power ALL life functions. It would vastly accelerate efforts to understand the building blocks of cells and enable quicker and more advanced drug discovery.
Andrei Lupas, the director of the Max Planck Institute for Developmental Biology in Tübingen, Germany, said he had already used the program to solve a protein structure that scientists had been stuck on for a decade.
Because the 'winner takes all' dynamics of AI, it's tough for an 'AI-poor' to create state-of-the-art AI in-house. They often don't have talent, data, or infrastructure. There’s no way around: they have to import AI.
The 'seven giants' strategy is to sell AI through automation. According to Kai-Fu Lee, if AI is the new electricity, they are the utility companies. They are installing 'the grid' to satisfy demand. Let's use Google as an example of how this strategy pans out.
Google has Tensor Processing units (TPUs), a technology that makes AI computation far cheaper than the previous generation (GPUs). It's in Google's interest that any 'AI-poor' consumes AI through Google cloud services. Because this is an eminently scalable business, google needs to make AI as easy to consume as possible. They are investing in how to simplify usage, expand use cases, educating businesses. The goal is that any AI-poor country or company can consume AI/cloud from them even with limited resources in talent or infrastructure.
Most of the AI-poor countries have political leaders who understand how risky this tech dependency is and try to minimize it at all costs. This is where Element.AI (And Canada!) could have been just the ticket!
Canada has the most deep learning researchers per capita in the entire world, and some of the best labs.
The country saw their ridiculously abundant researcher pool as an opportunity. They funded companies and changed visa policies to make sure Canada would become an AI-rich country. One that would provide services in AI to the AI-poor countries. Stopping the brain drain to the US was an extra layer of goodness.
Canada has excellent relationships with the G7. All G7 members (excluding the US, which hosts 4 of the 7 giants) have a skill deficit in AI. They would have been happy to buy Canadian AI, as they cannot buy AI from the US nor China: it would be a geopolitical mistake to be more in their hands than they already are. Canada could have become a competitor to the two AI superpowers (US and China) if it played its cards right. This is extraordinary: most other countries would kill to be in that position as their industries' revenues dwindle down.
The spearhead of Canada’s strategy was Element AI. A company with about 500 employees world-class-level at deep learning, rivaling the concentration of talent in DeepMind. A company with Joshua Bengio, one of the Godfathers of the field, as a cofounder. A company with >300 million USD in funding over 4 years. Element AI became the self-appointed representative of Canada’s AI sector. A company that couldn’t fail. Or could it?
Element AI, designed to avoid Canadian talent leaving for the US, was just bought by ServiceNow, a Californian company. Element AI was ServiceNow’s fourth AI acquisition in 2020, following Loom Systems, Passage AI, and Sweagle.
Having the full support of the government didn’t help finding a business model that worked. And they withered in the vine. This is a historic moment. The implosion of element AI marks the end of an era. One where Canada could raise to match the AI sophistication of the US and China, and serve the AI-poor countries.
There were two opportunities for an 'AI-poor’' country to catch up: DeepMind and elementAI. Deepmind went to google, enlarging an already tremendous advantage in AI and data, and the UK must have lamented their decision to let that happen ever since.
ElementAI went to a US company before they could produce anything of significant value, but they were still a gigantic whale that could have ‘fed’ an AI-poor country for a decade.
The chances of another AI company of that caliber forming anywhere outside the US and China are virtually zero. The ‘AI-poor’ world has missed the last opportunity to create a stronghold. The G7 (minus the US) will have to attach themselves to one of the two AI superpowers, in a deal that would get progressively worse as the value AI provides grows compared to traditional industries such as manufacturing.
I can see two possible scenarios.