News recently broke that NVIDIA is investing 100 billion dollars in OpenAI.
OpenAI, in turn, is expected to turn around and buy a similar amount worth of chips from NVIDIA.
OpenAI gets money from NVIDIA. OpenAI turns the money into NVIDIA chips. NVIDIA gets the money back from OpenAI.
Everyone’s making money. Everyone should be happy, right?
Wait a minute, something is wrong here. Or is it?
We are witnessing a fascinating dynamic right now. To typical observers, this should feel very bubbly. The underlying reality has some very different mechanisms at play.
NVIDIA’s game is nurturing competitors so that there is demand for NVIDIA’s chips. Under no circumstances does NVIDIA ever want to create a demand monopoly, because if NVIDIA only has one customer, that customer can set the price.
OpenAI’s aim, of course, is to dominate AI and power not just the AI sector of the economy, but the entire economy itself.
There’s a caveat here though; For the AI companies, their investments are enormous and justifiable because they think it will pay off. How? Other companies will pay for it. Why? Because it can replace workers and multiply the efforts of existing workers.
That’s not lucrative enough for the investments being discussed though. OpenAI’s Sam Altman has hinted quite openly about how they will need $7 trillion to pay for AI datacenters soon enough, and he doesn’t expect for growth to stop there. Yet, that is almost a quarter of the total value of all US GDP.
How, in any way, does it make sense to put so much money into AI?
The answer lies in acceleration. Let’s mark 2022 as the last regular non-AI economic year. If you fully automate all work done in 2022 via AI such that it can be completed at 10x the pace (very possibly for much of white collar work), you have a possibility of a 10x gain in GDP in the same 1-year timespan. Equivalently, at the half-year mark of this fully-automated economy, it will have produced 5x as much as the regular human-only 2022 year.
From this perspective, it makes sense for AI labs to place massive bets. To them, it could easily be winner-take-all, and the all to take is much, much larger than what it is today.
For the chip companies, their investments are highly strategic. If NVIDIA is investing chips in OpenAI, it is jumping on the one lever it has: giving a company a lot of chips.
One reason NVIDIA would choose to do this is they sense an imbalance in the market. One company is too close to winning and having a runaway monopoly.
But the other possible reason is they see other companies being too equal in capacity. The best way for NVIDIA to force all players to spend more is to bolster up a loathed competitor. While Google has its own TPU chips, it may be forced to buy extra NVIDIA chips just to stay competitive at the scale OpenAI is being raised to.
The funny thing is that NVIDIA can continue playing this game forever. If a smarter but under-resourced AI lab falls behind, NVIDIA can give that one some more chips and keep the other labs on their toes. If they’re all getting too equal in capability and capacity, NVIDIA can give more chips to the company that is tapped out on money or investors, forcing all the other moneyed players to pour more cash in to keep an equal footing.
There must be limits, right? One would think all the benefits of AI-accelerated economics would end at the world of software. If all computer related knowledge work were automated and accelerated to operate at 10x the pace everyday, I do think the benefits of it would be enormous, but that alone does not provide a 10x boost to the whole economy. At some point, the rubber does have to meet the road. So what is the answer?
Expand the domains that are categorized as “computable”.
A great example of this is digital biology and chemistry. With accurate enough simulations and AI models trained to develop molecules, an AI model can run highly accurate medical simulations in-silicon and land on near-perfect drug candidates right away. This would massively speed up drug trials, finding working medicines, and reaching FDA approvals. This process has been the single largest bottleneck of the pharmaceutical industry for decades and advancing beyond that would open up an entire sector of the economy by an order of magnitude.
In this case, there is also the add-on benefit that an enormous percentage of the working populace could suddenly be more healthy and energized, which would certainly aid in growing GDP as well.
It doesn’t stop there though. Rapid advances are being made in AI for general-purpose robotics: Models that can control robots in an adaptive manner such that the same model can operate completely different robotics platforms and autonomously figure out how to accomplish tasks across these different machines.
The reason this is important is that robotics itself is the final bottleneck to economic advancement. Robots operate in the frontier of things that cannot be automated purely by better simulations or AI models or software: Physical production.
And the reason THAT is important is that once superintelligent AI models can arrange matter according to its own design, there is very little standing in the way breakneck economic acceleration.
Factory plans can be designed to have perfect assembly efficiency and built by robots. The robots can operate 24/7, and will likely be able to work faster than humans as well. Jobs that once took Months could conceivable take days, given efficient-enough supply chain logistics. This will give rise to hardware supply capacity that will enable AI to power advancements and production automations across every field known to man.
In fact, Sam Altman recently wrote about how this is OpenAI’s current plan: To create a factory that can output 1 gigawatt of compute hardware capacity and energy infrastructure every week. Having no restriction the hardware provides enormous unlocks across the board, both for innovations in model intelligence, and inference capacity itself.
The decades to come will be a marvel to witness, barring any major catastrophe. People concerned about bubbles today, again, are right to do so. But they are missing what AI as a technology *is*: Bigger than the internet, bigger than electricity, and – dare I say it – bigger for the species than the discovery of fire. It is the missing ingredient for an autonomous, self organizing civilization.
Despite global GDP being the highest it’s ever been, we should expect it to grow several orders of magnitude in the coming decade as we experience the equivalent of several lifetimes of innovation in just a few short years.