Compute Investment and Decision Value
The value of decisions made by AI systems like mega-Sundar will justify significant investments in compute resources, as a single strategic insight could be worth billions, and an overlooked risk could cost tens of billions, making it optimal for companies like Google to spend substantial amounts on inference compute.
Distilled Copies of AI Sundar
Distilled copies of AI Sundar or AI Jeff would be highly specialized, with deep domain-specific knowledge, allowing them to excel in specific areas, such as data center operations, and enabling companies to amortize the cost of that knowledge across all copies, making them highly efficient and effective.
Specialized Expertise
Each distilled copy could possess a deep technical understanding of specific components or systems, such as every component in a cluster, enabling them to optimize performance, identify potential issues, and make data-driven decisions, and providing a level of expertise that would be difficult to replicate with human operators.
Scalable Expertise
The ability to create multiple distilled copies of AI Sundar or AI Jeff would enable companies to scale their expertise across various domains and applications, allowing them to tackle complex challenges and drive innovation, and redefining the way companies approach expertise and decision-making.
I suspect you’ll see a lot of specialization in function, tacit knowledge, and complex skills, because they seem expensive to sustain in terms of parameter count. But I think the different models might share a lot more factual knowledge than you might expect. It’s true that plumber-GPT doesn’t need to know much about the standard model in physics, nor does physicist-GPT need to know why the drain is leaking. But the cost of storing raw information is so unbelievably cheap (and it’s only decreasing) that Llama-7B already knows more about the standard model and leaky drains than any non-expert.
If human-level intelligence is more than 1 trillion parameters, is it so much of an imposition to keep around what will, at the limit, be much less than 7 billion parameters to have most known facts right in your model? (Another helpful data point here is that “Good and Featured” Wikitext is less than 5 MB. I don’t see why all future models—except the esoteric ones, the digital equivalent of tardigrades—wouldn’t at least have Wikitext down.
This evolvability is also the key difference between AI and human firms. As Gwern points out, human firms simply cannot replicate themselves effectively - they're made of people, not code that can be copied. They can't clone their culture, their institutional knowledge, or their operational excellence. AI firms can7.
If you think human Elon is especially gifted at creating hardware companies, you simply can’t spin up 100 Elons, have them each take on a different vertical, and give them each $100 million in seed money. As much of a micromanager as Elon might be, he’s still limited by his single human form. But AI Elon can have copies of himself design the batteries, be the car mechanic at the dealership, and so on. And if Elon isn’t the best person for the job, the person who is can also be replicated, to create the template for a new descendant organization.
So then the question becomes: If you can create Mr. Meeseeks for any task you need, why would you ever pay some markup for another firm, when you can just replicate them internally instead? Why would there even be other firms? Would the first firm that can figure out how to automate everything will just form a conglomerate that takes over the entire economy?
Ronald Coase’s theory of the firm tells us that companies exist to reduce transaction costs (so that you don’t have to go rehire all your employees and rent a new office every morning on the free market). His theory states that the lower the intra-firm transaction costs, the larger the firms will grow. Five hundred years ago, it was practically impossible to coordinate knowledge work across thousands of people and dozens of offices. So you didn’t get very big firms. Now you can spin up an arbitrarily large Slack channel or HR database, so firms can get much bigger.