Your example of “Everything Inc” is also similar to what I’m expecting. As in, I agree with: 1. The large majority of business strategy/decisions/implementation can (somewhat) quickly be done by AI systems. 2. There will be strong pressures to improve AI systems, due to (1).
That said, I’d expect: 1. The benefits are likely to be (more) distributed. Many companies will be simultaneously using AI to improve their standings. This leads to a world where there’s not a ton of marginal low-hanging-fruit for any single company. I think this is broadly what’s happening now. 2. A great deal of work will go into making many of these systems reliable, predictable, corrigible, legally-compliant, etc. I’d expect companies to really dislike being blind-sighted by sub-AI systems that do bizarre things. 3. This is a longer-shot, but I think there’s a lot of potential for strong cooperation between companies, organizations, and (effective) governments. A lot of the negatives of maximizing businesses comes from negative externalities and similar, which can also be looked at as coordination/governance failures. I’d naively expect this to mean that if power is distributed among multiple capable entities at time T, then these entities would likely wind up doing a lot of positive-sum interactions with each other. This seems good for many S&P 500 holders.
”or anything remotely like them, to “Everything, Inc.”, I just can’t. They seem obviously totally inapplicable.” This seems tough to me, but quite possible, especially as we get much stronger AI systems. I’d expect that we could (with a lot of work) have a great deal of: 1. Categorization of potential tasks into discrete/categorizable items. 2. Simulated environments that are realistic enough. 3. Innovations in finding good trade-offs between task competence and narrowness. 4. LLM task eval setups would get substantially more sophisticated and powerful.
I’d expect this to be a lot of work. But at the same time, I’d expect a lot of of it to be strongly commercially useful.
Your example of “Everything Inc” is also similar to what I’m expecting. As in, I agree with:
1. The large majority of business strategy/decisions/implementation can (somewhat) quickly be done by AI systems.
2. There will be strong pressures to improve AI systems, due to (1).
That said, I’d expect:
1. The benefits are likely to be (more) distributed. Many companies will be simultaneously using AI to improve their standings. This leads to a world where there’s not a ton of marginal low-hanging-fruit for any single company. I think this is broadly what’s happening now.
2. A great deal of work will go into making many of these systems reliable, predictable, corrigible, legally-compliant, etc. I’d expect companies to really dislike being blind-sighted by sub-AI systems that do bizarre things.
3. This is a longer-shot, but I think there’s a lot of potential for strong cooperation between companies, organizations, and (effective) governments. A lot of the negatives of maximizing businesses comes from negative externalities and similar, which can also be looked at as coordination/governance failures. I’d naively expect this to mean that if power is distributed among multiple capable entities at time T, then these entities would likely wind up doing a lot of positive-sum interactions with each other. This seems good for many S&P 500 holders.
”or anything remotely like them, to “Everything, Inc.”, I just can’t. They seem obviously totally inapplicable.”
This seems tough to me, but quite possible, especially as we get much stronger AI systems. I’d expect that we could (with a lot of work) have a great deal of:
1. Categorization of potential tasks into discrete/categorizable items.
2. Simulated environments that are realistic enough.
3. Innovations in finding good trade-offs between task competence and narrowness.
4. LLM task eval setups would get substantially more sophisticated and powerful.
I’d expect this to be a lot of work. But at the same time, I’d expect a lot of of it to be strongly commercially useful.