@jerryjliu0: This is a great article on how startups/frontier labs can coexist. Another way to look at this is task complexity - the…

This is a great article on how startups/frontier labs can coexist. Another way to look at this is task complexity - the number of bits of information needed to specify a task such that AI can solve the task above a threshold of accuracy: * If the minimum number of bits is low (e.g. summarize call transcript), then you can just prompt Claude Cowork to do it. * If the minimum number of bits is much higher (e.g. follow a 100-page SOP for a production-line deviation) - especially if the task needs to be standardized throughout the org - then the act of specifying the task with the relevant guardrails/auditability/communication becomes much more complex, and it is simply infeasible to expect that an organization can harness the core technology without the software scaffolding in place. Higher complexity task specifications are correlated with how complex it is to verify those tasks, though they aren't necessarily the same. I think both directions are opportunities for AI startups to tackle. E.g. an e2e sales rep agent is somewhat easy to verify (overattain your number), but task specification of how to actually do it is complex, and the time horizon for running it can take over a year - to see whether the rep can actually hit its number! This means that even if Fable 5 can do it accurately by just giving it a goal, there's lots of opportunities to optimize this workflow to massively reduce cost (in this case it matters for S&M spend) A lot of tasks are both highly complex to specify and hard to verify e.g. complex insurance claim adjudication. In these cases, the massive bottleneck isn't the model itself, but in the human's ability to even define what good looks like to solve the task at hand. As frontier models get better, the minimum number of bits to specify any task will go down, but IMO there will still be a massive gap for knowledge work that any non-frontier lab company can exploit.