sashank_1509

I don’t want to be the one to go there but I think it’s ridiculous that NyT has 2000 engineers. GTA 5 had listed 150 SWE in its credits.

It feels like Apples out of ideas, they should lean more into Vision Pro than a Mac with a touch screen or iPhone Duo.

An agentic OS, that you can speak to a majority of the time to get stuff done, also seems like an interesting direction

No, I think if you paused the game while the LLM is thinking it will still fail.

Even outside of controls based games, LLMs fail on sokoban like puzzles weirdly enough, see MazeBench, they’re also not that good at chess, probably around 1600 elo.

Even if this was true, it is very weird. When was the last time, institutions and bosses ask their employees to use a specific tool? It’s more often that employees fall in love with a tool and ask institutions to pay for it, here’s it’s the exact opposite

In the age of LLMs, is this basically a hobby now. Since an LLM can implement and apply graph theory algorithms far better than any human can. “But the human needs to judge if it’s applicable”, that’s probably not true even in today’s models like Opus 5.5. It definitely does a better job than most humans at applying graph theory to prove theorems. It also does a good job of applying it to solve algorithms. So what’s the point of this intellectual activity?

More and more it feels like as humans we are re-entering the pre 1700s era where most humans never learnt any technical knowledge, circa they never needed it.

The LLM boosters will claim, this is good and fine, LLMs will create an age of miracles and we will climb the kardashev scale or whatever other nonsense. I don’t think that will happen, I’m like 99% sure we won’t even cure cancer. We’re just making large swathes of intellectual work that we invented since the 1700s to now redundant for some reason and hoping that’s a better future, we are liberated from the “toils of thinking”. Well humanity has lived for 1000s of years without thinking of any of this stuff, so we’ll be fine but I suspect the world was richer when we did think about all this.

It is very weird that University of Kentucky is asking research administrators to incorporate AI in its workflow. It’s one thing for a Silicon Valley startup to ask SWE to use AI to maximize speed of development, its another thing entirely for a University insulated from competition, with a small admin staff, asking them to use AI for what exactly?

How much additional productivity do they think admin staff can crank out, and does that even matter. Does a 100X more productive research administrator mean the University will not be “left behind”, in this new world. I think we are officially reaching mass hysteria phases of the AI revolution.

I’m not sure what axiom maths business model is, seems like excesses of AI boom era.

No one’s gonna care for formalizing a 10,000 page math proof with 46M lines of Lean code, unless it is very cheap to do, which seems unlikely.

I might be wrong, but I expect robotics to come before Biology. At least robotics is much easier to verify, most humans can look at a robot and let you know if it is working correctly or not.

Current generation LLMs still can’t win single player games that most humans can win. Someday the LLM will be able to complete getting over it by Bennett Foddy, that day is not today

I strongly agree, I work in big tech right now and I feel almost like a charity case. Before Opus 5.5 my work would involve maybe a few hours of coding per week and pointless meetings. Now it’s few minutes of Opus 5.5 prompting and more pointless meetings. And im being paid very well for this, not gonna complain but this can’t last.

I suspect some of these anecdotes are false, 15 mins of tale of two cities, 8 digit division in your head?

This gives a sense of embellishment by a clueless layman who think very smart people can memorize every single thing they ever do. Computers memorize well, very smart humans, it’s not clear if they memorize any better than a man of the street. I was struck by a fields medalist recent comment that he sometimes struggle remembering proofs he wrote himself in the past.

I sympathize with the sentiment in this post but it’s a losing proposition.

In the past building something fast, complicated etc required quite a lot of skill and some time. Now you need far lesser skill and lesser time. This just means what you are building is commodified, and has far lesser value that it had before. Take typescript, if it becomes clear that LLMs can speed it up with no regressions and a week or so of prompting, then most likely every company will have their own typescript compiler and no one will use your new one that you built with your 200$ plan.

A lot of tasks are much easier now and also monetarily they’re not worth the 200$ or so tokens of the ChatGPT plan you’re using, because anybody (or at least a number of people) can do it now.

Rather than being more ambitious with AI, you will be far better off finding a unique combination of skills and services that does not compete / AI can’t provide. Only the big labs can be really ambitious with AI.

You have to be really confident that the PHP RCE is not anything serious, 500$ is quite a bit?

Every scaled company , say with hundreds of employees has built a moat of relationships and learnings that live in the minds of its leadership and key employees.

Can AGI figure that out. Maybe, but I think it is very likely that AGI will also have to fail in the real world, and then develop those learnings that those companies have done. There is no way to circumvent this computation, you need to do these steps to even know what the correct solution is. I like to think of it as playing chess. Intuition helps in directing exploration, but any agent, human or machine has to explore down a minimum number of paths before converging to the optimal move. There is no algorithm that can generate an optimal move without this search, P!=NP says something similar, exponential search can’t be done in polynomial speed.

Stephen wolfram says something similar, superintelligence, whatever cannot predict the output of the trillionth step of a cellular automate without calculating all the steps before it, there is a minimum amount of computation required, the problem is computationally irreducible below that.

So maybe in the future, with humanoid robots, AGI can decide to start creating a Walmart competitor. Then it will make a large number of mistakes and it needs to learn from, this could mean continual learning or maybe just memory in markdown files if that’s enough. It needs to hit all those failures quickly till it finally has something as efficient and capable as Walmart. And this is to just become a capable competitor, during this time you will keep burning money and to surpass Walmart, you have to do far better which is hard, how much more efficient can Walmart be, idk.

All to say, OAI starting its own company is a losing proposition, huge capital, unclear benefits, high risk. If OAI just provides AI to Walmart, they can make money immediately and Walmart can become more efficient by firing its human workers, which is the problem we are stumbling into.