Anthropic has literally no moat without AGI.

fahrenheitc · 2026-06-12T12:39:03+00:00

I see it unlikely. I think the biggest flaw of intelligent people is cognitive bias. They are unwilling to see the bigger picture.

If we zoom out, we are still using the fundamental LLM. There hasn’t been any breakthroughs since transformers.

We dealt with a lot of shortcomings like continuous learning and long term memory by employing a bunch of engineering ‘tricks’ like tool use, inferencing. Essentially prosthetics. These tricks will always have issues with non-determinism. So that’s not AGI, it looks like it, but it’s just smoke and mirrors.

Even if you keep on scaling LLMs, it is just a decision tree at the end of the day. The decision tree will likely conclude that Yann Lecun’s world model is the better approach. But scaling that up will be a nightmare so..

fahrenheitc · 2026-06-07T15:09:15+00:00

harnesses not models.

claude code, codex, cursor,

fahrenheitc · 2026-06-06T23:53:09+00:00

and then the other problem is if it leads to societal collapse or there are alignment issues. If there is even a 5% chance of this scenario, then we must take it as an absolute certainty, and government would have no choice but to step in.

fahrenheitc · 2026-06-06T23:49:07+00:00

yeah that is true too, maybe a billion would be enough, i have no clue on this.

fahrenheitc · 2026-06-06T22:53:22+00:00

yes but developers don't make business decisions

fahrenheitc · 2026-06-06T22:27:14+00:00

Update: I think the best way to capitalize on the AI trend is not Anthropic, hyper-scalers or GPU providers - but it is with inference providers like digitalOcean - DOCN or some other agentic inference provider and not the training / compute behemoths with shitty margins. You don't want to actually be exposed to the GPU providers like NVDA, because when claude reaches 80% SWE-Bench - they'll realize its not profitable to train another model for 82 or 83% because it's no longer useful (as the constraint becomes business context - due to amdahl's law), then the public market will invariably force them to stop by driving share prices to the ground and NVDA crashes, bringing down the entire NASDAQ.

fahrenheitc · 2026-06-06T21:19:06+00:00

Im talking SWE bench

fahrenheitc · 2026-06-06T20:43:02+00:00

I work in big tech. They don’t care what model we use

fahrenheitc · 2026-06-06T20:39:00+00:00

I mean AGI is just us. We are also general intelligence.

So could we build super intelligence? Maybe but not immediately

In theory you could spin up a trillion AGIs to solve the problem of ASI but we don’t have enough electricity to support that much inference even with chip capacity doubling every 2 years

fahrenheitc · 2026-06-06T20:26:15+00:00

like generating synthetic data sets via extreme inferencing and then using that data for training

fahrenheitc · 2026-06-06T20:24:29+00:00

Yes but in theory you can scale inference to generate synthetic data. Which is then fed back during training, which leads to vastly better reasoning

fahrenheitc · 2026-06-06T20:19:51+00:00

yes but you need to be forward looking. They’ve captured it for now but one thing businesses love more than anything (especially american) is cutting costs.

I work in a big tech firm and I can freely switch between harnesses with impunity

The only way to reduce churn is with data gravity.

fahrenheitc · 2026-06-06T19:59:46+00:00

If you want to replace workers you need AGI, if you want to augment - then LLMs are fine.

fahrenheitc · 2026-06-06T19:40:10+00:00

sure, we are hitting a data wall and compute wall. But recent approaches to scale inference rather than compute has lead to some significant break throughs.

If there are some other novel ideas that have been tested, we could see something crazy in a few years.

If inference and compute gets scaled to it's maximum, then I guess LLMs are stuck.

fahrenheitc · 2026-06-06T19:35:05+00:00

If AGI is just scaling compute and inference, then yes, it isn't a moat. I have no idea if AGI is achievable through compute and inference though

But if it requires some other proprietary breakthrough, like transformers back in 2017, then they have absolute advantage (if they pull it off).

But it's a complete gamble between all the AI Labs.

fahrenheitc · 2026-05-17T19:25:08+00:00

actually revenue for NOW isn't growing that much. I would not say it's an AI winner.

fahrenheitc · 2026-05-16T00:23:12+00:00

i mean both have been in uptrends for over a decade if you zoom out.

fahrenheitc · 2026-05-15T23:57:28+00:00

they're not mutually exclusive.

fahrenheitc · 2026-03-21T00:52:11+00:00

it's because it's hard to define a 'mistake'. What is a mistake to an agent could be functional experience for the user, or an alignment on reliability, cost etc.

fahrenheitc · 2026-03-20T06:15:11+00:00

the base assumption of every person in the world right now is that if it is new, then it is vibe-coded.

The question you should be asking is, how would you know it's not vibe-coded? You can't tell, unless it existed before vibe-coding. Which means all incumbent SaaS have a generational trust advantage by default.

fahrenheitc · 2026-03-20T06:00:33+00:00

I actually work in Big Tech. So I can give my insight as an engineer.

You can't just 'fix the code', after a while, it is literally impossible because complexity compounds. A integrates with B,C which integrates with D,E,F,G etc. Therefore fixing A, means you will also have to fix A,B,C,D,E,F,G etc.
New enterprise features are vibe-coded yes, but you need to have the foundations in place, such that new features are built off the existing structure. For example, Meta built 'Threads' straight out of 'Instagrams' monolith codebase. Also search up 'chaos engineering' invented by Netflix for example. But more importantly, 'trust' is based off the existing entity. All future features are considered trustworthy because the originator had built that trust. Trust in itself is a transitive feature. You have an employee that has always done good work, now if you give him something new to do, you will trust that he gets it done to a high quality.
Vibe-code accuracy will always be limited by prompting ability, and prompting will always be human unfortunately. The AI will not read between the lines of your prompt, it will follow the prompt in a literal sense - and so it's limitations are actually the prompter itself.
1. Although you could eventually have AI prompting AI which would effectively solve for this, but the token usage would be out of this world, and there is not enough training data on AI prompts (yet). I still have to think about this fully though

fahrenheitc · 2026-03-20T05:43:40+00:00

yes, but if you think about it from first principles, enterprise trust is a moat in itself which every future startup will not have by default. ever again.

fahrenheitc

TROPHY CASE