lol these agents are missing the point re. What people actually do in these jobs.

This is an attempt to inflate token generation to fool people into increasing anthropic’s valuation.

> Will the big labs leave anything for external competition?

No, why would they if they have the choice?

> what happened to platformication of everything?

Business happened. The web works differently from how it used to. The users are different. LLM inference and AI tools is a different core product from search and ads. That, and we have the benefit of hindsight now. Maybe a Google newsroom would've actually been a good idea in 2006 in hindsight, who knows.

Also realistically you could say the same thing about Google Maps and Street View. That probably also killed some startups. Google isn't running a charity for startups.

This is premature caution/fear.

> Will the big labs leave anything for external competition?

Unfortunately no.

The TAM for Anthropic and OpenAI is anything that runs software or a screen.

Any software or technology business that has high margins that Anthropic and OpenAI are not doing will be a target.

After both their IPO's mandates Wall Street them to push for more growth by competing in other technology business areas or they will get punished in the markets.

It is ROI or bust.

But Google did move into a lot of spaces: maps, mail, docs, etc.

> in the early internet you wouldn't see google creating their own news site

Google News was definitely a thing (and actually still exists).

Building a startup on an LLM is like building a house on a foundation of quicksand. As the LLM gets better it naturally erodes your moat. It's a completely different dynamic compared to the internet. It's why I'm watching this from the sidelines.

You’re advocating for less competition? AI startup valuations are out of control. People are raising $20m seed rounds.

If you can’t prove PMF and differentiation with $10m, I’m sorry but you’re not a serious enterprise.

And if what you’re building is “pitch deck AI”, I mean, come on.

History suggests otherwise. railroads, telecoms, search all consolidated. The natural equilibrium for transformative infrastructure is winner take all. AGI/ASI won’t be different but will be nearly every vertical and governments will legislate too little too late.

I am not sure if people are using claude design, security review stuff and other tools they have built so far.

Building is the easy part. There are lot of service level stuff that I am sure anthropic will not be able to provide, therefore they are trying to partner with other orgs in that realm.

I am very skeptical about their stuff now.

If you are builder, I believe you should avoid anthropic, it can be default to monopolistic behavior, I am not saying they are doing it, but they could, where in they see what you are building, if you have traction, position a product in that realm. Just saying.

> tfw you've been huffing your own copium so much that you forgot you're selling shovels

Nope If anything firms are pulling back (I know someone closely who works at blackrock).

Seen it used in some of the fraud models (I work in insurance). So that's both from the perspective of people trying to claim fraudulently and from suppliers over charging. I can't say how much of a lift we actually get vs existing ML models

On the spend management side of things, I've found pretty remarkable success in letting LLMs check "does this receipt match this reimbursement request and based on all the information about the user, the request, and our policy, is it appropriately allocated to appropriate GL, Location, Department, and Project codes?" If the verification step fails, it kicks it back and the user can either override it (which gets it flagged for AP review), or fix it. It does substantially better than the naive Bayes classifier I was using before.

Yes. On the accounting side agents can handle a lot of the low value work like recons and other ledger activity pretty well. On the investment side I think like you pointed out it’s going to be a lot of research, industry, company, macro etc. Value in letting run on top of the data you have and put together ideas at a quicker pace than a human can. There is still a human in the loop but it can do a nice job of lining up thought you might have otherwise missed.

Reads different to me. Some examples to go run with and build your own. Covers cases from the investment side and then the obvious ones in an accounting perspective. It would be highly surprising that any of these would be use in production without modification. I am sure it will happen but the intent to me is to take this and run with your own process.

I find all of these .md files released by the labs to be ai generated slop. The only exception being maybe the /simplify command

the details are key here. there is plenty of automatable financial work, sure, but also when it comes to reporting finances/costs (formally or informally) and having a real human being be accountable for them, you REALLY need to trust that nothing is hallucinated.

Any idea how they ensure this doesnt happen? As in, how can a user verify that the model did not touch any of the numbers and that it only built pipelines for them.

what I've been telling my CFO who wants to get AI involved in things is that for a lot of accounting and finance work "Trust but verify" doesnt work because verify is often the same process as doing the work.

I'll be honest, I thought the first few items on your list of time consuming work was sarcasm.