nlpnerd

This is basically Temu Jev

Yet another "AI data lab" that is really just running data labelling operations for the frontier labs. I do wonder where this business is going in the long term.

How real is this? That people are taking inference costs seriously enough to invent a new line item "AI COGS"?

Except there are like a dozen Openrouter clones out there with nowhere near the same traction. Not as easy as people believe to build a successful startup. The team probably had the right experience and advantage from building OpenSEA.

Not because everybody from the old team left?

"Where is the return on investment? The return is in the usefulness of AI."

Wow.

What YT videos?

TIL

From a recent article in The Economist. It turns out that once again many of the jobs that people predicted would be eliminated by AI have instead received a boost, at least for now. This closely follows another recent report on how Indian BPO firms are surprisingly thriving due to AI.

"Moreover, AI is helping offshore workers in the Philippines do new, more complex jobs. They are picking up work training AI models or supervising AI agents. Hospitals in America are increasingly outsourcing the checking of insurance eligibility and filing of medical records to Filipinos packing AI tools. Mr Gallimore says more “high-value” work, such as accountancy or engineering, is going offshore in part because “AI is a leveller. You can now teach someone complex stuff quickly” and still get it done more cheaply than in America or Europe. Some of this is done at so-called global capability centres, captive back offices that are booming in India and also now growing in the Philippines."

It seems like AI has incidentally commoditized the complement (procedural knowledge), and in turn created greater demand for inputs like human judgement and "empathy".

Fine line between articulating a vision well or marketing vs just lying.

This why those RL env startups are able to charge frontier labs so much for their work. LLMs still generalize poorly outside of self-verifiable tasks like coding and math.

Labs have to compensate with post-training in RL env that embeds these expertise well, which is non-trivial both in terms of domain knowledge and technical expertise.

Very much agree that a lot of LLM calls today are redundant and could be decomposed into reasoning vs just parsing(regexes), ETL, etc

That's a good point actually. I just recalled that Openrouter charges a premium of 5.5% on transactions. That's probably comparable or fat relative to the cut for payments processors like Stripe.

Like what's the angle here, and what's the outlook for LLM routers like Openrouter to justify a 10B valuation?

Somebody enlighten me.

The mad rush and overhiring didn't stop immediately after Covid. I know of cases where experienced software engineers were getting picked up by big tech with huge increments as late as end of 2023.

My impression is that the current trend for layoffs only really began around early 2025. For reference, the layoffs during the dotcom bubble I was told took around 2 years to plateau

OP here, and I agree it's probably not AI directly and more of dealing with the post Covid overhiring, and CAPEX for AI investments.

Enterprise adoption is definitely not happening as fast as the AI proponents (startups, frontier labs, big tech) would like people to believe (probably the reason why frontier labs are going the FDE/consulting path). So it's unlikely that the impact on productivity is enough to produce the alleged impact.

This^

This is why every other crypto company is now an AI company.

This is a fascinating example of perhaps why a move towards a "world model" or some better form of representation can be helpful.

Honestly, the article title is a bit of clickbait. The main complaint is about people on medical leave being disadvantaged by the token usage as performance metric that was introduced.

"Nadella argues that if AI companies get to freely scrape the internet to train their models, it’s only fair that enterprises get to study — or “distill” — those models in return."

Two wrongs don't make one right

AI coding forces people to frontload a lot of the detailed thinking that used to take place over an epic or sprint. It's not a very natural way of working, and in fact is quite contrary to the iterative development style that most devs are already used to.

I think a lot of ppl assume that usage here is simply for AI coding, which is easily governed. I suspect that the more tricky issue are those usage powering workflows and application logic that cannot be easily throttled down.

Yeah

That's kind of stretching things. It's not that software cannot be maintained but the cognitive load of building and maintaining software seems to be now backloaded to the maintaining part and people need to be aware of this.

Well...we didn't sack anybody if you were wondering. I feel like Ford's issues is mainly overestimating AI's capabilities. Whereas our case is more of a need for re-engineering processes to be more AI-native.

That is an idealistic take without business sense. Startups (and individual hackers in this case) exists to take this kind of radical bets because the risk/reward profile is asymmetrically in their favour. Whereas for an enterprise, the risk/reward is inverse.

If Peter Steinberger is able to generate even a 100M this year from Clawdbot what he has is a multi billion dollar business that would be life-changing even for a successful entrepreneur like him who is already a multi-millionaire. If it collapses from the security flaws, and other potential safety issues he loses nothing, starting from zero and going back to it. Peter Steinberger (and startups in general) have a lot to gain and very little or close to nothing to lose.

The iPhone generated 400B in revenue for Apple in 2025. Clawdbot even if it contributes 4B in revenue this very year would not move the needle much for Apple. On the contrary, if Apple rushes and botches releasing something like this they might just collapse this 400B/annum income stream. Apple and other large enterprises (and their execs) have a lot to lose and very little to gain from rushing into something like this.

"Paper after paper shows these things are hiding data, fabricating output, reward hacking, exploiting human psychology, and engaging in other nefarious behaviors best expressed as akin to a human toddler - just with the skills of a political operative, subject matter expert, or professional gambler."

Anthropomorphizing removed, it simply means that we do not yet understand the internal logic of LLM. Much less disturbing than you suggest.