theptip

> This gets to the same point of "If ChatGPT can make you a successful million dollar business in a few prompts why is OpenAI sharing it" paradox

I agree fully, to be clear o was using that prompt as a shorthand, I don’t think _you_ can type that prompt and get $1B back.

It can’t do this now, obviously. You need a lot of scaffolding and domain expertise to get the most out of these models. (Eg Harvey.)

But eventually, the labs can just run the business themselves.

I’ve long held that as we see models become more capable, they will be less available to the public. It seems pretty obvious that vertical integration on private models (perhaps a per-industry fine-tune, eg a legal model) licensed to subsidiaries will let the labs capture more value.

Why would you publish your model, enable competitors, and enable distillation of your hard-won intelligence? Only antitrust law can prevent this outcome IMO, the business pressure is extremely strong.

Congrats on the release - private repos are going to be huge. How do you see the positioning vs Buzz and Radicle? Do you see them doing anything better now, and are you planning to try to close any specific gaps?

From my very limited testing private repos were the big missing piece; interested in other areas yet to be filled in.

Would AI companies rather have $100/yr of license revenue or $480/yr of token revenue at 50% margin? Obviously the latter, by more than 2x? And even larger dollar profit from the higher sub levels you mention.

> Someone can always optimize a model down to running on a typical 32 GB M6 or M5 Mac. It doesn't need to be the best.

Two observations - one, there is some set of tasks where intelligence is already saturated, and sure, that threshold is rising by the month. So, sure, for some tasks you don’t need the best model. But there are other tasks that need models smarter than what we have now - like “make a cure for <disease>” that will yield billions of dollars of value, but likely require quintillions of tokens and higher IQ/token than anything we have now.

As long as we have non-saturated economically valuable tasks there will be incentive to train new and smarter models, and the frontier will always be on racks of GPUs not local distilled versions.

Two, it’s not clear that you can distill all the way down to 32GB even for the current frontier models. IIRC Karpathy has speculated that you can get an AGI into GPT-3 size, if you drop all the memorized facts and keep the reasoning core. But this is highly speculative! The best 32GB models right now are substantially dumber than even the large open-weight models, let alone the frontier closed models.

Perhaps I am missing something, what are you imagining of “a locally ran inference engine” that doesn’t already exist in a DGX workstation, Framework desktop, MacBook, etc?

It would have made HF safer, that’s for sure.

HF is a US company, so this is irrelevant to my point.

HF was not signed up to any of the (free to OSS) programs like Daybreak or Glasswing.

Perhaps the crux is: what is “value”?

If we mean dollar value then yes, hand crafted code may go to zero as nobody wants to pay a 10x premium for it.

If we mean (as I did) the subjective value to the creator themself, then there is no reason this has to stop. It will just make the same shift as farming to gardening.

You shouldn’t expect to get paid for this, or do it on your employer’s time, but you are absolutely free to do it in your leisure time, and perhaps just like Go and Chess, AI will make these pursuits more accessible than ever, to those who are interested in learning for fun.

No. It’s a healthy practice to build prototypes and spikes to resolve technical uncertainty. These should be considered disposable.

Sometimes people get attached to the prototype, and that’s counterproductive. The most common example is “nice job, the 2 week spike shows us that this project is not viable, we are canning it and moving on to a more impactful project”.

This is an underrated perspective. A common tactic I use with agents. Just building the slop prototype until it has enough fidelity to be obviously right or wrong can be way faster than sitting down, thinking hard, and planning in advance. This is especially true for background tasks, and IME is the key to productively parallelizing yourself without decision paralysis.

At some point token efficiency will bite, but even that is overrated; the metric you really should care about is:

impact / (salary + token spend)

> Throw nine autonomous agents at a single codebase without crystal-clear orchestration, and they will happily rewrite each other's work in a high-speed loop.

Mm, I think this is quite overstated in the era of Opus 5 / Sol.

What we actually see is that agents are much better at coordinating than humans. Numbers from OpenAI show 4x the agents gives 2x the speed.

Of course, for truly single-threaded tasks you can’t gain much by parallelizing, but ask Fable/Astra to chop a big task into small pieces, write the APIs first, then sub-agents take what’s behind the API, and you’ll usually get a much better speedup than if you asked humans to work this way.

In practice most big projects have crux choke points and then fanout migration/buildout phases.

Even on the cruxes, where you need a single decision, agents don’t have ego and so you can get speedups by eg. asking for 10 clean room implementations of the same problem, and have a judge model pick the best; this can let you go much faster, and no human team would put up with that collaboration model.

Missing the point. Which is, some people like the details, code as craft; others like the high level, product/business impact, systems, etc, code as a means to an end.

The profession used to have a need for both, now the “coding machine” and artisanal code crafter are trending obsolete in most companies.

It’s asymmetrical though. Google is terrible at product and moves glacially slow.

Anthropic can take the parts of Google it needs FAR faster than Google can do the reverse.

Yeah I’m really surprised at how little attention this is getting.

I’d say it’s more Notion than GDocs for now, but the direction is clear. Gobbling up all the SaaS in order of descending value to agents.

My bet is on promoting their internal Tasks representation to something more like a Jira/Linear.

But then obviously Slack is another one, and Duet is already starting to build a “Channel” abstraction, so making Duet multiplayer is a clear possible next step.