Comment by hiddencost
3 days ago
That presumes that performance improvements are necessary for commercialization.
From what I've seen the models are smart enough, what we're lacking is the understanding and frameworks necessary to use them well. We've barely scratched the surface on commercialization. I'd argue there are two things coming:
-> Era of Research -> Era of Engineering
Previous AI winters happened because we didn't have a commercially viable product, not because we weren't making progress.
The labs can't just stop improvements though. They made promises. And the capacity to run the current models are subsidized by those promises. If the promise is broken, then the capacity goes with it.
> the capacity goes with it.
Sort of. The GPUs exist. Maybe LLM subs can’t pay for electricity plus $50,000 GPUs, but I bet after some people get wiped out, there’s a market there.
Datacenter GPU's have a lifespan of 1-3 years depending on use. So yes they exist, but not for long, unless they go entirely unused. But then they also deprecate in efficiency compared to new hardware extremely fast as well, so their shelf life is severely limited either way.
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> They made promises.
That's not that clear. Contracts are complex and have all sorts of clauses. Media likes to just talk big numbers, but it's much more likely that all those trillions of dollars are contingent on hitting some intermediate milestones.
Maybe those promises can be better fulfilled with products based on current models.
We still don't have a commercially viable product though?
I've fed thousands of dollars to Anthropic/OAI/etc for their coding models over the past year despite never having paid for dev tools before in my life. Seems commercially viable to me.
> I've fed thousands of dollars to Anthropic/OAI/etc for their coding models over the past year despite never having paid for dev tools before in my life. Seems commercially viable to me.
For OpenAI to produce a 10% return, every iPhone user on earth needs to pay $30/month to OpenAI.
That ain’t happening.
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If you fed thousands of dollars to them, but it cost them tens of thousands of dollars in compute, it’s not commercially viable.
None of these companies have proven the unit economics on their services
If all frontier LLM labs agreed to a truce and stopped training to save on cost, LLMs would be immensely profitable now.
That isn't what I've seen: https://www.wheresyoured.at/oai_docs/
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google what you just said and look at the top hit
it's a AI summary
google eats that ad revenue
it eats the whole thing
it blocked your click on the link... it drinks your milkshake
so, yes, there a 100 billion commercially viable product
Google Search has 3 sources of revenue that I am aware of: ad revenue from the search results page, sponsored search results, and AdSense revenue on the websites the user is directed to.
If users just look at the AI overview at the top of the search page, Google is hobbling two sources of revenue (AdSense, sponsored search results), and also disincentivizing people from sharing information on the web that makes their AI overview useful. In the process of all this they are significantly increasing the compute costs for each Google search.
This may be a necessary step to stay competitive with AI startups' search products, but I don't think this is a great selling point for AI commercialization.
And so ends the social contract of the web, the virtuous cycle of search engines sending traffic to smaller sites which collect ad revenue which in turn boosts search engine usage.
To thunderous applause.
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I don’t think the models are smart at all. I can have a speculative debate with any model about any topic and they commit egregious errors with an extremely high density.
They are, however, very good at things we’re very bad at.
Have you considered the AI is right, and you make the mistakes?
> the models are smart enough, what we're lacking is the understanding and frameworks necessary to use them well
That’s like saying “it’s not the work of art that’s bad, you just have horrible taste”
Also, if it was that simple a wrapper of some sort would solve the problem. Maybe even one created by someone who knows this mystical secret to properly leveraging gen AI
Besides building the tools for proper usage of the models, we also need smaller, domain specific models that can run with fewer resources