It is quite hard to imagine how the demand is saturated now. I think any company that uses a sliver of AI will happily increase their token consumption 100x if it's free.

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flir a day ago | parent | next [-]

Are you assuming a brute force "burn tokens until it passes the tests" model, or is there a really sweet approach on the horizon that is impractical at current token costs?

I'm asking 'cos while I'm philosophically opposed to the first option, but I'd love to hear about anything that resembles the second.

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a day ago | parent | next [-]

[deleted]

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SpicyLemonZest a day ago | parent | prev [-]

One idea I've heard is prototype-first design reviews. If the cost of code genuinely trends to zero, there's no reason why most technical disagreements about product functionality couldn't come with prototypes to illustrate each side of the debate. Today, that's not always practical between token costs and usage limits.

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pydry 21 hours ago | parent [-]

What if the agent fucks up the better approach but does a good job of the worst approach?

	▲	SpicyLemonZest 20 hours ago \| parent [-]
		Then hopefully the reviewers will notice that the first prototype's flaws are correctable. Sometimes they won't, and they'll end up making a bad decision, just as they sometimes make bad decisions today with no prototypes to look at. But having prototypes allows for a lot of debates that are today vague and meandering to be reduced to "which of these assertions at the end of this integration test do you think is the correct behavior?".

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pydry a day ago | parent | prev [-]

Executive FOMO disease is being exploited by the model providers to push for maximal token usage even when it is pointless.

This includes encouraging people to set up elaborate multi model set ups (e.g. "gas town") for coding that do not meaningfully improve productivity but which certainly do cause token usage to explode.

It also includes encouraging execs to use token consumption as a proxy for productivity - almost akin to SLOC.

AI has a halo right now and the managerial class seem to be willing to forgive almost any failure because the promise is so enticing. We're at peak expectations right now. They will soon start to be less forgiving when the warts which are intrinsic to LLMs remain unsolved.

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monknomo a day ago | parent | next [-]

nobody know how to measure software productivity + ai is supposed to mean productivity goes up = more ai means more productivity

As best as I can tell, that's the thinking. It's one number, it's very easy to find and manage, and there is a belief that it directly measures productivity.

I disagree that it does; seems to me the throughput of useful features is a better measure, but I'm not in the drivers seat on this one

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irke a day ago | parent [-]

Incremental revenue and cost-savings, at least for enterprises, is where it would show up. There’s also a present value consideration - if LLM’s make those dollars come into existence closer to the present, they are worth more.

The personal use case stuff is messy and subjective.

	▲	monknomo a day ago \| parent [-]
		attributing incremental revenue to gross engineering effort is challenging, imo. Cost savings is primarily a function of headcount here. Which is also easy to measure, and so if we take my thesis that easy to measure stuff is prioritized...

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irke a day ago | parent | prev [-]

Yep - it’s impossible to separate experimental tokens vs value creating ones.

Ultimately the performance will be assessed via the income statement and cash flows of customers of the model producers.

Frankly in the window pre-IPO it’s in the best interests of OAI et al to show a line going to the top-right in relation to tokens, in their prospectus. What does that mean?

Strategic manipulation.