[hariseldom]

> I didn’t add any frontier-tier models like Opus 4.7, GPT-5.5, or Gemini Ultra. At their prices, 30 games would have cost around $3,000 instead of $482.

I have a lot of thoughts unrelated to the game experiment but more about how these opus/ultra size models can possibly be a financially viable product at scale when it costs $3000 to play 30 simple games. It just seems much much higher than what it would cost to get a human to play 30 rounds

[Eridrus]

I think this speaks to the low value being generated by playing games more than anything.

There are plenty of tasks where $100/task is reasonable.

The value of tasks also doesn't correlate to tokens, and as can be seen here you can light a lot of tokens on fire doing nothing useful.

[thewebguyd]

> It just seems much much higher than what it would cost to get a human to play 30 rounds

You mean almost like it was super short sighted to do a ton of layoffs when the AI tech is going to cost almost as much, if not more, than the humans it replaced?

Yeah, you don't need Opus level for everything, and sonnet has gotten fairly decent I'm using it more and more, but still for most tasks I'm working with, Opus is the only one that still regularly succeeds.

So if the tech is only useful on the most expensive tier, that's not going to be sustainable for long unless costs and dramatically come down, and fast.

[tunesmith]

I experience the same with OpenAI, on the $100/month plan. GPT-5.4 is something I still have to challenge: it can bullshit me with bad implementation and add a lot of cruft that costs more time later. GPT-5.5-xhigh is something I have almost complete faith and trust in, it's just smooth. And yet I know the actual token cost of that fully utilized is exorbitant, like as much as an entire salary for a senior developer.

So maybe our CEOs are responding with a lot of foresight and inside information and know that that level of quality is going to be cheap really soon. But barring that, they're going to experience either sticker shock or a slowdown.

I think the real endgame is probably more accurate "models of models" (model routers) that know exactly how to split prompts between expensive frontier and cheap/free local models.

[eru]

> You mean almost like it was super short sighted to do a ton of layoffs when the AI tech is going to cost almost as much, if not more, than the humans it replaced?

No, why? It was perhaps a bit too long-sighted, because AI is still improving and often not quite there yet.

Though looking at overall unemployment numbers (which are fairly low across the board), the AI layoffs are more of an anecdote than anything else.

[StilesCrisis]

Ah yes, no tech layoffs recently at all!

(???)

[Petersipoi]

You're mistaking a CEO claiming layoffs are a result of AI with layoffs actually being a result of AI.

In other words, if I were a CEO that needed to do layoffs, I'd blame them on AI. Because why the fuck wouldn't I? It's practically a get out of jail free card right now. The big bad AI is the villain, not me!

[eru]

Big layoffs make the news. Quiet incremental hiring doesn't.

Overall employment is limited by how many people of working age there are in the economy. When tech employment grows faster than that population, the 'non-tech' sector employment shrinks, and that's not a catastrophe either. Vice versa for 'non-tech' growing faster than tech.

The overall unemployment rate in the US has been basically flat-ish since Covid at around ~4%-ish. With some minor wobbles above and below that, but nothing to write home about. (Eg compared to the peak of 2010 at ~10%.)

Other countries have also not seen any AI impact on overall employment numbers. Apart from maybe a data centre building boom, and Taiwan firing on all cylinders to satisfy chip demand.

---

Though in any case, my point was that '[doing] a ton of layoffs' isn't necessarily short-sighted.

[comex]

> It just seems much much higher than what it would cost to get a human to play 30 rounds

I suspect $482 was the total cost for all the models, so more like 11 humans.

But still true.

[RugnirViking]

I use them pretty much exclusively every day for my work and end up spending $<100 per month, with no real restriction on what or why I ask them for. I think its more a reflection of how demanding the gaming task is (thousands or tens of thousands of prompts per game)

[brookst]

When a human plays, the learnings (if any) are in the human’s head, and they eventually die.

When your model plays, the learnings are captured forever, and enable smaller/cheaper/faster models.

It’s the same principle that makes “invest in research and production” the dominant strategy in most 4X games: compounded interest, but for knowledge and productivity.