True visionaries think outside the box, but most tech executives are forcing their employees into black boxes, out of fear of not doing exactly what their competitors are doing.

We have lemmings for leaders, and that means that—much like the LLMs that are being shoehorned into everything—there isn’t room for original thinking. Everyone’s strategy looks exactly the same.

I'd be curious to hear from people well versed in group psychology/dynamics and/or just a lot of leadership/people experience: what leads people to this type of thinking once they get in a group setting? It just... seems endemic at this point.

Obviously nobody here is going to know what I do or don't know, but I'm just increasingly curious what I am not understanding about this type of thing. It seems so obvious, yet that makes me ever more suspect that I'm oversimplifying it, or just totally ignorant about the problem in general.

Lets talk my bonus, I will open the bidding at $1 per token.

"It is difficult to get a man to understand something, when his salary depends on his not understanding it." -Upton Sinclair

Each country has their own laws around what uber is and isn’t allowed to do. This needs to be formalized in code. For example you actually call a taxi, though the uber app, and the amount you pay is per mile, not a fixed fare decided ahead of time. To add to this complexity, some cities will have their own laws. What happens if you take an uber from town a to b, where each one has different laws ? A lawyer probably has an answer but the app needs to adhere to that. On top of that laws change all the time.

Optimization, well you can always optimize something. speed, costs, paths etc. In a way this never ends.

I think the part we interact with as consumers is a tiny sliver of the complexity those services have to build and operate.

I think this is partly a problem with companies that have had heavy investment. Uber’s value isn’t based on what they are doing, it is based on the idea that they are going to render ideas like owning your own car or taking public transit obsolete (I mean that’s an exaggeration but less of one than it ought to be).

It's like trying to win a race by setting a gas station on fire.

> They should also promote projects that aim at saving tokens, increasing cache hits, codifying the information in ways such they use as less context as possible (graphs of knowledge are pretty good for this!)

My understanding is that most big "tokenmaxxing" companies do have teams who are working on this in the background.

Pretty sure I know what you're saying, but the visual on this one doesn't match the point you're making.

Still very valuable. They just need to have strategies that match what the tools are capable of - not strategies that involve "rub the magic lamp and increase profits 80%".

If the market is rewarding companies going after the "rub the lamp" strategy, they're going to say they're doing that to juice stock prices.

Maybe the market is finally realizing blindly spending billions on LLMs with almost no strategy is not a good strategy.

Who knows.

The number of product teams who have shipped expensive-to-operate AI features is wayyyy up there, and for many of the scenarios I've seen, customers simply don't care or are unwilling to pay significant premium for access to it.

At the same time I'm starting to see some direction from people in leadership that I should "use the right model for the job" and things along those lines, which is a very, very different line from what I was hearing 12 months ago.

My continued prediction is that we are going to see a tweak on the SaaS model where the sweet spot moves to metered usage pricing of really fine-grained API-based access for apps which traditionally have been operated solely via the UI. Long term the trend is going to be "we'll house the data, enrich it, maintain it, provide fine-grained API access over it tailored to model usage, and you bring the model" with some services opting to give you the model interaction layer/harness. IOW I don't think SaaS is dead. Far from it. However, I do think that a lot of people are going to be looking to interact with SaaS apps via their own models with APIs that support those use cases better than a lot of those APIs do today.

Anyone who can find the actually valuable portions of the space early has a potentially huge competitive advantage. Even if the result of the experiment is the negative that AI is actually mostly not that useful, that is still extremely useful information in a time of great uncertainty regarding outcomes.

The bottom line is that this approach may be expensive, but if you have the money to burn, it's far from the worst strategy if you are trying to position yourself correctly for the future.

The incentive structure of this type of decision is 'absolutely under no circumstances existentially mess up'. Ostensibly with respect to the organisation, but in actual reality much more so with respect to the individual(s) involved in the decision.

If everyone else is doing something that kind of obviously makes no sense, and you decide to break from the crowd by instead doing what does make sense, then there's a pretty solid chance of gaining a temporary edge while reality resolves the truth. But those gains probably won't matter all that much for the organisation, or indeed your position within it. It's a solid chance of an unimportant gain.

However on the other hand, there's a tail risk that something very unexpected happens and the thing everyone's doing that makes no sense actually turns out to make sense - sometimes even for entirely unpredictable incidental reasons - and then, well, you're in trouble. Not necessarily 'you' the organisation.. they'll likely be able to catch up and it won't matter that much. But for 'you' personally, the decision maker, it's very much not good.

As a bonus, in the much more likely scenario that the thing that makes no sense turns out to indeed make no sense, you're in the same boat as everyone else, there's no relative loss, and most importantly you don't stick out as someone who did something as risky as to go against the prevailing, albeit pretty clearly nonsensical, sentiment.

So basically, game theory tells you pretty quickly to just go with the thing that makes no sense if you're optimising for some (weighted) cross of what's best for the organisation and yourself as the decision maker.

We aren’t there yet, so far it is just a COO questioning the investment

But it's not. Some FAANGs are doing amazing things with unlimited tokens. Other companies have no clue what to do with tokens, they've just told their engineers to max them.

It really depends on how you're using the tokens. If you're just using them for Codex and Claude Code - yeah, tokenmaxxing is incredibly dumb.

Of course, the latest DeepSeek models are not as good as Claude, but they're not super far off either.

Agents are expensive in large part because tool calls require round trips. It's because these APIs are stateless and not streaming so you have to resend the whole context each time. This means you have roughly #tool calls x 1/2 context size cached input tokens over any given session. Most API providers overcharge you by a huge amount for cached tokens. A exception being Deepseek. Paying OpenAI $0.05 for 100k cached GPT5.5 tokens during a possibly 2 second round trip agent tool call is like paying $100/hr for what is likely to be ~10 to 20 GB of VRAM residence (holding the KV cache).

Or it got offloaded to NVME and you are paying $0.05 for that much PCIe bandwidth.

It's especially a crazy assumption to make relative to the costs of employing a human. The costs of paying an entry level employee are unlikely to go down at all, and even if those costs do decline, there's a floor they can't drop below (minimum wage at the extreme end), whereas companies are free to optimize agentic costs as close to zero as possible.

So you are assuming that a cost which is extremely susceptible to optimization but which no one has yet seriously attempted to minimize will remain perpetually above a cost which is much less susceptible to optimization, is already subject to enormous efforts to minimize, and has a legally mandated floor. That seems like a bad bet.

I’ve spent $10-$20 a day using Claude to write code and closer to $5 a day now that I mostly use Deepseek and GLM, using API pricing (no subscriptions) since I don’t use Claude Code.

This is a rounding error for a company. So I think there’s plenty of room to use AI extensively while being more cost-conscious.

I also don't think that blitz scaling will work like with Uber. The engineers are still there. We can work without the LLM tools.

I'd imagine GPT-5.5 and Claude Opus 4.7 could run just fine on a 16x H200 node and serve at least 10 heavy users without the token output getting choppy.

The financials don’t make sense now. Based on the expenditure the finances won’t ever make sense.

The former is the issue, and how many companies have been operating. It's like a trucking company ranking driver effectiveness by fuel used instead of by cargo moved.

But on a more serious note, do we know how much Uber spent per technical employee/month? I assume it is far more than even any of those $200 "max ai" plans.

And the other question is how much the public would be willing to spend, in my estimation this is as "cheap" as it will ever get (main-stream at least).

Adds nothing insightful to these discussions.