Summary of GPT-5 presentation without marketing pixie dust

1. The LLM has become, on average, slightly (by a few percent) smarter.
2. In some aspects, the LLM has become significantly smarter (by tens of percent).
3. In some aspects, the LLM has become a bit dumber(!).
4. The API has become cheaper, or not, — it depends on how you use it.
5. OpenAI is deliberately misleading people about the capabilities of the new model — see the screenshots.

=> The world's LLM leader has begun to bog down, and the rest will likely follow.

When everything is going well and you make another breakthrough, you don't cheat with the pictures.

This doesn't mean that progress has stopped. Still, it does mean that the growth of technology is transitioning from the explosive phase of "discovering new things" to a more or less steady phase of "optimizing technologies in a million directions, where there are only enough hands for a hundred."

We are close to the "disillusionment" phase of the Gartner hype cycle.

In this regard, I would like to remind you of my AI future prognosis — so far, it's holding up.

Let me add a few more thoughts.

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Payment processors cancel games

Apologies for the clickbait headline — unfortunately, that's exactly what's happening.

Just because not all games are being banned right now, or not the ones you personally play, doesn't mean your favorite shooter won't be banned in a year or two because it "doesn't align with the values" of some deputy deputy's mistress (or lover) at MonopolyPayCard.

Let me tell you about:

• What happened.
• What I think about it.
• What I want to advise those who engage in such nonsense.

I strive to minimize the use of harsh language in this post, but I've kept some, as it most accurately conveys my opinion about the situation. This post, like the entire blog, expresses my personal opinion. If you can't or are not allowed to read harsh language, please skip this text.

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Vantage on management: Hypothesis testing loop

We generally assume that if our product has such and such attributes, then people will be willing to pay such and such money for it.

Sometimes we make assumptions explicitly:

If we paint the sign red and light it up, the number of evening visitors to our restaurant will increase by 10%.

sometimes we do it implicitly:

I'll build the coolest theme park on the Moon with blackjack and AI.

but I'd venture that one can't avoid assumptions altogether.

These assumptions we call hypotheses.

Based on these hypotheses, we modify the product, measure the results, and then decide which hypotheses are right and which are not. The correct hypotheses remain in our worldview and product; we abandon the incorrect ones. After that, we form new hypotheses and repeat the cycle.

This cycle runs perpetually while the product exists and is a special case of the feedback loop.

There's a wealth of literature on this cycle, its implementation, and its various nuances. The best practices are packaged into kits for every taste [ru]: from intricate system engineering to Lean Startup and Agile. The cycle itself is known by dozens of acronyms: PDCA, OODA, DMAIC, 8D — too many to count — every self-respecting guru-consultant with a book under their belt adds a new one.

Kicking off the feedback loop is absolutely crucial — without it, the product is toast, like any other endeavor.

However, it's not enough just to launch the loop — what you do within it is just as important.

The latter isn't covered as thoroughly in the literature as it should be. One thing is to look at simplified cases in books. Another is to steer a real-world product in real-time, dealing with all its complexity, opacity, confusion, delays, data gaps, and so on.

That's why, in this long essay, I'll try to delve deeper into one specific stage of the feedback loop that's often unjustly overlooked — hypothesis synthesis.

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Vantage on management: Points of view on a product

Let's imagine you are developing a SaaS — a Tamagotchi-style to-do list for gamers, or even something less useful.

This SaaS is your product — what you offer to users — what they pay money for.

But what exactly are you offering? Is it just the ability to feed to-do lists to the pet?

Beyond the obvious attributes, such as specific functionality, appearance, and a branded joystick in the package, your product has numerous less-obvious attributes that are also important to users and thus impact its success.

Let's start by looking at these attributes and follow a logical chain through increasingly holistic system-level viewpoints on the product and its management.

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On the roles of function and selection in evolving systems

Recently, I encountered the scientific paper On the roles of function and selection in evolving systems (DOI: 10.1073/pnas.2310223120).

Michael L. Wong with co-authors concisely describes the phenomenon of evolution as a universal process inherent to our universe (not just the biosphere of planet Earth), and they do so within the context of a systems approach.

Their ideas resonate with my worldview, so I decided to summarize the article for myself briefly and, of course, for you.

And suppose you think that understanding evolution is unnecessary. In that case, I recommend you reconsider, as evolution affects not only hedgehogs and monkeys but also software, work teams, countries, ideologies, and even the thoughts in your head [ru] — understanding how all this works is essential.

The authors define an "evolving system" as a collective emergent phenomenon of many interacting components (subsystems of this system) that manifests as an increase in their diversity, distribution, and patterned behavior as time progresses.

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