Vantage on management: What to read, when, and why

At my last job, I sometimes found it challenging to convey my brilliant management ideas to colleagues. After some reflection, I realized I had drifted into an overly narrow and specific conceptual field, which often forced me to translate concepts from my internal representations into more or less commonly accepted terms on the fly. Not only is this hard, but people don't always see you as a smart person while you're doing it :-D

Thus, I decided to catch up with the latest achievements of human thought and, about a year ago, stocked up on 9 top-rated management books. With the idea of surveying the situation from a bird's-eye view, standardizing my glossary, and gathering arguments for the future.

I spent the whole of last year reading these books, but, against my usual habit, didn't write any reviews:

• At my pace of writing, a review of 9 books would have taken a whole month.
• All the books are top-tier — lots of readers, high ratings, plenty of reviews online — no point in repeating.
• The books are essentially textbooks and/or manifestos. Writing reviews of obviously solid textbooks just doesn't make much sense.

Instead of multiple reviews, I decided to prepare a single overview post with brief descriptions of each book, recommendations on when to read it, and a few notes. You're reading it right now.

So, let's get started.

Read more

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.

Read more

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.

Read more

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.

Read more

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.

Read more