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40 GigaChat Case Studies vs the Benchmark: Checking Sber's Numbers

23 min read

Sber, Russia’s largest bank and the company behind GigaChat, released a sponsored showcase: forty business cases from companies that deployed GigaChat and reported the results. EdTech, MedTech, HRTech, cybersecurity, PropTech. Polished cards, concrete numbers, real startups.

Sber’s promotional project

On the image: the “One step ahead” promo slide from the Sber500×GigaChat accelerator – 40 startups across 9 industries. Claimed effects: business processes up to x16 faster, costs down by up to 90%, up to 95% task automation, and revenue up by up to 30%.

We have a benchmark of our own: 29 models, 4,308 independent evaluations on managerial tasks. In it, GigaChat sits dead last – 29th out of 29 after the second wave of testing. That creates an interesting situation.

Not because Sber is lying. The cases are real, the startups exist, the automation works. The question is different: was this the optimal model for the tasks they were solving?

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40 GigaChat Case Studies vs the Benchmark: Checking Sber's Numbers
280x Cheaper in Two Years: The AI Economy Has Flipped
9 min

280x Cheaper in Two Years: The AI Economy Has Flipped

In 2023, a single query to GPT-4 cost enough that you had to count carefully. In 2025, the equivalent query became 280 times cheaper. Not 280 percent – 280 times. In two years, the cost of using AI went from a barrier to a rounding error.

Stanford AI Index – the annual report that compiles data on the AI industry from hundreds of sources – flagged this collapse in its 2025 edition. The 2026 report added context: AI investment exploded to $285.9bn, consumers are extracting $172bn of value a year, and data centres are eating electricity at the scale of New York State. The economy flipped – just not the way most people expected.

4 Prompt Engineering Techniques Tested on 7 Models: Workshop Guide
30 min

4 Prompt Engineering Techniques Tested on 7 Models: Workshop Guide

“Analyze this project and give recommendations” – one prompt, seven models, and GPT-5.4 produced 2,231 words of vague advice while Claude Sonnet delivered 11 complimentary phrases like “excellent budget structure.” Rewriting the prompt using a five-element structure brought all seven models down to 346–443 words, and the praise disappeared. Token savings: 41% to 79% depending on the model.

This isn’t theory. This is data from a “Prompt Engineering in Practice” workshop I ran at the IIBA conference. One project brief, four techniques, seven models, 28 runs – and $0.054 total. Cheaper than a vending machine coffee.

99% Quality at 1.4% of the Price: What's Wrong with the AI Model Market
8 min

99% Quality at 1.4% of the Price: What's Wrong with the AI Model Market

Most managers pick an AI model the same way: grab the most expensive one available. The logic makes sense – pricier means better. That’s how enterprise software worked for the last twenty years.

The AI model market in 2026 works differently. The cost per query ranges from $0.0001 to $0.17 – three orders of magnitude. And the actual quality difference between the top ten models? 0.24 points on a five-point scale. Meanwhile, Wharton / GBK Collective reports that a third of corporate AI projects never get past the pilot stage. And Epoch AI shows that only 5.6% of users apply AI in any genuinely deep way.

Maybe the question isn’t which model is best, but whether paying a premium delivers proportionally better results for typical management tasks.

We tested it. The answer was harsher than we expected.

How to Get the Most Out of YandexGPT: What Works and What Doesn't
13 min

How to Get the Most Out of YandexGPT: What Works and What Doesn't

Millions of people in Russia use Alice every day – not because they choose to, but because it’s free, built into Yandex Browser, and works without a VPN. YandexGPT, the model under Alice’s hood, is the best Russian model in our benchmark, but it’s still a long way behind GPT-5.4.

Can you get answers from it that come close to GPT, if you learn how to ask the right way? We tested exactly that in an experiment: ten prompting techniques, six management tasks, two independent LLM judges. The short answer: yes, you can – but not every technique works, and some make things worse.

Below are the concrete templates you can copy into the chat right now, and the anti-patterns to steer clear of.

Local LLMs for Managers: What You Can Actually Run at Home
20 min

Local LLMs for Managers: What You Can Actually Run at Home

Anyone who has spent enough time with ChatGPT or Claude eventually asks the question: can I run something similar right on my own laptop – without a subscription, without data leaving the machine, without depending on someone else’s servers?

In 2026, the answer is yes – but the caveats matter more than the answer.

This article is for people already using cloud LLMs who want to understand what local execution actually gives you, what hardware you need, and where expectations break down. No deep technical dive, but concrete numbers.

P5.express and Agentic AI: Where It Helps, Where It Breaks Things
24 min

P5.express and Agentic AI: Where It Helps, Where It Breaks Things

In the PMI world, portfolio management is typically imagined as something monumental: steering committees, Tableau dashboards, hundreds of Jira fields, weekly status meetings with a deck of slides. P5.express offers a different approach. Three cycles, five documents, two roles. The entire system fits on a single page.

This is exactly the kind of system where agentic AI makes sense: minimalist architecture that’s easy to understand, clearly defined roles, structured data. But “makes sense” doesn’t mean “everywhere.” Some parts of P5.express stop working when automated – not because the AI is bad, but because those parts derive their value from the human process itself.

Below is a cycle-by-cycle breakdown. What’s worth delegating to an agent, what’s better left to people, and which model fits these tasks best.