Statistics

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.

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?

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.

In April 2026, Stanford HAI released its ninth annual AI Index Report – the most comprehensive overview of the state of the AI industry. The headlines are predictably optimistic: corporate adoption reached 88%, generative AI outpaced the internet in diffusion speed, and the consumer value of GenAI tools is estimated at $172 billion annually. But look behind the numbers – and a familiar problem emerges.

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.

In November 2025, Stanford data surfaced an unexpected fact: Americans were using AI more at home than at work. We unpacked that in a mysummit.school piece here – and the takeaway then was blunt: work AI was still catching up to personal AI.

Four months later, Epoch AI teamed up with Ipsos for new research – and the picture has shifted.

In March 2025 at SXSW EDU, strategic foresight advisor Sinead Bovell delivered a talk on AI and the future of education. No hype, no panic. But with two studies that change how you should think about AI’s role in learning.

First: a group of students who used ChatGPT without restrictions scored 17% worse than the control group working from a textbook. Second: a different group, where AI was deployed within a fully redesigned instructional system, outperformed a traditional lecture by a factor of two.

Same tool. Opposite outcomes. The difference is in the approach.

This week, Sber unveiled GigaChat Ultra – a new flagship model with a reasoning mode (Thinking). The model is available for free via web, mobile apps, and a Telegram bot. We immediately added both variants to our AI model research for managers: ran them through all 32 scenarios using our unified methodology, scored them with both LLM judges, and compared against the other 52 models.

We conducted a large-scale study: 52 models, evaluations from two independent LLM judges, across 8 categories of management tasks. This is the most comprehensive Russian-language AI ranking for managers available today.

The question remains the same: which AI actually works for a manager in Russia – without VPN, without workarounds?