YandexGPT is a family of generative language models from Yandex, available through Alice, Yandex Browser, the Yandex Cloud API, and other ecosystem services. By February 2026, Yandex introduced the new Alice AI model family, which together with YandexGPT forms a powerful platform for working with the Russian language.
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Grok by Elon Musk’s xAI is an AI system that stands out from competitors (ChatGPT, Claude) with minimal content filters and direct real-time access to X (Twitter) data. By May 2026, Grok has evolved beyond a chatbot into a full agent ecosystem with native video processing, deeply integrated into Tesla vehicles. The latest model – Grok 4.3 – launched April 30, 2026, bringing a 1M-token context window, 58% price cuts, and native video input support.
Gemini is Google’s generative AI ecosystem, which by February 2026 has become a key tool for companies deeply integrated with Google services. Its main advantage is seamless integration with Google Workspace (Docs, Sheets, Gmail, Drive) and instant access to real-time data through Google Search.
By mid-2026 the AI-tool market has grown specialized: ChatGPT’s grip has loosened, and professionals work with a “stack” of several models rather than a single one. Perplexity has carved out the niche of a smart navigator in an ocean of information – and over the past year it has grown from a search box into a browser and a working assistant.
Anthropic ships Claude updates almost every two weeks. Behind the version numbers, what matters to a manager is what actually changes in the work. Two such events happened by the summer of 2026. On May 28, the new flagship Claude Opus 4.8 shipped – its strength is reliability: it judges its own work more honestly and is less likely to deliver a confident but wrong result. And on June 9, Anthropic opened access to a Mythos-class model for the first time – Claude Fable 5, but within 96 hours it had to be taken offline at the demand of the US government. That story is a useful lesson about the limits of generative AI, and we cover it below. A context window of 1 million tokens has long been available to all users at no extra cost.
By 2026, calling ChatGPT just a “chatbot” feels off. It’s a working platform with several models, search, deep research, an agent mode, image generation, Codex for development and Sora for video. And precisely because of that, newcomers find it harder to grasp the main thing: what among all of this does an ordinary manager actually need, and what still matters only to power users and tech teams.
This version of the article is updated as of April 8, 2026: I’ve removed the models and tariffs that no longer apply, double-checked launch dates and kept only the changes that truly affect day-to-day work.
On 11 February 2026, at a government meeting, President Tokayev publicly criticised KazLLM. The model, launched with great fanfare in December 2024, has just 600,000 users – 3% of the country’s population. For comparison: 2.6 million people in Kazakhstan use ChatGPT. The president was blunt: KazLLM “cannot compete with ChatGPT.”
This statement cuts to the heart of the matter. Why does Kazakhstan need its own language model if global solutions work better? And if sovereign AI is necessary – why is it losing?
The answer is more complicated than it seems. Because KazLLM is not “Kazakhstan’s ChatGPT.” It’s a fundamentally different tool with a different mission. Comparing them is like comparing a national power plant with an imported household appliance.
In February 2026, the investment research newsletter Citrini Research published a scenario that flips the usual logic on its head. Bears typically predict that AI will underdeliver. Citrini asks a different question: what if AI delivers on every promise – and that’s exactly what causes the problem?
Their piece «The 2028 Global Intelligence Crisis» is a fictional memo dated June 2028. Not a forecast, but a stress test: what happens to the economy if machine intelligence really does replace white-collar workers as fast as the developers claim?
“Why would I need your course when I can just ask ChatGPT?”
This question shows up in the comments under every post about AI education. The logic is bulletproof: the internet is free, ChatGPT will explain anything, YouTube is overflowing with tutorials. Why pay for a course when all the information is already out there?
It’s a great question. The answer is not the one people expect to hear.
Because between “having access to information” and “knowing how to work with it” – there is a chasm. Access to medical textbooks doesn’t make you a doctor. Owning a kitchen knife doesn’t make you a chef. And the ability to ask ChatGPT a question doesn’t make you an AI specialist. As MIT’s research on choice architecture shows, a specialist’s real strength lies not in having the tool, but in the ability to critically evaluate what the tool suggests.
And this isn’t just a neat metaphor – it’s a research-confirmed fact.
Not long ago I was putting together a proposal for a new client. The amount was unusual, the terms – likewise. My gut said: go with X, you know this market. But I decided to “check” with Claude. The model produced a well-reasoned answer with a different number – 15% below my estimate. It sounded convincing. I changed the number.
A week later the client signed without negotiation. And instead of satisfaction, I felt annoyed: what if my original number would have gone through too? I’ll never know – because at the moment of decision I suppressed my own judgment in favor of the algorithm’s “statistically grounded” answer.
This is the very pattern that Anthropic’s researchers call Disempowerment – loss of control. Not dramatic, not obvious. Just a quiet swap of “I decided” for “AI suggested.”