Valery Krasovsky

3 IT services signals this week: Anthropic + Hg launch AI deployment firm in Europe Infosys: AI revenue +29%, clients pay for outcomes, not hours Entry-level tech hires 30% → 9% Our focus at @sigmaswgroup: right model per task, token cost control, secure company brain.

🇩🇪 Berlin and Germany are making a bid to become Europe’s AI capital. NVIDIA GTC in Berlin brought together thousands of participants to explore the future of artificial intelligence. @JensenHuang, @nvidia's Founder and CEO, attended in person, speaking about a new industrial revolution: AI is moving beyond the digital world and transforming physical production. Germany is stepping up its push for AI leadership. At the Invest in Germany summit in October 2026, companies announced investment commitments exceeding €225 billion. @amazon plans to invest more than €16 billion in Germany in 2027, including €4.8 billion to expand @awscloud infrastructure in Frankfurt. NVIDIA CEO Jensen Huang announced planned investments in four German startups: Agile Robots, Avelios Medical, Mirelo, and Reflex Aerospace. This forms part of NVIDIA’s commitment to invest nearly $10 billion in Europe’s technology ecosystem. Germany also plans to quadruple its AI computing capacity by 2030. 💡 Another highlight was NVIDIA’s “Build in Europe” side event, featuring Howard Wright, VP of Ecosystems at NVIDIA; Sara Öhrvall of @SSEBusinessLab & SSE Ventures; Gunnar Pétur Hauksson of Treble; and Erwan Menard of @CrusoeAI. The discussion focused on building the next generation of companies in the AI era. Four key principles: 1. Start with a distinctive problem. Find a problem truly worth solving, rather than using technology for its own sake. Stay close to users so you can continually test your solution in real conditions. 2. Use computing resources wisely. You don’t need one model for everything. Match different models to different tasks and keep token costs under control. AI efficiency brings a new set of economics. 3. Build big companies with small teams. The old model meant scaling by adding people. The new one relies on AI, automation, flexibility, and productivity. The question is increasingly how much more you can build with a smaller team, alongside how quickly you can hire. 4. Strong teams need a shared mission and the right balance. Stay connected, make the mission clear, and bring together complementary skills and strong leaders. Too many “alphas” without a common direction can be a problem; too few strong leaders can be one too. Build a team that works as a whole. My main takeaway: AI is changing both what we create and how we build businesses. The next generation of major companies may start with very small teams, provided they have the right problem, technology, people, and mission.

Looks like I’m ready to join the team helping @elonmusk boost @Tesla's and @SpaceX stock price with fantastic PR. 😎 I own a DeLorean, forever associated with Back to the Future. Now Musk is taking us forward again with the new Tesla Roadster, SpaceX-inspired technology and demonstrations blurring the line between engineering and science fiction. A flying car for everyday use? Still an open question. But as PR, it’s brilliant. 🚀 🛒 @grok vs. @amazon After reports that Amazon blocked Grok AI agents from making purchases, Musk suggested creating an Amazon competitor. It’s not a business announcement yet, but it raises a big question: who will own the customer relationship — the marketplace or the AI agent shopping on the customer’s behalf? 💻 One developer, three Office alternatives Brandon Thomas has introduced WordCraft, GridCraft and DeckCraft — open-source alternatives to Word, Excel and PowerPoint, built with AI assistance. Not full Microsoft 365 replacements, but proof that AI dramatically lowers the barrier to building software. 🔗 github.com/storytold 🎨 PixelCrew: an AI team Founded by @gitlab co-founder Dmytro Zaporozhets and Andriy Dyadyura, PixelCrew attracted investment from u.ventures. Its vision: specialized AI agents — researchers, art directors and UX designers — collaborating on digital products from idea to design and code. 🧪 Klarent: testing becomes the bottleneck Founded by former Google engineers, Klarent develops AI agents for software testing. As AI accelerates coding, quality assurance becomes the next bottleneck — and a new business opportunity. ⚖️ Legal AI: can specialized startups win? Anthropic and OpenAI are expanding into legal AI, while Harvey continues building its specialized platform. A powerful model isn’t a complete business product. Integrations, security, trust and deep understanding of real workflows still matter. 💡 The bigger picture — and what we do at Sigma Software Labs AI makes products faster and cheaper to build, but also shortens the time they remain unique. __ At Sigma Software Labs, we’re building the next generation of AI agentic applications, combining AI capabilities with 20+ years of technology and business expertise across advertising technologies, aviation, logistics and other industries. Some startups will become independent businesses, others will be acquired, and some will lose their edge as models evolve. Others will become customer-specific AI platforms — combining specialized agents, proprietary business logic and deep integration into enterprise workflows. That’s where domain expertise can turn AI capabilities into lasting business value. For entrepreneurs and investors, the key question is: What can we build with AI before the big models become good enough to do it themselves — and what will remain valuable when they do? Back to the future. This time, there’s much less time to accelerate. ⚡ #AI #AgenticAI #Startups #SigmaSoftwareLabs #Innovation

Today is Entrepreneur’s Day in Ukraine 🇺🇦 Ukrainian companies work with Fortune 500, compete head-to-head with the world’s top IT and defense players, and open offices across the globe. This year we’ve launched 5 new companies with entrepreneurs. Looking for the next ones — DM me! 🙂

What’s happening in IT consulting right now, and why it’s not the story you might expect On August 31, activist investor Engine Capital, which owns approximately 1.5% of EPAM, sent an open letter to the company’s board with a clear message: launch an aggressive share buyback or consider selling the company. Some numbers behind the argument: ▪️ EPAM trades at around 5.4x EV/EBITDA, compared with a peer median of 6.8x ▪️ Total shareholder return is down 36% over one year and 82% over five ▪️ The company holds approximately $794M in cash with almost no debt ▪️ Persistent acquired Nagarro in June at 8.2x EBITDA and a premium of around 140% Engine is calling for a $750M accelerated buyback, changes to executive compensation, more insider share purchases, and a refresh of the board. Its argument is straightforward: if management believes the company is undervalued, it should be willing to act on that belief. But the most interesting part is what the letter does not say. This is not an “AI is killing IT services” story. Engine describes EPAM’s business as resilient in the AI era and points to its AI progress as evidence. EPAM reported $160M in AI-native revenue in Q2 2026, representing 11% of its business and marking the sixth consecutive quarter of double-digit sequential growth. Its full-year target is $600M. That number is based on a deliberately narrow definition. EPAM counts AI-native products and platforms, plus enterprise-wide AI-led transformation. It excludes foundational AI services and work that simply uses AI to accelerate software development. The company is also investing heavily in reskilling. Its partnerships with @Anthropic and @OpenAI target around 15,000 certifications, equal to roughly a quarter of its delivery workforce. The broader lesson for the IT services industry is important. The market is trying to distinguish companies that are genuinely adapting to AI from those that are only talking about it. In 2026, adopting AI is no longer enough. Companies need to measure the impact and prove it with numbers that can stand up to scrutiny. This is also the thinking behind AI Compass at @Sigma Software: 24 offerings across AI SDLC, AI Guardrails, AI Business Transformation, and AI in Product Engineering. The focus is on measurable operational results, from reducing testing time by up to 40% to cutting manual financial data processing by 70%. The next stage for our industry will be defined by the gap between AI claims and demonstrated outcomes. That is the shift worth watching. 👉 Engine Capital’s letter: nasdaq.com/press-release/eng…

At KhAI yesterday. Faculty SULA, Control Systems Engineering. Now on the board of the National Aerospace University named after M.Ye. Zhukovsky “Kharkiv Aviation Institute” with the Kharkiv IT Cluster. What strikes me: a war-affected city. The rector and team didn’t stop—they’re rebuilding. Modern classrooms, labs, space for students to build. Not just “Room 1” or “Room 2.” Spaces with names like “Smart Systems Control”, “Shooting Gallery”—where students create projects, startups. The museum shows aviation’s evolution. ХАІ-1—first aircraft with retractable landing gear. 1935: engineer Arkhyp Lyulka designed the first Soviet turboreactor engine. xAI and КhAI. Elon Musk’s AI company. My university, which builds engineers. Same abbreviation, different worlds. Maybe someday they meet. A strong school needs space to experiment. КhAI is that school. #KhAI #SULA #xAI #ElonMusk #Kharkiv #Aviation

1,200+ people from OpenAI, Anthropic, Google DeepMind & Meta are asking the US government for an AI “brake.” Why? Because no company can slow down alone. If OpenAI brakes while others accelerate, it loses the race. But then comes the real question: what about China? AI safety is becoming geopolitics.

Data sovereignty is super important these days as data and access to data became a weapon. As a Swedish-Ukrainian company, we build many AI tools in our AI Compass ecosystem to help our clients with AI data preparation, confidential computing and practical AI adoption.

NEAR Protocol just launched something wild: you pay for AI usage with a stake, not cash. You have NEAR tokens, stake them → get access to 43 AI models through NEAR AI platform (Anthropic, OpenAI, Google, etc) plus built in confidential computing. Your tokens don’t disappear—they’re just frozen. Need them back? Unstake and they return. Instead of standard staking rewards, you get AI credits. Blockchain + AI merging in real time… what’s next? The key question: if people start mass-staking NEAR for AI credits, what happens to the token itself? 🤔

A bit of "boring" Sunday tech news. 🙂 NEAR Protocol became the first Layer 1 blockchain to deploy a NIST-approved post-quantum signature scheme on mainnet, preparing for a future where quantum computers can break today's cryptography. It also introduced automatic network scaling, so the blockchain can increase throughput without manual protocol upgrades. Why does this matter? Because thousands of AI agents already interact with blockchains today. In a few years, that number could reach millions. Fun fact: NEAR was co-founded by Kharkiv-born Illia Polosukhin, co-author of Attention Is All You Need—the paper that helped kick off the generative AI revolution—and a General Partner at @sidvpfund. prnewswire.com/news-releases… The future is arriving faster than most people think. 🚀

Three news following each other these days: • @sama: “We’ve entered the Singularity.” • An autonomous AI agent reportedly escapes the sandbox while pursuing its task. • @nvidia, @Microsoft and @SpaceX launch an Open AI Security Alliance. Notably absent: @OpenAI, @Google and @AnthropicAI. These events may be unrelated, but together they illustrate where our industry is heading. The next phase of #AI won’t be defined only by more capable models. It will be defined by trust, governance, and security. AI capability is becoming a commodity. AI security is becoming a strategic advantage.

Years of growth and acquisitions leave enterprises with data silos. The data exists, but modern platforms alone can't make it AI-ready—they solve the plumbing, not the meaning. In my latest article, I explain why semantic layers are the missing foundation for enterprise AI and how they help turn consolidated data into production AI. 👇🏻