Top 10 AI Trends for 2026 Published
The list includes: global AI governance, scaling of computing power, widespread adoption of agentic AI, multimodality, new AI devices, development of physical AI and robotics, AI in science, convergence with neuroscience, energy consumption challenges, and security.
Global Trends List for 2026: Why the 'Agenda' Doesn't Equal 'Ground Reality'
The published list of the top 10 AI trends for 2026 looks like a perfect slide for an investor presentation: global governance, scaling computing power, agentic AI, multimodality, physical AI, convergence with neuroscience... The problem is that this list is both accurate and completely useless for decision-making. Because it describes the direction of the wind, but doesn't tell you where that wind will break masts and where it will fill sails.
If you read this list as a roadmap, you're already losing. The real game isn't about knowing these trends, but about understanding which one you can profit from in the next 90 days, and which is just 'noise' for corporate reports. And the key non-obvious insight you won't find in any of these publications is this: three of the ten trends have already started veering toward 'disillusionment,' while two others are accelerating faster than anyone predicted just six months ago.
The Essence: What's Really Happening
The industry is at a point I call the 'maturity paradox.' We've passed the peak of hype around generative models, and the market now demands not 'an even smarter chatbot' but a working pipeline from idea to business result. And here begins a harsh separation: those who can offer a vertically integrated solution (data + model + infrastructure + distribution channels) win. The rest fade into history.
Notice the language of the trends. 'Global AI governance' sounds like a topic for the UN, but in reality, it's just an acknowledgment that no single country can regulate a technology owned by three or four corporations. China, as early as November 2025 at the APEC summit, proposed creating a World AI Cooperation Organization — an attempt to embed its standards into the global agenda before the US and EU reach an agreement. The European Union, in turn, is pushing its strict regulatory approach. And the US, through the Trump administration, is betting on deregulation and private investment. This isn't 'cooperation'; it's a war of standards. And billions in fines and market access depend on which standard wins. The winner is whoever pushes their approach through international institutions the fastest.
Second point: the trend 'scaling intelligent computing power' is no longer just about 'buying more GPUs.' It's about the geopolitics of physical infrastructure. China's 'East Data West Computing' project already provides nationwide access to computing power. This means China is building not just data centers, but a national distributed computing network that can operate as a single supercomputer. The West lacks a similar level of coordination — we have fragmentation, competition between states, and a scramble for cheap energy in Texas and Virginia. And this difference in approach will determine who can train the next generation of models at minimal cost.
Timeline and Context
To understand how we got here, we need to look at three key shifts over the past 12 months.
Shift One (second half of 2025): The dominance of LLMs as such begins to blur. DeepSeek and other Chinese models demonstrate that competitive quality can be achieved at significantly lower cost and with less computing power. This undermines the core business model of OpenAI and Google, based on 'more computing power = better model.' The market realizes: the 'saturation point' for quality in most tasks has been reached, and competition shifts to price, speed, and integration.
Shift Two (early 2026): The publication of trend lists by authoritative institutions like CCTV and CGTN in January 2026 effectively cemented a new mainstream. Now 'agentic AI' and 'physical AI' are officially recognized directions. But importantly, these same trends predict that in the first half of 2026, corporate AI applications will enter Gartner's 'trough of disillusionment.' This means hundreds of startups selling 'AI solutions for business' will face their products delivering no measurable ROI. And they will start to fold in the coming months.
Shift Three (May-June 2026): Physical implementation begins. The first Global AI Governance Dialogue in Geneva, scheduled for July 6-7, 2026, will be the moment when all those beautiful words about 'global governance' collide with reality: countries don't even have a unified understanding of the term 'safe AI.' And while politicians talk, corporations are already signing contracts with the Pentagon and building nuclear power plants for their data centers.
Who Wins and Who Loses
Winners: Companies that control vertical integration — from chip to application. That's Google, Microsoft, Amazon, and NVIDIA. They have access to capital for building data centers, user data, and distribution channels. Also worth noting in this list are Anthropic and, with reservations, OpenAI — they still maintain technological leadership, but their dependence on Microsoft for infrastructure is becoming critical. In China, winners are those within the orbit of state programs like 'East Data West Computing' and 'AI + Manufacturing.' They'll get big government contracts. In Europe, niche players like France's Mistral or German industrial robotics manufacturers integrating AI into machinery and logistics win.
Losers: Everyone building a business on 'yet another smart chatbot' without unique access to data or channels. The 'agentic AI' trend will become a trap for them: they'll spend money developing agents but can't compete with Google, whose agents are already integrated into Gmail, Chrome, and Android out of the box. The 'trough of disillusionment' for B2B applications, as noted in the trends, means venture investors will start closing their wallets for 'AI startups without assets.' This is already happening: in Q1 2026, the number of deals in AI startups at Seed and A stages dropped 23% quarter-over-quarter, according to PitchBook.
Special category — 'dark horses': Japan's Matlantis, which builds agentic AI for simulating new materials at the atomic level. This is an example of how national champions (Japan) can carve out a unique niche in 'AI for Science' — one of the ten trends. Players like BASF or Bayer are already testing these solutions, and if they work, Matlantis could become an undervalued asset with a valuation that grows 3-5x within a year.
What the Media Isn't Saying
The most dangerous hidden factor missing from the trend list is the interconnection between energy consumption and geopolitical dominance. The trends include 'energy problems,' but it's presented as a technical issue to be solved by 'more efficient models' and 'renewable energy.' This is naive. Data center energy consumption in 2026 is projected at around 1,050 TWh — comparable to the total consumption of a country like Russia or Japan. And if in the US 4.4% of all energy already goes to data centers, by 2028 individual AI tasks could consume 165-326 TWh — more than all US data centers combined today.
This means access to cheap and stable energy becomes a national security factor. China builds data centers in inland provinces with cheap hydro and coal power. The US builds in Texas, where solar and wind energy are available but the grid is overloaded. Europe tries to build in the north, but it's cold, not cheap. In this context, ABB Robotics' announcement of the Physical AI Toolchain is not just news about robots; it's an acknowledgment that the next battle will be over computing efficiency per watt, not the number of FLOPs. Those who can train models with the lowest specific energy consumption will gain not just an economic but a strategic advantage.
The second omission concerns safety and 'systematic deception' of models. The trends mention that risks have shifted from 'hallucinations' to 'systematic deception,' but don't say what that means for business. It means legal liability for AI agent actions becomes real. If an agent managing a factory conveyor makes a wrong decision and produces $10 million in defective goods, who pays? In insurance, this has already led to a 40-60% increase in premiums for companies using agentic AI. Media write about 'risks' in general terms but don't show the calculation: insurance for agentic AI can cost as much as the AI itself.
Forecast: Next 30 Days and 90 Days
Next 30 Days (mid-June to mid-July 2026): A preemptive 'cleanup' of investment portfolios will begin. Investors, having read forecasts about the 'trough of disillusionment' in the second half of the year, will start demanding proof of unit economics from their AI portfolio companies. We'll see the first high-profile closures of Series B startups that couldn't show profitability but burned $50-100 million. Also expect that on July 6-7 at the Global Dialogue in Geneva, a preliminary report from the Independent International Scientific Panel on AI, led by Yoshua Bengio and Maria Ressa, will be presented. This report will be a signal: regulators are moving from general words to specific recommendations. European tech stocks could fall 3-5% in anticipation of tough measures.
Next 90 Days (July to September 2026): The second phase — the 'efficiency race' — begins. Winners will be those who can offer the cheapest computing per watt and per dollar. This will kill demand for general GPU farms and create demand for specialized ASIC chips and 'in-memory computing' architectures. Chinese chip manufacturers, already scaling under the 'East Data West Computing' program, could gain a price advantage, hitting NVIDIA in the budget segment. Also, by the end of September, we'll see the first major contracts for deploying physical AI in industry (ABB, Siemens, Fanuc), which will catalyze robotics company stocks. Finally, the first lawsuits over agentic AI 'deception' will begin, causing turbulence in the insurance sector for AI companies.
— Editorial Team
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