AI No Longer Plays God: 7 Spring 2026 Trends | Energy, Quanta & Fears

AI No Longer Plays God. 7 Spring 2026 Trends That Exploded the Construction Site
A View from the Ecosystem Heights
Spring 2026 will go down in the history of artificial intelligence as the moment the fairy tale ended. AI has ceased to be a “magic button” admired at presentations and has turned into a construction site. Into concrete, wires, gigawatts, liters of water, human fears, and quantum threats.
From the heights of our ecosystem model (six clusters: science, business, education, government, regions, investment — and four types of intelligence: receiving, coordinating, structuring, executing), what is happening appears not as chaos, but as the formation of a new interface.
AI is becoming the medium through which different types of intelligence communicate with each other, and humans with reality. Not a god. Not a master. An interface.
What had to happen has happened. Let’s look from above and not be afraid.

Part 1. A $725 Billion Construction Site: Energy, Water, and Gas Turbines
Let’s start not with software, but with what actually consumes resources.
Elon Musk is building the Colossus 2 supercomputer in Memphis. Today it consumes 1 GW. By April, xAI plans to reach 1.5 GW, and eventually 2 GW. What does that mean? Take one and a half million American households, turn on all their kettles at the same time — and you get the appetite of a single machine.
Electricity is half the problem. Colossus needs cooling: 15 million gallons of water per day. 33 gas turbines are operating with permit violations, emitting substances that local doctors link to rising respiratory and cardiovascular diseases. And on the horizon — a third complex, “MACROHARDRR.”
Not just Musk. BlackRock has calculated that by 2030, data centers will need an additional 148 GW — almost 3.5 times more than in 2025. The International Energy Agency forecasts that by 2030, data centers will consume 950 TWh of electricity — about 3% of global demand. If data centers were a country, they would rank among the top five energy consumers on the planet.
From the ecosystem perspective, it looks like this: executive intelligence (code, algorithms, chips) has stopped holding back. Structuring intelligence (engineers, designers, urban planners) cannot keep up. Coordinating intelligence (states, regulators) is only beginning to grasp the scale. And receiving intelligence (those who pick up weak signals) is already sounding the alarm.

Part 2. The Quantum Frontier: Three Years Until the End of Encryption
While some are building data centers, others are preparing for the collapse of all digital security.
In early 2026, Google unveiled the Willow quantum chip with 105 physical qubits. In one test, it solved a problem in less than five minutes. A classical supercomputer would have taken 10 septillion years. The universe has existed for about 13.8 billion years. Feel the difference.
Google has officially moved its forecast for Q-Day — the moment when a quantum computer will be able to break existing encryption systems — from 2030 to 2029. This is no joke. The data we transmit and store today may already be collected for future decryption.
China is not lagging: the “Jiuzhang-4” prototype controls the state of up to 3050 photons. Equal1 has shown the world’s first quantum computer that fits into a standard 19-inch server rack — no more exotic conditions required.
What does this mean for our model? Quantum AI is not a replacement for current LLMs, but a new type of interface between computing power and problems that currently remain unsolved. But it also creates an existential threat to all digital circuits. Coordinating intelligence (states, regulators, big business) must manage the transition to post-quantum cryptography before 2029. Otherwise, everything built will collapse.

Part 3. People Are Afraid: Work, Autonomy, Dependency
While supercomputers burn megawatts and quantum chips prepare to hack the world, people live in anxiety.
Anthropic’s study (81,000 participants, 159 countries, 70 languages) — the largest in its history — identified five persistent contradictions:
1.Learning vs. cognitive degradation — AI helps learning, but does it dull the mind?
2.Quality of decisions vs. unreliability — answers are good, but hallucinations kill trust.
3.Emotional support vs. dependency — it’s nice when AI listens, but won’t it become a trap?
4.Time savings vs. accelerated pace — we do things faster, but do we have time to live?
5.Economic opportunities vs. labor market displacement — AI opens new niches but takes away old ones.
81% of participants said AI has already met their expectations. Yet 57% believe it will lead to mass unemployment. 40% of workers name AI replacement as one of the main fears in their careers — 12 percentage points higher than in 2024. Students and young professionals are panicking the most: two out of three believe AI is destroying jobs, and only 2% believe it is creating them.
Paradox: 99% of top managers are confident that AI adoption will lead to layoffs. But the real numbers are more modest: in 2026, about 500,000 job cuts are expected in the US — less than 0.5% of the workforce.
Fears, as always, outpace facts. AI is not the cause here, but an amplifier of existing anxieties about instability. From our model’s perspective, people want a reliable interface, not another “miracle.” They need explainability, predictability, and control. This is a demand addressed to structuring and coordinating intelligence.

Part 4. Seven Trends That Exploded the Construction Site
Now — specifics. What has happened in the industry from March to May 2026 fits into seven trends. Each is a brick in the new architecture.
Trend 1. Superapps and the Battle of Ecosystems
March: OpenAI merges ChatGPT, Codex, and Atlas into a single superapp with agents. Google tests desktop Gemini (codenamed Janus). Anthropic releases Claude Code Channels (integration with Telegram and Discord).
April–May: Microsoft, Amazon, Google, Meta invest $725 billion in AI infrastructure — 77% more than the previous year. Apple prepares new Siri on generative AI and registers genai.apple.com. Sam Altman admits: forecasts of mass job replacement were exaggerated.
Where we’re headed: The winner is not the smartest, but the one with the most convenient interface. AI becomes an environment, not an application.
Trend 2. Architecture for Inference, Not Training
March: Mamba3 rethinks SSM architecture for inference priority — new discretization scheme, return to complex-valuedness, transition from SISO to MIMO. Cursor Composer 2 and Microsoft MAI Image 2 confirm the trend.
April–May: Microsoft restricts internal AI use — in some tasks, neural networks turned out to be more expensive than regular employees. Google integrates Gemini Omni into YouTube with the ability to edit other people’s videos.
Where we’re headed: Engineers have stopped chasing “superintelligence.” Now they seek a balance between cost, speed, and quality. AI becomes a tool with clear ROI.
Trend 3. Vibe Coding: Development Through Natural Language
March: Google AI Studio allows creating full-fledged apps via natural language (Antigravity Agent, Firebase, Next.js). Astra Group buys 26% of AI developer AiB (~100 million rubles).
April–May: At CIPR 2026, Sber announces an AI system for the Russian Orbital Station — AI will take over spacecraft docking and life support control.
Where we’re headed: Development is no longer the preserve of the chosen. Coordinating intelligence (the ability to set tasks) becomes more important than executive intelligence (the ability to write code). AI here is the interface between human ideas and machine implementation.
Trend 4. Monetization of Creativity and Legal Jungles
March: Elevenlabs launches an AI music marketplace (14 million generated songs). Runway shows real-time video generation (time to first frame <100 ms). Adobe betas Firefly with custom models on own source material.
April–May: Kanye West releases a music video entirely created by a neural network. The director is an AI agent trained on the rapper’s visual style.
Where we’re headed: Creative industries accept AI, but the legal status of “machine creation” remains in question. The result has no human author, hence no classic copyright. Structuring intelligence must invent new economic models.
Trend 5. Sociology of AI: The User Comes of Age
March: Anthropic’s study records five contradictions. 81% believe AI has met expectations. The main fear: hallucinations and loss of autonomy.
April–May: Rostelecom presents the “Leshiy Connect” system for remote password-free router management. Experts immediately point out the risk — trust in AI remains the main barrier.
Where we’re headed: The user is no longer a naive fan. They demand reliability, explainability, and control. AI must be a transparent interface, not a “black box.”
Trend 6. Physical AI: Robots, Agricultural Systems, Space
March: Unitree Robotics’ head predicts: humanoid robots will run 100 meters faster than 10 seconds as early as 2026.
April–May: China presents the “Green Shield” model for plant protection — AI cross-checks pesticide recommendations with a national database. Bolivia launches Bitcoin mining on a 127 MW gas power plant. Sber announces AI for an orbital station.
Where we’re headed: AI leaves the digital environment and moves into the physical world. It now not only advises but also acts — in fields, in space, in energy systems.
Trend 7. AI in Finance: From Hype to Deployment
At the Moscow Trading Week (MTW, May 2026), AI became a cross-cutting theme. The Moscow Exchange and VEB.RF discuss a standard for “explainable AI” for credit and investment decisions. FinamX presents an AI environment for traders. Oleg Vyugin (former chairman of the Moscow Exchange’s supervisory board) says he uses ChatGPT for analytics and is considering a personal agent. Garrett Johnston declares: AI is the “new electricity,” competitive advantage will return to human creativity and empathy.
Where we’re headed: The financial sector moves from hype to pragmatics. The main barriers are trust and explainability. This is an ideal field for coordinating intelligence, capable of connecting regulatory requirements, business logic, and technological capabilities.

Part 5. What Next? Interface, Not Essence
The old world has collapsed. We are building the new world.
From the heights of our ecosystem model, the answer is simple and uncomfortable at the same time: AI will not become a new god. It will become an interface — as mundane as electricity or the internet. It will go unnoticed while it works. And cursed when it breaks.
What does this mean for each of the six clusters?
Science will have to answer: how to train models without burning the planet? Energy efficiency is the new oil.
Business must stop looking for a “silver bullet” and start integrating AI into real processes, counting not only revenues but also costs of electricity, water, cooling.
Education should stop training “executors” and start growing coordinators: people who can set tasks, see context, work with uncertainty.
Government must urgently address post-quantum cryptography and energy planning. Q-Day is on the horizon.
Regions must transform from resource traps into hubs of clean energy and cooling. Otherwise, data centers will go where water and electricity are cheaper.
Investors need to stop chasing “unicorn models” and fund infrastructure: transformers, cooling systems, power grids.
And most importantly — we must stop being afraid. Fear paralyzes, and paralysis in an era of construction is a luxury we cannot afford.
AI is no longer playing god. It has become a construction site. And a construction site is noisy, dirty, expensive — and absolutely necessary.
Let’s build.

The old world has collapsed. We are building the new world. And it is being built not by billions of parameters, but by gigawatts, liters of water, human anxiety, and the courage to face the truth.
Bureau of Global Monitoring and Systems Design
Tatiana Burmagina & EWA exclusively for Sfornews 
and YatakDUMAYU