🧠 AI didn’t just replace jobs.
It rehired people — to train their replacement.

As companies deploy AI to write, diagnose, analyze, and edit, many professionals have already lost their full-time roles.
What comes next is more subtle: the same people are brought back as short-term contractors — not to do the job, but to teach AI how to do it better.

Doctors review AI-generated medical notes.
Lawyers check legal reasoning written by models.
Editors polish AI texts they once wrote themselves.

This is no longer “human + AI collaboration.”
It’s a transition phase: human as quality control for a system designed to outgrow them.

The work pays — for now.
But its purpose is temporary by design.

AI still makes mistakes.
And those already displaced are the ones fixing them — accelerating the moment when even that role disappears.

GPT-5.2 Pro has solved its fourth Erdős problem.

Mathematician Terence Tao described the result as “perhaps the most unambiguous so far” in terms of the uniqueness of the approach.

The author of the solution (if we can even call a human that — given the problem was simply fed into ChatGPT 🤔) claims that no prior solutions existed at all.
That’s not entirely true: forum users point out draft proofs in the literature from 1936 and 1966. However, Tao emphasizes that GPT-5.2’s method is fundamentally different from those earlier attempts.

Now the obvious question remains:
how will GPT-5.2 surprise us once the Erdős problems finally run out? 😏

Forum discussion:
www.erdosproblems.com/forum/thread/281?order=oldest

@science

Last night’s strong geomagnetic storm painted the sky with an unusually rare red aurora — and from the International Space Station it looked like the crew was literally flying through the glowing curtain, Russian cosmonaut Sergey Kud-Sverchkov said.

Why the red? Green auroras typically glow around ~100 km altitude, but red emissions come much higher (~300–400 km), where the atmosphere is thinner and it takes more energy to light it up — which is why this color is far less common.

#SpaceWeather #Aurora #ISS #SolarStorm

We all need humor sometimes

Mom says: “Since AI bots will kick office plankton out of offices, you should go to a farm and harvest crops — AI won’t be a problem there.” 🤝🌾

Meanwhile, a farm owner in China — who used to hire people to pick the harvest — is watching this:

Robots now pick fruit, navigate rows, detect ripeness, and work day/night.
So yeah… the “safe haven” plan might need a Plan B. 😅🤖

AI-projects

#humor #farms #robots

The recent AI boom, combined with long and quiet winter holidays, unexpectedly resulted in a short piece of speculative fiction.

It’s not about evil machines.
It’s about responsibility, optimization, and the moment when systems designed to assist humans quietly begin making decisions instead of them.

The text is available in EPUB and FB2 formats.

Feedback is simple:
👍 — if it resonates

Other options are not currently supported.

2026 is the year AI stops playing — and starts becoming infrastructure

This isn’t hype. It’s a structural shift.

IEEE Computer Society has consolidated its outlook into 26 key technology trends for 2026, and almost all of them point to the same idea:
AI is no longer a feature or a tool — it’s becoming a new economic layer, comparable to electricity, the internet, or cloud computing.

⸻

What we’ll see in the real world (not just demos)

AI & the Future of Work
AI agents become standard “team members” across most office jobs.
Competitive advantage shifts from headcount to intelligence leverage: one human + multiple agents > a large department.

Wearable AI devices
New “always-on” form factors push AI into everyday life — and sharply raise privacy and surveillance concerns.

AI-generated content
The most mature and widely deployed area: video, music, presentations, documents.
The concept of authenticity takes a direct hit.

Social AI
Assistants learn soft skills:
reading emotions, adjusting tone, negotiating, de-escalating conflict.

Embodied / Physical AI
Robots, drones, and autonomous systems scale across manufacturing, logistics, and urban infrastructure.

Autonomous driving & robotaxis
Autonomy shifts toward capital-intensive, dense urban services, powered by heavy compute and training via digital twins.

⸻

How work and the economy transform

The firm is no longer “a group of people”
It becomes people + agents.
This is stated explicitly in the AI & Future of Work forecast: agents as standard members of teams.

Jobs dissolve into functions
The labor market moves away from professions toward tasks and outcomes.
“Future of coding” and “vibe coding” mean software is produced by non-developers — code becomes a byproduct of intent.

The real bottlenecks: energy and trust
AI scaling hits two hard limits:
• power generation and data-center energy consumption
• identity, data provenance, and control

IEEE puts it bluntly: adoption bottlenecks = Trust + Power.

Skills that matter
Reskilling isn’t just technical.
Critical thinking, adaptability, communication, collaboration, and change management rise in value.

⸻

The most important directions for science & deep tech

AI-driven scientific discovery & robot scientists
High risk–high reward: accelerated science, paired with risks of false optimization and misplaced trust.

In-memory computing & new processors
The real enemy of AI isn’t compute — it’s data movement and energy loss.
Radical gains must come from performance-per-watt, not raw FLOPS.

Quantum-safe cryptography & trust infrastructure
Preparing for post-quantum threats while building scalable digital trust layers.

AI-enabled digital twins
Savings via simulation instead of replication: predictive maintenance, system optimization —
with new vulnerabilities and accountability challenges.

Future of medicine & engineered therapeutics
According to the authors, medicine carries the largest potential impact on humanity, with bioengineered therapies entering the core technology stack.

⸻

The key takeaway

AI is no longer “about the future.”

It is becoming infrastructure of the present —
with its own power requirements, trust layers, governance, and social consequences.

The real question is no longer “Will AI happen?”
It’s “Who controls energy, data, and trust in an AI-driven world?”

Source: IEEE Technology Predictions 2026


#AI #Science #FutureOfWork #Robotics #DigitalTwins #Infrastructure #Medicine