Cyberpunk remote work, IRL.

Operators in the Philippines—paid about $250/month—are remotely piloting shelf-stocking robots in Japanese stores.
Today it’s teleoperation; tomorrow it’s training data. The real question: how fast until the robots learn enough to cut humans out of the loop?

#robots #teleoperation #retailtech #AI #futureofwork #science

🤖 “Hand-motion farms” are real — and they’re training robot hands.

In parts of India, workers strap a small camera to their forehead and spend hours doing simple, tactile tasks: folding towels, packing boxes, sorting everyday objects.
The POV videos go to U.S. labs, where neural networks study exactly how human fingers grip, pull, twist, and place—so robots can learn to copy the same motions.

Why this matters:
• Dexterity is the bottleneck. Vision models are great, but robots still struggle with cloth, cables, zipper pulls, and irregular objects. Human POV data captures the micro-moves that simulators miss.
• Imitation learning at scale. Hour after hour of clean, labeled hand maneuvers becomes training fuel for policies that generalize to new objects and tasks.
• Societal twist. It’s efficient—and a little dystopian: people meticulously teach the fine motor skills that may one day automate their own work.

Humans teaching their replacements, one folded towel at a time.

#AI #robots #imitationlearning #India #futureofwork

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

🚨 #QuitGPT? A movement is urging people to cancel their AI subscriptions

A new campaign called “QuitGPT” is gaining traction online — encouraging users to cancel their paid ChatGPT subscriptions as a form of protest.

According to a recent report by MIT Technology Review, the movement frames subscription cancellations as a political and ethical statement. Supporters argue that advanced AI systems are becoming deeply embedded in power structures — and that consumers should push back using the one lever they control: their wallets.

So what’s actually happening?

• Activists are calling for users to unsubscribe from services developed by OpenAI
• The campaign is spreading across social platforms, with users publicly announcing cancellations
• Critics question AI governance, transparency, and leadership decisions
• Others argue that boycotting AI tools may slow innovation — or simply push users toward alternative models

This isn’t just about one product.

It’s about a broader question:
👉 Who shapes the future of AI — engineers, governments, corporations… or users?

We are entering a phase where AI is no longer experimental. It’s infrastructure.
And when technology becomes infrastructure, it inevitably becomes political.

Whether the QuitGPT campaign grows or fades, it signals something important:
AI is no longer just a tool. It’s a societal force — and people are starting to treat it that way.

What do you think?
Should users influence AI development through market pressure — or is engagement the better path?

#AI #Technology #Ethics #FutureOfWork #DigitalSociety

🔬 Anthropic Study: AI Could Already Do a Quarter of Our Work — But Humans Rarely Use It Yet
@science

📝 A new analysis from Anthropic’s Economic Index looks at millions of real interactions with the AI assistant Claude to understand how AI is actually used at work today — and how much more it could do.

📊 Key insight:
There’s a huge gap between AI capability and real-world usage.

What the data shows:
▪️ Around 44–49% of jobs contain tasks that AI could already assist with.
🔹 At least ~25% of tasks in the U.S. economy are technically accessible to current AI systems.
▪️ But most of those capabilities remain largely unused in practice.
🔹 When AI is used, it usually augments humans rather than replacing them.

In other words:
AI could already do far more work than it currently does — but adoption is still catching up.

📈 If widely adopted, current-generation AI could increase labor productivity growth by roughly ~1–1.8 percentage points per year, potentially doubling recent productivity trends.

💡 The implication:
The real transformation may not come from new AI breakthroughs — but from people gradually using the tools that already exist.

💬 Question:
Which tasks in your job could AI already handle today — but nobody is actually using it for yet?

🔗 Source:
https://www.anthropic.com/research/labor-market-impacts

#AI #FutureOfWork #Anthropic #Productivity #Technology