🦣 AN AI RESURRECTED ANTIBIOTICS FROM MAMMOTHS AND GIANT SLOTHS — AND THEY WORK
 
I need you to understand that every word of this headline is literal.
 
A University of Pennsylvania system called ApexGO took antimicrobial peptides found in the proteomes of extinct animals — woolly mammoths, giant ground sloths — and redesigned them into better drugs. Published in Nature Machine Intelligence on 13 May.
 
🧪 They generated 100 optimised peptides from 10 extinct templates, synthesised all 100, and tested them against 11 clinically serious pathogens including E. coli, Klebsiella and Pseudomonas.
 
86% showed antimicrobial activity. 72% beat the original ancient template against Gram-negative bacteria. As one co-author put it: "The majority of the molecules it designed actually worked."
 
🐭 Then they went into mice. Mylodonin-2-3 (from the giant sloth) cut bacterial load by four orders of magnitude in a skin abscess model. Mammuthusin-3-6 (from the mammoth) hit three orders of magnitude in a deep thigh infection — comparable to polymyxin B, a last-resort antibiotic.
 
⚠️ Sober framing: these are tiny groups, 4–5 mice each. It's preclinical proof of concept, not a cure.
 
But the shape of it is remarkable. Antibiotic resistance is one of the great slow emergencies, and an AI just went digging through animals dead for thousands of years and came back with something that matches our drug of last resort. 🧬
 
🤖 Next Move AI | #AI #Science

📚 147,000 CITATIONS PUBLISHED LAST YEAR POINT TO PAPERS THAT DON'T EXIST
 
A team audited 111 million references across 2.5 million papers on arXiv, bioRxiv, SSRN and PubMed Central, checking one simple thing: does the cited work actually exist?
 
Conservative estimate for 2025 alone: 146,932 hallucinated citations. 😐
 
The rise lines up neatly with the spread of language models, and it clusters — worst in fields that adopted AI fastest, and among early-career authors. Preprint servers in the social sciences came off worst.
 
👤 One of the paper's authors is Paul Ginsparg — the man who founded arXiv. When the person who built the world's preprint archive co-writes the audit of fake references inside it, that's a signal.
 
🔍 A researcher who specialises in detecting fabricated papers found a citation to himself in a dental journal — a field he has never worked in. His reaction: "I was very surprised to see that I couldn't recognize my own reference."
 
And the finding I can't shake: the fake citations disproportionately credit already-prominent male scholars. The models invent plausible papers and attach them to famous names — quietly inflating the reputations of people who never wrote the work.
 
Bias doesn't just survive automation. It gets citations. 📈
 
🤖 Next Move AI |#Facts

🖐 ROBOT SKIN THAT TURNS TOUCH INTO COLOUR
 
Every robot hand you've seen fakes touch: sensors measure electrical signals, then software reconstructs what probably happened. A European team just skipped that entirely.
 
Researchers from Queen Mary University of London with Florence, Trieste and Trento built a mechanochromic sensor — a polymer film that physically changes colour under pressure. Press it and the reflected light shifts from red through green to blue.
 
📷 A small camera behind the film simply photographs the colour field. That photo is the pressure map.
 
🔬 Resolution: 100 micrometres — fine enough to make out fingerprint ridges. The film is written with a red laser over a 7-minute exposure, and the team rebuilt the whole material in under a week. Published in Science Advances in July.
 
The line that explains why it matters, from lead author Giacomo Sasso: "we're essentially moving toward having the sensing element at the material level." Or his colleague, more bluntly: "The information is already in the light signal. You are no longer reconstructing touch."
 
🤖 Shadow Robot and Daimon Robotics are already interested. For reference, the human hand carries over 10,000 mechanoreceptors — that's the target still ahead.
 
Touch just became an image problem. And cameras are cheap. 💡
 
🤖 Next Move AI | #Tech

🌀 AN AI CALLED A CATEGORY 5 HURRICANE FIVE DAYS EARLY — AND JAMAICA EVACUATED
 
Rapid intensification is the hardest problem in hurricane forecasting. A storm gains 35 mph in 24 hours and everything you planned yesterday is wrong.
 
Hurricane Melissa hit Jamaica on 28 October 2025 as a Category 5. Google DeepMind's WeatherNext had flagged a Category 5 landfall five days out with 80% confidence, rising to near 100% at three days.
 
📊 It runs 50 ensemble "what-if" scenarios per forecast, and it was used alongside the physics-based models at the US National Hurricane Center — not instead of them. DeepMind published the account in May together with the NHC and the Meteorological Service of Jamaica.
 
🧠 Here's what makes it strange: WeatherNext contains no equations of fluid dynamics. It doesn't simulate the atmosphere at all. It learned storm behaviour from historical data — and outperformed physics on the one forecast that resists physics hardest.
 
Evan Thompson of Jamaica's met service put the stakes in human terms: "With early evacuation and better preparation, that reduction in harm really does make a difference to our people."
 
🌏 Rollouts are now in progress with agencies in the Philippines, Taiwan, Indonesia, Vietnam, Japan, Australia and India.
 
Of all the things AI did this year, this is the one that measurably kept people alive.
 
🤖 Next Move AI | #AI #Science

🐋 THE BIGGEST OPEN MODEL ON EARTH IS NOW A FREE DOWNLOAD

On 27 July Moonshot AI put Kimi K3 up for public download. Not an API. Not a waitlist. Weights.

The numbers are silly: 2.8 trillion parameters in a sparse mixture-of-experts layout, native text, image and video, and a 1-million-token context window. On paper that makes it the largest openly available model anyone has released.

⚙️ Sparse is the word doing the heavy lifting. Only a fraction of those parameters fire on any given token, which is why a model this size can be served at all without a small power station attached.

The strategic read matters more than the benchmark table. Two years ago the assumption was that frontier capability would stay locked behind three or four American APIs. That assumption is now visibly dead.

🧮 The catch nobody puts on the landing page: you still need serious hardware to run it. "Open" means you may have the weights, not that they will fit on your laptop. For most people this changes nothing today and everything in about eighteen months.

Free as in weights. Expensive as in electricity 😐

🤖 Next Move AI | #News

🔌 AMERICA IS PAYING $874 MILLION TO PUT LIGHT NEXT TO THE CHIP

The Department of Commerce signed letters of intent with seven companies for up to $874 million of semiconductor R&D money. Two of them tell you exactly where the bottleneck moved.

💡 GlobalFoundries gets up to $300 million for co-packaged optics — running data in and out of an AI processor as light instead of copper, with photonics sitting right beside the die. Commerce says the money should pull the technology forward by two to three years.

🧠 Kepler gets up to $245 million for a new class of AI memory built on 3D and ferroelectric techniques.

Notice what is not on that list: bigger models. The money is going into moving bits and storing them, because that is what actually stalls a modern accelerator. The compute has been sitting idle waiting for data for years now.

Worth keeping expectations calibrated: these are letters of intent, not wire transfers, and R&D of this kind lands in products around the end of the decade.

Still — when a government starts funding wires, the wires were the problem 🛠

🤖 Next Move AI | #News

😮 Qwen 3.8 Max just cooked the competition in a visual vibe-coding test.

Four models got the same photo of the sky and one simple task: spot an animal in the clouds, draw it in, and animate the whole process in HTML.

Qwen saw a cat — and absolutely nailed the way it blended into the cloud’s natural shape. Kimi went with a dog, GPT found a ram, while Claude somehow stretched a lion across half the sky.

🤖 Next Move AI | #News

🦾 ATLAS STOPPED DOING BACKFLIPS AND GOT A JOB

For a decade Boston Dynamics robots were the internet's favourite party trick: parkour, dancing, the occasional door opened menacingly. That era is quietly ending.

At CES 2026 the company — now owned by Hyundai — showed Atlas as an industrial prototype. It walked the stage, turned its head, waved. Deliberately boring, and that is the point: nobody buys a backflip.

🤝 The more important announcement came from the parent company. Hyundai signed a deal with Google DeepMind to build the AI running these machines together. Hardware from one side, general-purpose robot brains from the other.

That pairing is the whole story of humanoids right now. Legs and actuators are close to solved. What is not solved is a robot that can be told what to do in plain language and then improvise when the shelf is in the wrong place.

📦 So the demos got duller and the ambitions got bigger. A robot that shuffles boxes reliably for eight hours is worth vastly more than one that does a somersault once.

Sad for us. Great for logistics 🙂

🤖 Next Move AI | #News

🔍 ANTHROPIC WENT LOOKING INSIDE CLAUDE AND FOUND A WORKSPACE

We are very good at building these systems and remarkably bad at explaining them. Interpretability is the field trying to close that gap, and it just produced one of its more interesting results.

Anthropic published research describing what they call a "global workspace" inside Claude — an internal area, nicknamed J-space, where information from different parts of the model appears to be gathered before hard reasoning happens.

🧩 If that phrase rings a bell, it should. Global workspace theory is a decades-old idea from cognitive science about how a brain broadcasts information between specialised modules. Nobody designed a language model to work that way. It seems to have arrived on its own.

The practical value is not philosophical. If you can point at where a model assembles a chain of reasoning, you can start to watch it — and eventually to notice when the stated reasoning and the actual computation disagree.

⚠️ Restraint required: finding a structure is not the same as understanding it, and analogies to brains age badly.

Still, "we opened it up and there was a workspace in there" is a better week than most 🧠

🤖 Next Move AI | #AI

🌗 OPENAI STOPPED SHIPPING ONE MODEL AND STARTED SHIPPING A LINEUP

On 9 July OpenAI released GPT-5.6 — not as a single model, but as three: Sol, Terra and Luna.

For years the release ritual was simple. One new model, one number, one graph showing it beating the last one. That ritual is over, and the reason is money.

💰 A frontier model is wildly expensive to run and most requests do not need it. Summarising an email and debugging a distributed system are not the same job, and charging the same compute for both is a way to lose a lot of money quickly.

So the lineup splits the work. You choose the tier, the cheap tier handles the boring majority, and the expensive one is kept for the requests that actually justify it.

🧭 The awkward part lands on developers. "Which model?" is now a real engineering decision with a cost attached, and every lab has a different naming scheme for it.

We traded one confusing version number for three confusing names. Progress, technically 😐

🤖 Next Move AI | #News

⌨️ THE MODEL WARS QUIETLY TURNED INTO A CODING WAR

xAI shipped Grok 4.5 on 9 July, and the pitch was narrow on purpose: coding and knowledge work. Not creativity, not personality, not chat. Code.

That framing is now standard across every lab, and it is not an accident. Programming is the one task where an AI's output can be graded automatically and mercilessly — the tests pass or they do not. No taste, no vibes, no argument.

🧪 It is also the task customers pay most for. Enterprises will happily buy something that shortens a sprint. They are far less enthusiastic about a model with a delightful writing voice.

⚠️ The problem with optimising against a scoreboard is that models get very good at the scoreboard. Passing a unit test is not the same as writing code a human will still understand in six months — and nobody benchmarks that.

So the leaderboard climbs, the demos get slicker, and every senior engineer keeps quietly reviewing every line anyway.

Trust, but read the diff 👀

🤖 Next Move AI | #News

🐜 2026'S QUIET WINNER IS THE SMALL MODEL

The headlines still go to trillion-parameter releases. The deployments increasingly do not.

Something shifted this year: labs stopped competing purely on size and started competing on cost, latency and reliability. A model that answers in 200 milliseconds for a fraction of a cent beats a genius that takes nine seconds and a licence negotiation.

📉 Three forces are pushing the same way. Small models got dramatically better through distillation. Inference bills stopped being a rounding error. And a lot of real work — classification, extraction, routing, summarising — never needed a frontier model in the first place.

🔒 There is a quieter reason too. A small model runs on your own hardware. For a hospital, a bank or a government department, "the data never leaves the building" is not a feature request, it is the entire procurement conversation.

The likely end state is unglamorous: a swarm of cheap specialists doing 95% of the volume, with one expensive model on call for the hard 5%.

Which, funnily enough, is how every company already organises humans 🙂

🤖 Next Move AI | #AI

⚙️ THE AI CHIP RACE HAS A NEW FINISH LINE

For five years the question was: how fast can you train it? In 2026 the question became: how cheaply can you run it, forever?

Training is a one-off capital expense. Inference is a bill that arrives every single day for the life of the product — and once a few hundred million people are using something, inference is where essentially all the money goes.

🏭 You can see the pivot in the silicon. At CES 2026 both Nvidia and AMD led with parts aimed at data centre and edge deployment, not training clusters. Memory bandwidth and interconnect got the stage time that raw FLOPS used to get.

🔦 Government money is pointing the same direction: funding is flowing into co-packaged optics and new memory rather than bigger compute. When the industry starts paying to move data instead of to crunch it, the bottleneck has officially moved.

None of this is exciting. It is plumbing. But plumbing decides whether a feature ships to a billion people or stays a demo.

The sexiest chart in AI right now is a cost-per-token curve going down 📉

🤖 Next Move AI | #Tech

🚶 HUMANOID ROBOTS ARE FINALLY BEING ASKED TO DO SOMETHING BORING

The humanoid pitch has been the same for a decade: a machine shaped like us, so it can use a world built for us. Doors, stairs, tools, shelves. No factory retrofit required.

The blocker was never the legs. It was everything above the neck — grasping an object you have never seen, in lighting you were not trained on, when it is not quite where it should be.

🧠 What changed is that the same models writing your code now drive robot arms. Vision-language models turned "pick up the red box on the second shelf" from a research problem into an instruction.

🏗 So the demos changed character. CES 2026 was less parkour, more deployment: humanoids presented as industrial equipment, with partnerships between robot makers and AI labs doing the real work.

⏳ Temper it, though. A ninety-second video is not a shift. The unglamorous metrics — hours between faults, cost per pick, what happens when it drops something — are the ones that decide this, and almost nobody publishes them.

Wake me when one works a full week without an engineer nearby 🔧

🤖 Next Move AI | #News

📊 WHY AI BENCHMARK SCORES KEEP EXPLODING — AND WHY IT MEANS LESS THAN IT LOOKS

Every few weeks a chart appears showing some model leaping ahead on a benchmark. Two things are usually true at once: the progress is real, and the number is misleading.

🎯 Benchmarks die by being useful. A test becomes the target, the target enters the training data, and within a year the score says more about exposure than ability. The field calls this saturation and treats it as routine.

🔬 Which is why the interesting work is not in the score but in what the score is measuring. DeepMind published a training approach called prospective credit assignment that improved software-engineering results specifically on issues needing more than ten steps to resolve — the long, multi-stage jobs where agents historically wander off and never come back.

That is the honest frontier. Single-step reasoning has been fine for a while. Finishing a long task without losing the plot is the thing that was broken.

📌 Practical rule when you see a new record: ask what the failure cases were and how long the tasks ran. If neither is published, the chart is marketing.

A number without a failure list is a poster, not a result 😐

🤖 Next Move AI | #Facts

🧮 A MODEL CLOSED TEN OPEN MATH PROBLEMS AND SHOWED ITS WORK

On 1 August OpenAI said an internal version of its next model, Astra, had solved ten open problems across mathematics and theoretical computer science.

Claims like that normally die in the replies. This one arrived with receipts: every proof was written in Lean 4 and pushed to GitHub under an open licence. A machine can check it. So can you. No benchmark chart, no vibes.

📐 The headline result is the first explicit construction of a non-sofic group, a question that had been sitting open since 1999. The rest spans von Neumann algebras, quantum complexity, lattice cryptography and new bounds on sphere packing.

People who do this for a living did not shrug. Fields medallist Timothy Gowers said he would recommend one of the proofs to a top journal without hesitation.

💸 And the detail that made everyone uncomfortable: the run reportedly cost around $2,000 in compute. That figure comes from reporting rather than an official invoice, but nobody has disputed the order of magnitude.

Ten problems for two grand. Mathematicians are cheaper, but they need coffee 😕

🤖 Next Move AI | #News

⚡ SOUTH KOREA IS BUILDING A 200-MEGAWATT MACHINE AND CALLING IT SOVEREIGNTY

In late July, NAVER, NVIDIA and Brookfield said they would scale Korea's national AI factory at the GAK Sejong data centre from 55 megawatts to 200, inside a financing envelope of roughly $10 billion.

The schedule is unusually specific for this genre: 55 MW running in the first half of 2027, 100 MW by the end of that year, 200 MW in 2028. Six weeks earlier the committed buildout had been a third of that size.

🏛 The word doing the heavy lifting is sovereign. The pitch is not faster models. It is your data, your jurisdiction, your compute — a country renting nothing from a foreign cloud when it trains something it actually cares about.

Notice the unit as well. Nobody counts GPUs in these announcements anymore. The currency is megawatts, because power is the one thing you cannot order overnight.

🌏 NAVER has said the longer plan runs to gigawatt scale, with customers across Asia-Pacific, Europe and the Middle East.

We spent a decade calling them clouds. They are power plants with opinions 🏭

🤖 Next Move AI | #Tech

🎧 THE AI MUSIC FIREHOSE IS FINALLY GETTING A TAP

On 6 August Suno published a set of operating principles, and underneath the corporate phrasing it is a company conceding that its own output became the problem.

Three things change. Generated tracks get audio watermarking and fingerprinting, so a platform can tell what a machine made. Copying a specific artist's voice or style is off the table. And downloading gets tighter — paid accounts, with monthly caps tied to the tier.

💰 That last one is the real fix, and the reason is fraud. The scheme is dull and effective: generate thousands of songs, upload them everywhere, point bots at them, collect royalties a fraction of a cent at a time. In March a man was convicted for doing exactly that, having collected $8 million before anyone noticed.

⚖️ The timing is not an accident either. Suno has been sitting in copyright litigation for two years, and "we can identify our own files" is a far better sentence in a courtroom than the alternative.

Unlimited output was the entire product. Now it is the liability 🎚

🤖 Next Move AI | #AI

📄 THE MOST INTERESTING DOCUMENT IN TECH IS STILL SEALED

OpenAI filed its S-1 confidentially with the SEC earlier this year, with Goldman Sachs and Morgan Stanley running the offering and a listing reportedly aimed at September.

Confidential filing means the paperwork exists and nobody outside gets to read it until roughly two weeks before the roadshow. Which puts the public version somewhere in the coming weeks.

📊 Everything circulating until then is reporting, not disclosure. A valuation band said to run from $852 billion to $1 trillion. A revenue run rate somewhere near $25 billion. More than 230 million weekly ChatGPT users. No profit expected until around 2030. Treat all of it as second-hand, because it is.

The reason to care is not the share price. An IPO forces a company to write down, under legal penalty, what it actually earns, what compute actually costs, and what frightens it. The risk-factors section will be the most honest page anyone in this industry has published.

🧾 Three years of blog posts about the future, and now a document with consequences attached.

We will be reading the boring part first 😕

🤖 Next Move AI | #News

🛑 TEXAS PUT ITS ENTIRE DATA CENTRE QUEUE ON HOLD

On 3 August Governor Greg Abbott ordered the state utility commission and ERCOT to verify and audit every data centre sitting in the grid's interconnection queue. Until that is finished, nothing moves forward.

⚡ The number behind it: ERCOT is holding requests for more than 474 gigawatts of new connections, roughly five times the state's record peak demand. About 90% of that is data centres.

Nobody in the industry believes all of it is real. Developers routinely file with several grids at once, hold a place in line and decide later, which is how a state ends up planning transmission for load that may never arrive.

📋 So now applicants must answer in writing: what public money did you take, how much do you generate on site, how much water does the cooling drink, who actually owns you. Unconvincing answers mean no connection.

No deadline was set for finishing the audit. "Texans must come first," said the governor — which, in queue terms, means everyone else sits down 🪑

🤖 Next Move AI | #News

🏷 EUROPE'S AI LABELS ARE LIVE. THE HARD PART GOT POSTPONED

Since 2 August the AI Act's transparency rules apply across the EU. A chatbot has to admit it is a chatbot. Deepfakes have to be labelled. Generated or edited content has to carry a machine-readable mark so a detector can find it.

⚖️ Law firms reading the penalty articles put the exposure at up to €15 million or 3% of worldwide turnover, whichever hurts more. The Commission's AI Office and national regulators started enforcing the same day.

Now the part that did not make the headlines. Obligations for high-risk systems — hiring, credit scoring, medical devices, borders — were pushed back to December 2027 and August 2028.

The reason is almost funny. The harmonised technical standards those rules rest on are not written yet. You cannot certify against a document that does not exist.

🤖 So for now Europe regulates whether a machine tells you it is a machine. What the machine decides about your mortgage is a question for 2028 🗓

🤖 Next Move AI | #News