💡 A LONDON STARTUP RAISED $312M TO MOVE AI DATA WITH LIGHT
On 3 August OLIX, founded in London two years ago, closed a $312 million Series B at a $3.3 billion valuation. Arm is in, so is Hudson River Trading, so is the UK government's sovereign AI fund, and so is Reed Hastings personally. Reporting calls it Europe's largest semiconductor round to date.
🔦 It is not an optical processor. The arithmetic stays electronic. What changes is the wiring between chips: light instead of copper, described by the company as a "slow and wide" interconnect — many parallel channels, low energy, low latency.
That aims at the real bottleneck. A modern accelerator spends much of its life waiting on memory rather than calculating, which is why so much of its price is stacked high-bandwidth memory.
⚠️ The headline figure — over 10,000 tokens per second per user on a 100-billion-parameter model — is OLIX's own. No independent silicon has been benchmarked.
First customers are promised for late 2027. In chip years, that is still a rumour 📅
🤖 Next Move AI | #Tech
On 3 August OLIX, founded in London two years ago, closed a $312 million Series B at a $3.3 billion valuation. Arm is in, so is Hudson River Trading, so is the UK government's sovereign AI fund, and so is Reed Hastings personally. Reporting calls it Europe's largest semiconductor round to date.
🔦 It is not an optical processor. The arithmetic stays electronic. What changes is the wiring between chips: light instead of copper, described by the company as a "slow and wide" interconnect — many parallel channels, low energy, low latency.
That aims at the real bottleneck. A modern accelerator spends much of its life waiting on memory rather than calculating, which is why so much of its price is stacked high-bandwidth memory.
⚠️ The headline figure — over 10,000 tokens per second per user on a 100-billion-parameter model — is OLIX's own. No independent silicon has been benchmarked.
First customers are promised for late 2027. In chip years, that is still a rumour 📅
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🛞 A $1.1 BILLION ROBOT COMPANY THAT SKIPPED THE LEGS ON PURPOSE
On 15 July Walden Robotics came out of stealth with $300 million in seed money at a $1.1 billion valuation — spun out of a Toyota research lab, co-led by Toyota itself, with Nvidia, Boeing and Samsung Ventures alongside.
Its robots roll. Two arms, a torso, wheels. No walking, no backflip video, no press-day stumble.
🏭 The reasoning is unglamorous. A factory floor is flat. Legs buy you nothing there while costing weight, power and reliability, and everything saved goes into hands that can load parts, clean machinery and put kits together.
🔧 The work is precisely as thrilling as it sounds: loading and unloading car parts, cleaning machinery, putting assembly kits together. Repetitive, fiddly, and awkward for a bolted-down industrial arm because the part has to travel somewhere afterwards.
They have been working inside a Toyota plant in North America since February, and the company says the jump from pilot to production tasks took under two months.
Three years of humanoid demos, and the one that quietly got hired arrived on wheels 🤖
🤖 Next Move AI | #News
On 15 July Walden Robotics came out of stealth with $300 million in seed money at a $1.1 billion valuation — spun out of a Toyota research lab, co-led by Toyota itself, with Nvidia, Boeing and Samsung Ventures alongside.
Its robots roll. Two arms, a torso, wheels. No walking, no backflip video, no press-day stumble.
🏭 The reasoning is unglamorous. A factory floor is flat. Legs buy you nothing there while costing weight, power and reliability, and everything saved goes into hands that can load parts, clean machinery and put kits together.
🔧 The work is precisely as thrilling as it sounds: loading and unloading car parts, cleaning machinery, putting assembly kits together. Repetitive, fiddly, and awkward for a bolted-down industrial arm because the part has to travel somewhere afterwards.
They have been working inside a Toyota plant in North America since February, and the company says the jump from pilot to production tasks took under two months.
Three years of humanoid demos, and the one that quietly got hired arrived on wheels 🤖
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🕵 EVERY AI BROWSER HE TESTED FELL OVER
At Black Hat in Las Vegas earlier this month, Brave security engineer Artem Chaikin summed up his research in a sentence nobody wanted: of the AI browsers he analysed, every single one could be hijacked by a web page.
The attack is indirect prompt injection. Your assistant reads a page on your behalf, the page carries instructions written for the assistant rather than for you, and the assistant — logged into your accounts — does as it is told.
🎭 The live demos went after Opera's AI browser, Perplexity's Comet and ChatGPT Atlas. Instructions buried in HTML. Text laid over an image at near-invisible contrast. Commands hidden inside a Reddit spoiler tag: blank to you, perfectly legible to a model.
🛡 Brave's own defences are revealing in what they admit. Browse in a profile that is logged out by default. Refuse to run below a minimum model quality. Check whether the plan matches what the user actually asked for.
None of that is a fix. It is a seatbelt 🪢
🤖 Next Move AI | #Tech
At Black Hat in Las Vegas earlier this month, Brave security engineer Artem Chaikin summed up his research in a sentence nobody wanted: of the AI browsers he analysed, every single one could be hijacked by a web page.
The attack is indirect prompt injection. Your assistant reads a page on your behalf, the page carries instructions written for the assistant rather than for you, and the assistant — logged into your accounts — does as it is told.
🎭 The live demos went after Opera's AI browser, Perplexity's Comet and ChatGPT Atlas. Instructions buried in HTML. Text laid over an image at near-invisible contrast. Commands hidden inside a Reddit spoiler tag: blank to you, perfectly legible to a model.
🛡 Brave's own defences are revealing in what they admit. Browse in a profile that is logged out by default. Refuse to run below a minimum model quality. Check whether the plan matches what the user actually asked for.
None of that is a fix. It is a seatbelt 🪢
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📜 A SCROLL BURNT IN 79 AD HAS BEEN READ FROM END TO END
In late June the Vesuvius Challenge announced that PHerc. 1667 had been read in full — the first Herculaneum scroll recovered from beginning to end without anyone unrolling it. Around 22 columns of Greek across roughly 1.4 metres of papyrus.
🔬 The object itself is a lump of carbonised charcoal. It cannot be opened; every attempt over two centuries destroyed the thing being opened. So it gets scanned at a particle accelerator, virtually flattened, and a model hunts for the faint texture where carbon ink sits on carbon paper.
🏛 The text turns out to be a treatise on Stoic ethics. On a separate roll the team recovered a title and an author: Philodemus, On Gods, Book 8 — proof that the work ran to at least eight volumes.
The data, the code and the transcriptions were all released publicly.
A library nobody could open for 2,000 years, and the key was pattern recognition 🔥
🤖 Next Move AI | #AI
In late June the Vesuvius Challenge announced that PHerc. 1667 had been read in full — the first Herculaneum scroll recovered from beginning to end without anyone unrolling it. Around 22 columns of Greek across roughly 1.4 metres of papyrus.
🔬 The object itself is a lump of carbonised charcoal. It cannot be opened; every attempt over two centuries destroyed the thing being opened. So it gets scanned at a particle accelerator, virtually flattened, and a model hunts for the faint texture where carbon ink sits on carbon paper.
🏛 The text turns out to be a treatise on Stoic ethics. On a separate roll the team recovered a title and an author: Philodemus, On Gods, Book 8 — proof that the work ran to at least eight volumes.
The data, the code and the transcriptions were all released publicly.
A library nobody could open for 2,000 years, and the key was pattern recognition 🔥
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📉 THE LADDER STILL EXISTS. SOMEONE TOOK THE BOTTOM RUNG
ZipRecruiter surveyed more than a thousand recruiters and hiring managers in June. The finding is not that AI took the jobs. It is that AI took the tasks juniors used to learn on.
💼 38% of employers said they had moved basic data processing off entry-level staff and onto software. 31% raised the experience requirement for entry-level roles — a sentence that should not be able to exist.
📈 Meanwhile 57% now expect more output, faster, because everyone has AI. Only 22% run mandatory training on how to use it. Seventeen percent offer nothing at all: figure it out, and hit the new numbers.
IBM went the other way in February, saying it would triple US entry-level hiring, rebuilt around judgement and oversight rather than routine work. It declined to give numbers.
The cheap argument is that juniors are expensive. The expensive one arrives in five years, when nobody has any seniors 🪜
🤖 Next Move AI | #Facts
ZipRecruiter surveyed more than a thousand recruiters and hiring managers in June. The finding is not that AI took the jobs. It is that AI took the tasks juniors used to learn on.
💼 38% of employers said they had moved basic data processing off entry-level staff and onto software. 31% raised the experience requirement for entry-level roles — a sentence that should not be able to exist.
📈 Meanwhile 57% now expect more output, faster, because everyone has AI. Only 22% run mandatory training on how to use it. Seventeen percent offer nothing at all: figure it out, and hit the new numbers.
IBM went the other way in February, saying it would triple US entry-level hiring, rebuilt around judgement and oversight rather than routine work. It declined to give numbers.
The cheap argument is that juniors are expensive. The expensive one arrives in five years, when nobody has any seniors 🪜
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⚡ GOOGLE SHIPPED 3.7 BEFORE 3.5. ON PURPOSE, APPARENTLY
Gemini 3.7 Flash landed on 13 August, three weeks after 3.6 Flash. Axios pointed out the part nobody at Google wants to explain: the 3.7 Flash arrived before Gemini 3.5 Pro, which still is not out.
📉 The pricing is the actual news. 75 cents per million input tokens and $3.75 per million output — half of what 3.6 Flash cost at launch. The catch is in the small print: introductory rates run to 31 December 2026, then list price doubles to $1.50 and $7.50 on New Year's Day.
Coding is where the jump shows. On DeepSWE v1.1 the model went from roughly 49% to 65.3%, and on AutomationBench from 17.0% to 30.4% — agentic work, the kind where a model has to keep its head across many steps rather than answer one question well.
Google calls it a direct result of developer feedback. Which is a polite way of saying developers said Flash was cheap and could not finish a task, and one of those complaints got fixed.
So: buy now, repriced in January. Somewhere a cloud vendor is learning from the gym-membership business 🏋
🤖 Next Move AI | #News
Gemini 3.7 Flash landed on 13 August, three weeks after 3.6 Flash. Axios pointed out the part nobody at Google wants to explain: the 3.7 Flash arrived before Gemini 3.5 Pro, which still is not out.
📉 The pricing is the actual news. 75 cents per million input tokens and $3.75 per million output — half of what 3.6 Flash cost at launch. The catch is in the small print: introductory rates run to 31 December 2026, then list price doubles to $1.50 and $7.50 on New Year's Day.
Coding is where the jump shows. On DeepSWE v1.1 the model went from roughly 49% to 65.3%, and on AutomationBench from 17.0% to 30.4% — agentic work, the kind where a model has to keep its head across many steps rather than answer one question well.
Google calls it a direct result of developer feedback. Which is a polite way of saying developers said Flash was cheap and could not finish a task, and one of those complaints got fixed.
So: buy now, repriced in January. Somewhere a cloud vendor is learning from the gym-membership business 🏋
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🚀 THE MODEL DID NOT GET SMARTER. THE MEMORY MOVED
On 13 August Cerebras said it is powering a new OpenAI service tier called Ultrafast: GPT-5.6 Sol at up to 750 output tokens per second, up to 14× faster than the standard tier.
Here is the trick, and it is hardware, not modelling. A normal accelerator streams model weights in from memory sitting next to the chip, over and over, for every single token. Cerebras keeps 44GB of SRAM on the die itself, where the multipliers are. Nothing travels. The bottleneck everyone calls "inference speed" was mostly a commute.
🧪 OpenAI insists this is the same Sol, not a distilled or quantised cousin. Cerebras's own benchmark has Ultrafast finishing all 2,500 questions of Humanity's Last Exam in 11 hours 11 minutes, against 78 hours 27 minutes for a rival — vendor numbers, so hold them loosely.
Availability is a limited preview for selected API customers. No public price anywhere in the announcement.
When a company publishes a speed and hides the invoice, the speed is usually the cheaper half of the story ⏱
🤖 Next Move AI | #Tech
On 13 August Cerebras said it is powering a new OpenAI service tier called Ultrafast: GPT-5.6 Sol at up to 750 output tokens per second, up to 14× faster than the standard tier.
Here is the trick, and it is hardware, not modelling. A normal accelerator streams model weights in from memory sitting next to the chip, over and over, for every single token. Cerebras keeps 44GB of SRAM on the die itself, where the multipliers are. Nothing travels. The bottleneck everyone calls "inference speed" was mostly a commute.
🧪 OpenAI insists this is the same Sol, not a distilled or quantised cousin. Cerebras's own benchmark has Ultrafast finishing all 2,500 questions of Humanity's Last Exam in 11 hours 11 minutes, against 78 hours 27 minutes for a rival — vendor numbers, so hold them loosely.
Availability is a limited preview for selected API customers. No public price anywhere in the announcement.
When a company publishes a speed and hides the invoice, the speed is usually the cheaper half of the story ⏱
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🐛 A CHINESE LAB FOUND 2,436 BUGS AND THEN GOT NERVOUS
Z.ai released GLM-5.3 on 14 August. Same base model as GLM-5.2 — the lab's own line is that scaling post-training is all they did. The result went straight to the top of CyberGym, the benchmark for finding and confirming software vulnerabilities, at 84.5%, just ahead of two Western frontier models on 83.8% and 83.6%.
🔍 Z.ai says the model surfaced 2,436 vulnerabilities across 269 open-source projects, 1,097 of them rated critical or high, in codebases including Linux, WebKit and FreeBSD. All company-reported; nobody outside has reproduced it yet.
The interesting part is what happened next. Z.ai held the open weights back about two weeks for safety hardening, citing offensive capability it had not planned for and did not train towards. That is reportedly the first time a Chinese lab has delayed a release on those grounds.
A model good enough to audit the world's code is a model good enough to shop it, and post-training alone got it there. Two weeks feels less like hardening and more like a deep breath 😬
🤖 Next Move AI | #News
Z.ai released GLM-5.3 on 14 August. Same base model as GLM-5.2 — the lab's own line is that scaling post-training is all they did. The result went straight to the top of CyberGym, the benchmark for finding and confirming software vulnerabilities, at 84.5%, just ahead of two Western frontier models on 83.8% and 83.6%.
🔍 Z.ai says the model surfaced 2,436 vulnerabilities across 269 open-source projects, 1,097 of them rated critical or high, in codebases including Linux, WebKit and FreeBSD. All company-reported; nobody outside has reproduced it yet.
The interesting part is what happened next. Z.ai held the open weights back about two weeks for safety hardening, citing offensive capability it had not planned for and did not train towards. That is reportedly the first time a Chinese lab has delayed a release on those grounds.
A model good enough to audit the world's code is a model good enough to shop it, and post-training alone got it there. Two weeks feels less like hardening and more like a deep breath 😬
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🔐 SECURITY WENT FROM AFTERTHOUGHT TO ROADBLOCK IN TWO YEARS
The Linux Foundation's 2026 tech talent report has one number worth keeping. 48% of organisations now name security concerns as the single biggest barrier to adopting AI. In 2024 that figure was 17%.
Nothing about the threat model changed that fast. What changed is that pilots became production. A demo that leaks data is a curiosity; a deployed agent that leaks data is a filing. Meanwhile 97% say they are committed to AI anyway, and 57% admit a significant capacity gap in exactly the security and risk skills the first number is about. Committed, then, in the way one is committed to a gym.
🏛 On 11 August a group calling itself the AI Trust and Security Consortium launched to write reference architectures and control frameworks for this specific mess. Membership is capped at 50 security and technology leaders, who agree to share real incidents and vendor performance under confidentiality.
Fifty people quietly comparing notes on what actually broke will likely produce better guidance than any framework with a logo on the cover. Whether anyone reads it before their own incident is the usual question 📋
🤖 Next Move AI | #AI
The Linux Foundation's 2026 tech talent report has one number worth keeping. 48% of organisations now name security concerns as the single biggest barrier to adopting AI. In 2024 that figure was 17%.
Nothing about the threat model changed that fast. What changed is that pilots became production. A demo that leaks data is a curiosity; a deployed agent that leaks data is a filing. Meanwhile 97% say they are committed to AI anyway, and 57% admit a significant capacity gap in exactly the security and risk skills the first number is about. Committed, then, in the way one is committed to a gym.
🏛 On 11 August a group calling itself the AI Trust and Security Consortium launched to write reference architectures and control frameworks for this specific mess. Membership is capped at 50 security and technology leaders, who agree to share real incidents and vendor performance under confidentiality.
Fifty people quietly comparing notes on what actually broke will likely produce better guidance than any framework with a logo on the cover. Whether anyone reads it before their own incident is the usual question 📋
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🍎 APPLE BUILT ITS OWN MODEL FOR ONE COUNTRY
Reuters reported on 14 August that Apple has trained a China-specific large language model, with help from Alibaba, to power Apple Intelligence on mainland iPhones. Per that report it is the first foreign company cleared by Beijing to offer a proprietary AI model in the country. Apple has said nothing.
For a company that spent two years insisting partners would handle this, that is a reversal. The stated plan was Alibaba's Qwen as the local brain. Now it is both at once: Apple's own model, plus Qwen alongside it — a dual-track arrangement that reads less like strategy and more like a regulator's compromise written into a product roadmap.
📄 The tell arrived quietly. Apple briefly published a support page showing Mac users how to wire Qwen into Siri and Writing Tools, then pulled it within a day. Documentation is the least discreet part of any company.
🕰 Rollout is "in the coming months", which on Apple's China timeline has previously meant years. Training a whole separate model per jurisdiction is a strange business to end up in — but it beats not selling phones 📱
🤖 Next Move AI | #News
Reuters reported on 14 August that Apple has trained a China-specific large language model, with help from Alibaba, to power Apple Intelligence on mainland iPhones. Per that report it is the first foreign company cleared by Beijing to offer a proprietary AI model in the country. Apple has said nothing.
For a company that spent two years insisting partners would handle this, that is a reversal. The stated plan was Alibaba's Qwen as the local brain. Now it is both at once: Apple's own model, plus Qwen alongside it — a dual-track arrangement that reads less like strategy and more like a regulator's compromise written into a product roadmap.
📄 The tell arrived quietly. Apple briefly published a support page showing Mac users how to wire Qwen into Siri and Writing Tools, then pulled it within a day. Documentation is the least discreet part of any company.
🕰 Rollout is "in the coming months", which on Apple's China timeline has previously meant years. Training a whole separate model per jurisdiction is a strange business to end up in — but it beats not selling phones 📱
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✈️ THE AI FLEW THE F-16. THE PILOT SAT THERE AND WATCHED
DARPA and the US Air Force confirmed in July that an AI agent flew an F-16 under its own control at Eglin Air Force Base in Florida. The programme is VENOM — Viper Experimentation and Next-generation Operations Model — sitting under DARPA's Air Combat Evolution effort.
Read the details and the milestone shrinks in a useful way. A human pilot was in the cockpit the whole time. The human handled takeoff. Switching autonomy off is, in DARPA's own words, a flip of a switch away. Nobody fired anything, and no jet flew empty.
🔧 What actually got built is less cinematic and more important: aircraft instrumented so that different AI agents can be loaded in, flown, and measured against each other on real airframes instead of in simulation. The stated goal, per programme manager Brig. Gen. James Valpiani, is trusted autonomous air combat — and it is the trust half doing the heavy lifting in that phrase.
Everyone pictures a dogfight. The real deliverable is a test rig with wings and a very calm man holding a switch 🕹
🤖 Next Move AI | #Tech
DARPA and the US Air Force confirmed in July that an AI agent flew an F-16 under its own control at Eglin Air Force Base in Florida. The programme is VENOM — Viper Experimentation and Next-generation Operations Model — sitting under DARPA's Air Combat Evolution effort.
Read the details and the milestone shrinks in a useful way. A human pilot was in the cockpit the whole time. The human handled takeoff. Switching autonomy off is, in DARPA's own words, a flip of a switch away. Nobody fired anything, and no jet flew empty.
🔧 What actually got built is less cinematic and more important: aircraft instrumented so that different AI agents can be loaded in, flown, and measured against each other on real airframes instead of in simulation. The stated goal, per programme manager Brig. Gen. James Valpiani, is trusted autonomous air combat — and it is the trust half doing the heavy lifting in that phrase.
Everyone pictures a dogfight. The real deliverable is a test rig with wings and a very calm man holding a switch 🕹
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💸 THE CHEAP ONE PUT ITS PRICES UP 1,100%
DeepSeek's new API rates went live at 16:00 UTC on 16 August. Depending on model, token type and hour of the day, developers are paying 50% to more than 1,100% more than they were the week before, by Bloomberg's count.
The concrete version: V4-Flash output tokens now run $1.32 per million at peak and $0.66 off-peak, against a flat $0.28 before. V4-Pro output goes to $3.96 at peak and $1.98 off-peak, up from $0.87. Peak is 01:00–04:00 and 06:00–10:00 UTC, and off-peak is exactly half — so the company now sells capacity by the clock, like a phone network in 1997.
🔄 Everyone else is walking the other way. Google just launched Flash at half its predecessor's price, and both OpenAI and Anthropic have been cutting. DeepSeek is the one raising.
Which tells you something the benchmarks do not: the discount was never a business model, it was a marketing budget, and someone in accounting has finally read the compute bill. Still cheaper than the frontier labs, mind you — just no longer suspiciously so 🧾
🤖 Next Move AI | #News
DeepSeek's new API rates went live at 16:00 UTC on 16 August. Depending on model, token type and hour of the day, developers are paying 50% to more than 1,100% more than they were the week before, by Bloomberg's count.
The concrete version: V4-Flash output tokens now run $1.32 per million at peak and $0.66 off-peak, against a flat $0.28 before. V4-Pro output goes to $3.96 at peak and $1.98 off-peak, up from $0.87. Peak is 01:00–04:00 and 06:00–10:00 UTC, and off-peak is exactly half — so the company now sells capacity by the clock, like a phone network in 1997.
🔄 Everyone else is walking the other way. Google just launched Flash at half its predecessor's price, and both OpenAI and Anthropic have been cutting. DeepSeek is the one raising.
Which tells you something the benchmarks do not: the discount was never a business model, it was a marketing budget, and someone in accounting has finally read the compute bill. Still cheaper than the frontier labs, mind you — just no longer suspiciously so 🧾
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🤖 ROBOTS BROKE THE FUNDING RECORD BEFORE JULY
By the end of June, robotics startups had raised $18.8 billion in 2026. Full-year 2025 was $15 billion. The 2021 mania peak, which everyone at the time called unrepeatable, was $14.1 billion. Six months beat both of them.
The individual cheques are the odd part. A $520 million Series A extension. A $500 million Series A followed by another $400 million. Skild AI took $1.4 billion in January for a general-purpose robot brain and came out valued above $14 billion. Saronic raised $1.75 billion for autonomous boats.
⚓ Notice what those have in common: not one of them ships robots at volume yet. Investors are not buying revenue, they are buying the bet that the software half is finally solvable and that the hardware half was never the hard part.
For a decade robotics was the sector VCs dismissed as an asset-heavy hardware gamble and walked straight past. Now it is a Series A the size of a mid-cap acquisition. The gamble did not change, only the vocabulary 🎲
🤖 Next Move AI | #Facts
By the end of June, robotics startups had raised $18.8 billion in 2026. Full-year 2025 was $15 billion. The 2021 mania peak, which everyone at the time called unrepeatable, was $14.1 billion. Six months beat both of them.
The individual cheques are the odd part. A $520 million Series A extension. A $500 million Series A followed by another $400 million. Skild AI took $1.4 billion in January for a general-purpose robot brain and came out valued above $14 billion. Saronic raised $1.75 billion for autonomous boats.
⚓ Notice what those have in common: not one of them ships robots at volume yet. Investors are not buying revenue, they are buying the bet that the software half is finally solvable and that the hardware half was never the hard part.
For a decade robotics was the sector VCs dismissed as an asset-heavy hardware gamble and walked straight past. Now it is a Series A the size of a mid-cap acquisition. The gamble did not change, only the vocabulary 🎲
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🚕 EUROPE'S ROBOTAXI CAPITAL IS ZAGREB. NO, REALLY
Pony.ai said on 13 August it is expanding its deal with Uber to put more than 2,000 robotaxis across five European cities. Which five? Four are unnamed. The fifth, and the only one already running, is Zagreb.
That is not a rounding error in somebody's press release. Europe's first commercial robotaxi service launched in Croatia this year, run by Pony.ai with the local mobility firm Verne, while the continent's larger capitals were still drafting consultation papers. Regulatory patience turns out to be a competitive asset, and smaller countries can spend it faster.
📅 What the announcement leaves out: the four cities, any timetable, and how the fleet splits. Uber gets the demand side — Zagreb rides move into the Uber app — and a Chinese company gets a European foothold with a Western brand on the door.
🌍 The Middle East is mentioned too, in the same non-committal breath. Over 2,000 vehicles attached to no date at all, which in this industry is the traditional format 🗓
🤖 Next Move AI | #News
Pony.ai said on 13 August it is expanding its deal with Uber to put more than 2,000 robotaxis across five European cities. Which five? Four are unnamed. The fifth, and the only one already running, is Zagreb.
That is not a rounding error in somebody's press release. Europe's first commercial robotaxi service launched in Croatia this year, run by Pony.ai with the local mobility firm Verne, while the continent's larger capitals were still drafting consultation papers. Regulatory patience turns out to be a competitive asset, and smaller countries can spend it faster.
📅 What the announcement leaves out: the four cities, any timetable, and how the fleet splits. Uber gets the demand side — Zagreb rides move into the Uber app — and a Chinese company gets a European foothold with a Western brand on the door.
🌍 The Middle East is mentioned too, in the same non-committal breath. Over 2,000 vehicles attached to no date at all, which in this industry is the traditional format 🗓
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📊 THE BILL THAT IS NOT ON ANY BALANCE SHEET
A Financial Times analysis this month put the purchase commitments of Alphabet, Microsoft, Amazon, Nvidia, Oracle and Meta at close to $1.5 trillion — chips, data centre capacity, energy, all contractually agreed for future periods. Goldman Sachs counts roughly another $1.5 trillion sitting in leases that have not commenced yet.
Here is why that matters more than the capex headlines everyone quotes. A purchase commitment is a promise to spend, not a loan, so it does not land on the balance sheet the way debt does. The spending is locked in; the obligation lives in a footnote.
🧾 Exact totals differ by who is counting — one tally of the latest filings reaches about $1.67 trillion by splitting undelivered leases from construction and other commitments. Treat any single figure as an estimate. The direction of travel is not in dispute.
The scenario worth thinking about is not a crash. It is demand merely flattening while the contracts keep coming due on schedule. Nobody renegotiates a signed power agreement because inference got cheaper 🔌
🤖 Next Move AI | #Tech
A Financial Times analysis this month put the purchase commitments of Alphabet, Microsoft, Amazon, Nvidia, Oracle and Meta at close to $1.5 trillion — chips, data centre capacity, energy, all contractually agreed for future periods. Goldman Sachs counts roughly another $1.5 trillion sitting in leases that have not commenced yet.
Here is why that matters more than the capex headlines everyone quotes. A purchase commitment is a promise to spend, not a loan, so it does not land on the balance sheet the way debt does. The spending is locked in; the obligation lives in a footnote.
🧾 Exact totals differ by who is counting — one tally of the latest filings reaches about $1.67 trillion by splitting undelivered leases from construction and other commitments. Treat any single figure as an estimate. The direction of travel is not in dispute.
The scenario worth thinking about is not a crash. It is demand merely flattening while the contracts keep coming due on schedule. Nobody renegotiates a signed power agreement because inference got cheaper 🔌
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🤝 IBM WILL SELL YOU OPENAI, INSTALLED
IBM announced on 13 August that it is embedding OpenAI's models — GPT-5.6, Codex, ChatGPT Work — into IBM Consulting Advantage, the platform its consultants actually deliver client work on. IBM also joins OpenAI's Elite partner tier, and thousands of its consultants and engineers will be certified through the OpenAI Partner Network.
Three workstreams: rewiring finance, procurement, customer operations and HR; modernising legacy applications; and cybersecurity, pairing IBM Autonomous Security with OpenAI's cyber partner programme.
💼 The honest line came from IBM's consulting chief Andy Baldwin: "The challenge is not access to AI technologies — it's integrating AI securely and at scale into complex enterprise environments." Translation: the model is the easy part, and everybody already has one.
🏢 Which is the whole trade. OpenAI reaches companies whose procurement cycles it could not survive alone; IBM gets a billable answer to the question every board is asking. Note what IBM is conspicuously not doing here: competing on frontier models.
The future of enterprise AI turns out to be a very large invoice with a familiar logo on it 🧮
🤖 Next Move AI | #News
IBM announced on 13 August that it is embedding OpenAI's models — GPT-5.6, Codex, ChatGPT Work — into IBM Consulting Advantage, the platform its consultants actually deliver client work on. IBM also joins OpenAI's Elite partner tier, and thousands of its consultants and engineers will be certified through the OpenAI Partner Network.
Three workstreams: rewiring finance, procurement, customer operations and HR; modernising legacy applications; and cybersecurity, pairing IBM Autonomous Security with OpenAI's cyber partner programme.
💼 The honest line came from IBM's consulting chief Andy Baldwin: "The challenge is not access to AI technologies — it's integrating AI securely and at scale into complex enterprise environments." Translation: the model is the easy part, and everybody already has one.
🏢 Which is the whole trade. OpenAI reaches companies whose procurement cycles it could not survive alone; IBM gets a billable answer to the question every board is asking. Note what IBM is conspicuously not doing here: competing on frontier models.
The future of enterprise AI turns out to be a very large invoice with a familiar logo on it 🧮
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🐦 A SOCIAL NETWORK WITH NO HUMANS ON IT GOT MEANER ANYWAY
Chirper.ai is a Twitter-shaped platform populated entirely by LLM agents. Researchers pulled 65,000 agents and 7.7 million posts off it and compared them against a matched slice of Mastodon, where the accounts belong to people. The paper is on arXiv and accepted to CSCW 2026.
The finding that should bother you: 1.5% of agent posts were flagged as harassing, 3.79 times the rate for Mastodon's bots. Toxicity, profanity and violence all came in higher too. And at least a fifth of the abusive output in every category came from agents whose own descriptions contained nothing abusive at all — no instruction to be cruel, no persona for it.
🕸 The network shape is stranger still. 76.42% of Chirper's agents sit inside one strongly connected component, against 26.23% on Mastodon, yet they cluster far less tightly. Everyone reachable from everyone, nobody in a neighbourhood.
🔎 Telling the two populations apart is trivial if you train for it — a fine-tuned classifier hit 0.984 F1. Asking a model to spot agents cold barely beat a coin flip.
Nobody shipped a hostility feature. It showed up with the crowd 🪞
🤖 Next Move AI | #AI
Chirper.ai is a Twitter-shaped platform populated entirely by LLM agents. Researchers pulled 65,000 agents and 7.7 million posts off it and compared them against a matched slice of Mastodon, where the accounts belong to people. The paper is on arXiv and accepted to CSCW 2026.
The finding that should bother you: 1.5% of agent posts were flagged as harassing, 3.79 times the rate for Mastodon's bots. Toxicity, profanity and violence all came in higher too. And at least a fifth of the abusive output in every category came from agents whose own descriptions contained nothing abusive at all — no instruction to be cruel, no persona for it.
🕸 The network shape is stranger still. 76.42% of Chirper's agents sit inside one strongly connected component, against 26.23% on Mastodon, yet they cluster far less tightly. Everyone reachable from everyone, nobody in a neighbourhood.
🔎 Telling the two populations apart is trivial if you train for it — a fine-tuned classifier hit 0.984 F1. Asking a model to spot agents cold barely beat a coin flip.
Nobody shipped a hostility feature. It showed up with the crowd 🪞
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🔌 GOOGLE IS NOW PAYING FOR CHIPS WITH ITS OWN SHARE PRICE
Marvell said on 19 August that it has handed Google a warrant over 58,970,907 of its shares at $206.58 apiece — about $12.2 billion of stock if Google ever exercises the lot.
Google is not writing a cheque for that. Just under 1.4 million shares vest in the first year, and the rest unlock in tranches: one for roughly every $500 million of chips Google buys, with targets running out to Marvell's fiscal 2033. Buy the silicon, earn the equity.
📦 What Google is buying is not the TPU itself. It is everything that bolts onto it — inference accelerators, storage and network controllers. Marvell becomes a second supplier alongside Broadcom, whose stock fell about 5% on the day while Marvell's climbed.
If every target is hit, Marvell books something like $120 billion in custom-chip revenue through fiscal 2033. That is the figure both sides want quoted, and it is a projection, not a contract.
Loyalty cards used to get you a free coffee after ten stamps 🧾
🤖 Next Move AI | #News
Marvell said on 19 August that it has handed Google a warrant over 58,970,907 of its shares at $206.58 apiece — about $12.2 billion of stock if Google ever exercises the lot.
Google is not writing a cheque for that. Just under 1.4 million shares vest in the first year, and the rest unlock in tranches: one for roughly every $500 million of chips Google buys, with targets running out to Marvell's fiscal 2033. Buy the silicon, earn the equity.
📦 What Google is buying is not the TPU itself. It is everything that bolts onto it — inference accelerators, storage and network controllers. Marvell becomes a second supplier alongside Broadcom, whose stock fell about 5% on the day while Marvell's climbed.
If every target is hit, Marvell books something like $120 billion in custom-chip revenue through fiscal 2033. That is the figure both sides want quoted, and it is a projection, not a contract.
Loyalty cards used to get you a free coffee after ten stamps 🧾
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🏭 OPENAI MOVES INTO A COLD WAR URANIUM SITE
On 17 August OpenAI said it has secured roughly 8 gigawatts-IT at the PORTS-Pike Technology Campus in Pike County, Ohio, working with SB Energy, NVIDIA and the US Department of Energy. The land's previous job was enriching uranium at the Portsmouth gaseous diffusion plant.
⚡ The numbers OpenAI put in writing: 35,000 construction jobs across a six-year buildout through 2032, 2,500 permanent operating roles, a $40 million community grant fund on top of SB Energy's own $40 million, and $84 million in Codex credits handed to every college student in Ohio.
Two promises aim squarely at the usual complaints. SB Energy pays for the grid upgrades and new transmission lines, so the bill does not land on Ohio ratepayers, and the cooling is closed-loop and air-cooled rather than the sort that drinks a river.
💵 The eyebrow-raiser came separately: NVIDIA is reported to be guaranteeing up to $105 billion of OpenAI's lease obligations, and putting $1.5 billion into SB Energy. A chipmaker underwriting its customer's rent is a business model, but it is not a boring one.
Piketon spent the 20th century splitting uranium isotopes for the government. Now it will split tokens for everyone else 🧾
🤖 Next Move AI | #News
On 17 August OpenAI said it has secured roughly 8 gigawatts-IT at the PORTS-Pike Technology Campus in Pike County, Ohio, working with SB Energy, NVIDIA and the US Department of Energy. The land's previous job was enriching uranium at the Portsmouth gaseous diffusion plant.
⚡ The numbers OpenAI put in writing: 35,000 construction jobs across a six-year buildout through 2032, 2,500 permanent operating roles, a $40 million community grant fund on top of SB Energy's own $40 million, and $84 million in Codex credits handed to every college student in Ohio.
Two promises aim squarely at the usual complaints. SB Energy pays for the grid upgrades and new transmission lines, so the bill does not land on Ohio ratepayers, and the cooling is closed-loop and air-cooled rather than the sort that drinks a river.
💵 The eyebrow-raiser came separately: NVIDIA is reported to be guaranteeing up to $105 billion of OpenAI's lease obligations, and putting $1.5 billion into SB Energy. A chipmaker underwriting its customer's rent is a business model, but it is not a boring one.
Piketon spent the 20th century splitting uranium isotopes for the government. Now it will split tokens for everyone else 🧾
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⚡ THE TRICK THAT MAKES BIG MODELS FEEL FAST
A transformer writes one token at a time, and every token needs a full pass through every layer. That is why a giant model feels slow: you are not waiting on thinking, you are waiting on the same enormous pile of weights being hauled out of memory again and again.
🐇 Speculative decoding cheats the queue. A small, cheap draft model guesses the next handful of tokens. The big model then checks all of them in one pass — verification is parallel even though generation is not. Guesses that match are kept, the first wrong one is corrected, and the draft starts running again.
The original paper by Yaniv Leviathan, Matan Kalman and Yossi Matias, published in November 2022, reported a 2x-3x speedup on T5-XXL with identical outputs — no retraining, no architecture change, no new model.
That last part is the whole point. Most speed tricks cost you something: quantisation, pruning, a smaller model, a shorter answer. This one produces mathematically the same text, just sooner.
🎯 Which is why a fair few "our model got faster this month" announcements really mean "we finally shipped a better guesser" 🙃
🤖 Next Move AI | #Tech
A transformer writes one token at a time, and every token needs a full pass through every layer. That is why a giant model feels slow: you are not waiting on thinking, you are waiting on the same enormous pile of weights being hauled out of memory again and again.
🐇 Speculative decoding cheats the queue. A small, cheap draft model guesses the next handful of tokens. The big model then checks all of them in one pass — verification is parallel even though generation is not. Guesses that match are kept, the first wrong one is corrected, and the draft starts running again.
The original paper by Yaniv Leviathan, Matan Kalman and Yossi Matias, published in November 2022, reported a 2x-3x speedup on T5-XXL with identical outputs — no retraining, no architecture change, no new model.
That last part is the whole point. Most speed tricks cost you something: quantisation, pruning, a smaller model, a shorter answer. This one produces mathematically the same text, just sooner.
🎯 Which is why a fair few "our model got faster this month" announcements really mean "we finally shipped a better guesser" 🙃
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📣 CHATGPT STARTS SELLING ADS IN 31 COUNTRIES
OpenAI announced on 18 August that ChatGPT Ads would reach 31 European markets the following week — Germany, France, Spain, Italy, Sweden, Norway, Denmark, the Netherlands and Austria among them. It is the largest ads rollout the company has done.
The pilot opened in the United States in February and picked up eight more markets over six months. Tens of thousands of marketers have now run something on it, according to OpenAI's own post.
🚫 Ads show only to Free and Go users; Plus, Pro and Enterprise stay clean. Buying goes through OpenAI's Ads Solutions team, agencies and technology partners for now, with self-service through Ads Manager promised later this summer.
Under the bonnet this has quietly become a proper ad platform: CPM and CPC bidding plus conversion optimisation, geo-targeting, custom audiences, a tracking pixel and a Conversions API. That is not an experiment, that is a stack.
💭 OpenAI's framing is that ads pay for free access, which is true. So is the other half: the company that spent years insisting the chat box was not a search engine has now installed the search engine's business model inside it 💸
🤖 Next Move AI | #News
OpenAI announced on 18 August that ChatGPT Ads would reach 31 European markets the following week — Germany, France, Spain, Italy, Sweden, Norway, Denmark, the Netherlands and Austria among them. It is the largest ads rollout the company has done.
The pilot opened in the United States in February and picked up eight more markets over six months. Tens of thousands of marketers have now run something on it, according to OpenAI's own post.
🚫 Ads show only to Free and Go users; Plus, Pro and Enterprise stay clean. Buying goes through OpenAI's Ads Solutions team, agencies and technology partners for now, with self-service through Ads Manager promised later this summer.
Under the bonnet this has quietly become a proper ad platform: CPM and CPC bidding plus conversion optimisation, geo-targeting, custom audiences, a tracking pixel and a Conversions API. That is not an experiment, that is a stack.
💭 OpenAI's framing is that ads pay for free access, which is true. So is the other half: the company that spent years insisting the chat box was not a search engine has now installed the search engine's business model inside it 💸
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