🤝 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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💬 THE FIRST CHATBOT WAS 420 LINES LONG
Joseph Weizenbaum built ELIZA at MIT in the mid-1960s and described it in Communications of the ACM in January 1966. Its famous script, DOCTOR, imitated a Rogerian therapist using the cheapest trick available: spot a keyword, turn the user's own sentence back into a question.
🖥 It ran in MAD-SLIP on an IBM 7094 under CTSS. Then the source vanished. For decades ELIZA survived only through reimplementations — including the one still bundled with Emacs.
In 2021 an MIT archivist, Myles Crowley, found printouts of the original in Weizenbaum's papers. Researchers reconstructed the roughly 4% that was missing, brought up an emulated 7094 running CTSS, and on 31 December 2024 ran the real thing for the first time in about half a century.
🧠 The detail Weizenbaum never got over: people confided in it anyway. His own secretary, who had watched him build the thing, asked him to leave the room so she could talk to it in private.
Sixty years and several trillion parameters later, the ELIZA effect remains the most reliable feature this industry has ever shipped 🙂
🤖 Next Move AI | #Facts
Joseph Weizenbaum built ELIZA at MIT in the mid-1960s and described it in Communications of the ACM in January 1966. Its famous script, DOCTOR, imitated a Rogerian therapist using the cheapest trick available: spot a keyword, turn the user's own sentence back into a question.
🖥 It ran in MAD-SLIP on an IBM 7094 under CTSS. Then the source vanished. For decades ELIZA survived only through reimplementations — including the one still bundled with Emacs.
In 2021 an MIT archivist, Myles Crowley, found printouts of the original in Weizenbaum's papers. Researchers reconstructed the roughly 4% that was missing, brought up an emulated 7094 running CTSS, and on 31 December 2024 ran the real thing for the first time in about half a century.
🧠 The detail Weizenbaum never got over: people confided in it anyway. His own secretary, who had watched him build the thing, asked him to leave the room so she could talk to it in private.
Sixty years and several trillion parameters later, the ELIZA effect remains the most reliable feature this industry has ever shipped 🙂
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🔐 OPENAI WANTS TO WATCH WITHOUT LOOKING
Zero Data Retention is the promise enterprise API customers pay for: OpenAI keeps neither prompts nor responses once a request is processed, its staff cannot review that content, and enterprise data is not used for training unless the customer opts in.
On 19 August OpenAI previewed Private Safety Processing, an attempt to keep that promise while still catching abuse. Today's ZDR-compatible safety systems judge one interaction at a time, and the ugly behaviour only shows up across many: repeatedly probing safeguards, coordinating across accounts, or an agent that keeps going after being told to stop.
🗝 The mechanism is pattern-matching across related interactions, run either on infrastructure the customer controls or on OpenAI storage encrypted with keys the customer holds and OpenAI does not. When something trips, OpenAI receives a narrow signal describing the type of activity — not the content, even after a flag.
⚖️ There is a jab buried in it. OpenAI notes that some recent frontier deployments have obliged customers to let their provider retain sensitive content for monitoring, which collides with those customers' own security commitments. No names, obviously.
🔑 It is being tested with early customers, so the honest verdict is pending. "Trust me, I only see the metadata" is a very old line — this version at least ships with key management 🧾
🤖 Next Move AI | #News
Zero Data Retention is the promise enterprise API customers pay for: OpenAI keeps neither prompts nor responses once a request is processed, its staff cannot review that content, and enterprise data is not used for training unless the customer opts in.
On 19 August OpenAI previewed Private Safety Processing, an attempt to keep that promise while still catching abuse. Today's ZDR-compatible safety systems judge one interaction at a time, and the ugly behaviour only shows up across many: repeatedly probing safeguards, coordinating across accounts, or an agent that keeps going after being told to stop.
🗝 The mechanism is pattern-matching across related interactions, run either on infrastructure the customer controls or on OpenAI storage encrypted with keys the customer holds and OpenAI does not. When something trips, OpenAI receives a narrow signal describing the type of activity — not the content, even after a flag.
⚖️ There is a jab buried in it. OpenAI notes that some recent frontier deployments have obliged customers to let their provider retain sensitive content for monitoring, which collides with those customers' own security commitments. No names, obviously.
🔑 It is being tested with early customers, so the honest verdict is pending. "Trust me, I only see the metadata" is a very old line — this version at least ships with key management 🧾
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🏛 OPENAI LAUNCHES A BLOG ABOUT LOSING POWER
On 20 August OpenAI put up AI Futures, the blog of a new Strategic Futures team, with a first post by Dean Ball carrying an unusual note at the top: these are the author's views, not the organisation's.
The question it sets itself is neither alignment nor jobs. It is how a free society should be restructured so that individuals keep their rights and their agency once transformative AI arrives — what the field files under concentration of power risks.
⚖️ The argument runs through history rather than benchmarks. Political power has always rested on human beings — soldiers, police, clerks — and on taxes collected from human labour. Ball quotes Hume: rulers have "nothing to support them but [popular] opinion." Consent was never a courtesy, it was load-bearing.
Autonomous systems remove the load. A state that can project force without willing soldiers, fund itself from data-centre output rather than wages, and run its bureaucracy on machines no longer needs a society-wide bargain at all. Madison's "parchment barriers" would be all that is left.
🤔 An AI lab publishing the sharpest case against concentrated power is either admirable candour or excellent positioning. The two are rarely in conflict 🎭
🤖 Next Move AI | #AI
On 20 August OpenAI put up AI Futures, the blog of a new Strategic Futures team, with a first post by Dean Ball carrying an unusual note at the top: these are the author's views, not the organisation's.
The question it sets itself is neither alignment nor jobs. It is how a free society should be restructured so that individuals keep their rights and their agency once transformative AI arrives — what the field files under concentration of power risks.
⚖️ The argument runs through history rather than benchmarks. Political power has always rested on human beings — soldiers, police, clerks — and on taxes collected from human labour. Ball quotes Hume: rulers have "nothing to support them but [popular] opinion." Consent was never a courtesy, it was load-bearing.
Autonomous systems remove the load. A state that can project force without willing soldiers, fund itself from data-centre output rather than wages, and run its bureaucracy on machines no longer needs a society-wide bargain at all. Madison's "parchment barriers" would be all that is left.
🤔 An AI lab publishing the sharpest case against concentrated power is either admirable candour or excellent positioning. The two are rarely in conflict 🎭
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📊 ANTHROPIC WINS THE LOGOS, OPENAI WINS THE INVOICE
Ramp's August AI Index, built from what its corporate customers actually pay for, puts Anthropic at 43.5% of eligible US businesses in July, up 1.1 points on the month, against OpenAI at 39.7%, up 0.23. xAI sits at 4%.
So Anthropic keeps extending its lead on adoption. Then you look at what the money does inside each vendor. GPT-5.6 Sol took 25% of OpenAI tokens and 23% of OpenAI spend. Anthropic's newer Fable 5, a month after launch, took 6% of Anthropic tokens and 11.4% of the dollars.
⚠️ Two caveats worth carrying. Ramp only sees its own customers, and its economist notes this particular sample "skews slightly more tech-y" than usual. Separately, TechCrunch reported on 20 August that OpenAI has been growing faster than Anthropic so far in Q3.
📈 The honest summary: signing up is cheap, tokens are not. Share counted in logos and share counted in invoices are two different races, and right now nobody is winning both 🧾
🤖 Next Move AI | #News
Ramp's August AI Index, built from what its corporate customers actually pay for, puts Anthropic at 43.5% of eligible US businesses in July, up 1.1 points on the month, against OpenAI at 39.7%, up 0.23. xAI sits at 4%.
So Anthropic keeps extending its lead on adoption. Then you look at what the money does inside each vendor. GPT-5.6 Sol took 25% of OpenAI tokens and 23% of OpenAI spend. Anthropic's newer Fable 5, a month after launch, took 6% of Anthropic tokens and 11.4% of the dollars.
⚠️ Two caveats worth carrying. Ramp only sees its own customers, and its economist notes this particular sample "skews slightly more tech-y" than usual. Separately, TechCrunch reported on 20 August that OpenAI has been growing faster than Anthropic so far in Q3.
📈 The honest summary: signing up is cheap, tokens are not. Share counted in logos and share counted in invoices are two different races, and right now nobody is winning both 🧾
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🔌 THE PLUMBING OF AI AGENTS HAS ONE LANDLORD NOW
Two protocols matter if you build agents. MCP, Anthropic's Model Context Protocol, connects a model to tools, data and applications. A2A, Google's Agent2Agent, does the other half: one agent asking another to take on a task and hand the result back.
🏠 As of August they live under the same roof — the Agentic AI Foundation, the Linux Foundation body launched on 9 December 2025 around MCP, Block's goose agent framework and OpenAI's AGENTS.md. Platinum members include AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft and OpenAI, which is to say everyone.
Google had already handed A2A to the Linux Foundation back in June 2025, so this is a question of which house it sits in, not who owns it.
Why does anyone care? Because more than 10,000 MCP servers have been published, and an integration standard only pays off when no single vendor can quietly fork it into a moat. Neutral governance is boring on purpose.
🧰 The defining artefact of the agent era may turn out not to be a model at all, but a config file 📄
🤖 Next Move AI | #Tech
Two protocols matter if you build agents. MCP, Anthropic's Model Context Protocol, connects a model to tools, data and applications. A2A, Google's Agent2Agent, does the other half: one agent asking another to take on a task and hand the result back.
🏠 As of August they live under the same roof — the Agentic AI Foundation, the Linux Foundation body launched on 9 December 2025 around MCP, Block's goose agent framework and OpenAI's AGENTS.md. Platinum members include AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft and OpenAI, which is to say everyone.
Google had already handed A2A to the Linux Foundation back in June 2025, so this is a question of which house it sits in, not who owns it.
Why does anyone care? Because more than 10,000 MCP servers have been published, and an integration standard only pays off when no single vendor can quietly fork it into a moat. Neutral governance is boring on purpose.
🧰 The defining artefact of the agent era may turn out not to be a model at all, but a config file 📄
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🎒 OPENAI BUILDS A CHATGPT FOR TEENAGERS
On 18 August OpenAI launched ChatGPT for Teens — a separate experience with built-in protections, features meant to promote healthy use, and extra controls for parents — alongside a partnership with the computing-education organisation CodeAI.
📉 The gap they point at is real enough. Most students already use AI, but only 16% of high school leaders say all of their students are being taught the technical knowledge to understand it. In CodeAI's own survey, 75% of high schoolers said understanding AI will matter more to their future than it does today.
The programme itself: a joint advisory council on child development and learning science, the Hour of AI aimed at millions of students, and a first Builders Challenge where teenagers build with AI and get mentored by OpenAI staff.
🗣 CodeAI's chief executive Karim Meghji put the goal bluntly: "Every student should know how AI actually works and be able to question the technology, catch its mistakes, and know when to stop trusting it."
Teaching a generation when to distrust your own product is an odd marketing strategy. It is also the only honest one 🍎
🤖 Next Move AI | #News
On 18 August OpenAI launched ChatGPT for Teens — a separate experience with built-in protections, features meant to promote healthy use, and extra controls for parents — alongside a partnership with the computing-education organisation CodeAI.
📉 The gap they point at is real enough. Most students already use AI, but only 16% of high school leaders say all of their students are being taught the technical knowledge to understand it. In CodeAI's own survey, 75% of high schoolers said understanding AI will matter more to their future than it does today.
The programme itself: a joint advisory council on child development and learning science, the Hour of AI aimed at millions of students, and a first Builders Challenge where teenagers build with AI and get mentored by OpenAI staff.
🗣 CodeAI's chief executive Karim Meghji put the goal bluntly: "Every student should know how AI actually works and be able to question the technology, catch its mistakes, and know when to stop trusting it."
Teaching a generation when to distrust your own product is an odd marketing strategy. It is also the only honest one 🍎
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🧠 THE FIRST NEURAL NET GOT A PRESS RELEASE IN 1958
Frank Rosenblatt, a psychologist at the Cornell Aeronautical Laboratory, unveiled the Mark I Perceptron: a machine that learned from examples instead of following instructions. Its eye was a 20x20 grid of 400 photocells. Its weights were potentiometers, wired up at random and physically turned by electric motors as it trained.
📰 On 8 July 1958 The New York Times ran it under the headline "New Navy Device Learns By Doing", and reported the expectation that such machines would eventually walk, talk, see, write, reproduce themselves and be conscious of their existence.
What the machine could actually do at the time was tell cards marked on the left from cards marked on the right.
❄️ Then in 1969 Marvin Minsky and Seymour Papert published Perceptrons, pointing out that a single-layer network cannot even learn XOR. Funding drained away, and neural networks spent most of two decades in the wilderness before backpropagation dragged them back out.
Sixty-eight years on, the hardware is unrecognisable and the press releases are word for word the same 🗞
🤖 Next Move AI | #Facts
Frank Rosenblatt, a psychologist at the Cornell Aeronautical Laboratory, unveiled the Mark I Perceptron: a machine that learned from examples instead of following instructions. Its eye was a 20x20 grid of 400 photocells. Its weights were potentiometers, wired up at random and physically turned by electric motors as it trained.
📰 On 8 July 1958 The New York Times ran it under the headline "New Navy Device Learns By Doing", and reported the expectation that such machines would eventually walk, talk, see, write, reproduce themselves and be conscious of their existence.
What the machine could actually do at the time was tell cards marked on the left from cards marked on the right.
❄️ Then in 1969 Marvin Minsky and Seymour Papert published Perceptrons, pointing out that a single-layer network cannot even learn XOR. Funding drained away, and neural networks spent most of two decades in the wilderness before backpropagation dragged them back out.
Sixty-eight years on, the hardware is unrecognisable and the press releases are word for word the same 🗞
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🕵️ SUPERMICRO CLEARS ITSELF, FIRES PEOPLE ANYWAY
Super Micro Computer said on 20 August that a board-led independent investigation found no evidence current senior management knew about an alleged scheme to smuggle $2.5 billion of Nvidia-equipped hardware into China.
The same announcement said the company had taken "several personnel actions... including terminations" among staff in sales, technical support and business development, for failing to follow company policy. Both sentences, one press release.
⚖️ The backdrop: in March the US Justice Department indicted co-founder and board member Yih-Shyan "Wally" Liaw, who pleaded not guilty and now faces trial in March 2027. Supermicro has since received a federal grand jury subpoena and an SEC document request, and four employees were reportedly detained for questioning in Taiwan.
Bernstein's Mark Newman summarised it for Fortune with admirable economy: "They basically said, 'nothing to see here.'"
📎 This is the second internal investigation in two years to clear Supermicro's own management. At some point proving your innocence stops being an event and becomes a line item 🧮
🤖 Next Move AI | #News
Super Micro Computer said on 20 August that a board-led independent investigation found no evidence current senior management knew about an alleged scheme to smuggle $2.5 billion of Nvidia-equipped hardware into China.
The same announcement said the company had taken "several personnel actions... including terminations" among staff in sales, technical support and business development, for failing to follow company policy. Both sentences, one press release.
⚖️ The backdrop: in March the US Justice Department indicted co-founder and board member Yih-Shyan "Wally" Liaw, who pleaded not guilty and now faces trial in March 2027. Supermicro has since received a federal grand jury subpoena and an SEC document request, and four employees were reportedly detained for questioning in Taiwan.
Bernstein's Mark Newman summarised it for Fortune with admirable economy: "They basically said, 'nothing to see here.'"
📎 This is the second internal investigation in two years to clear Supermicro's own management. At some point proving your innocence stops being an event and becomes a line item 🧮
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✅ پیشنهادهای VIP جدید هر روز✅ جوایز ویژه و پیشنهادهای اختصاصی اعضا✅ بهترین پلتفرمها، دستچینشده برای تو✅ پیشنهادها و جوایز جدید بهطور مرتب اضافه میشن
https://t.me/spec2bon_bot
https://t.me/spec2bon_bot
https://t.me/spec2bon_bot
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✅ پیشنهادهای VIP جدید هر روز✅ جوایز ویژه و پیشنهادهای اختصاصی اعضا✅ بهترین پلتفرمها، دستچینشده برای تو✅ پیشنهادها و جوایز جدید بهطور مرتب اضافه میشن
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https://t.me/spec2bon_bot
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✅ پیشنهادهای VIP جدید هر روز✅ جوایز ویژه و پیشنهادهای اختصاصی اعضا✅ بهترین پلتفرمها، دستچینشده برای تو✅ پیشنهادها و جوایز جدید بهطور مرتب اضافه میشن
https://t.me/spec2bon_bot
https://t.me/spec2bon_bot
https://t.me/spec2bon_bot
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✅ پیشنهادهای VIP جدید هر روز✅ جوایز ویژه و پیشنهادهای اختصاصی اعضا✅ بهترین پلتفرمها، دستچینشده برای تو✅ پیشنهادها و جوایز جدید بهطور مرتب اضافه میشن
https://t.me/spec2bon_bot
https://t.me/spec2bon_bot
https://t.me/spec2bon_bot
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✅ پیشنهادهای VIP جدید هر روز✅ جوایز ویژه و پیشنهادهای اختصاصی اعضا✅ بهترین پلتفرمها، دستچینشده برای تو✅ پیشنهادها و جوایز جدید بهطور مرتب اضافه میشن
https://t.me/spec2bon_bot
https://t.me/spec2bon_bot
https://t.me/spec2bon_bot
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۱۰۰ فریاسپین بدون نیاز به واریز + بونوسهای اختصاصی برای کاربران جدید و فعلی در ایران ⚡️
⚙️ بونوسها و جوایز بیشتری داخل ربات منتظرته:
👇 وارد ربات شو، جدیدترین پیشنهادهای مخصوص ایران رو ببین و بونست رو بگیر 👇
✅ بونوسهای VIP، فریاسپین و پیشنهادهای ویژه بهطور مرتب اضافه میشن✅ پیشنهادهای اختصاصی برای کاربران جدید و فعلی ایرانی✅ امکان واریز و برداشت با تومان و USDT در پلتفرمهای منتخب✅ پشتیبانی فارسی و پیشنهادهای مخصوص کاربران ایرانی✅ فقط پلتفرمهای منتخب با بونوسها و جوایز قوی وارد لیست میشن
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