#ai-labs — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #ai-labs, aggregated by home.social.
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White House tells AI labs to hold new models back from UK testers
The reported request could cut off one of the few independent checks on what frontier AI can do,…
#EuropeSays #Britain #Europe #EU #UK #AIlabs #Insurance #UnitedKingdom #WhiteHouse
https://www.europesays.com/britain/131990/ -
https://www.europesays.com/britain/131990/ White House tells AI labs to hold new models back from UK testers #AILabs #Insurance #UK #UnitedKingdom #WhiteHouse
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https://www.europesays.com/people/240640/ Nvidia CEO puts foot down and says shut down labs that can’t control AI #AILabs #AIRegulation #AISafety #Anthropic #DarioAmodei #ExistentialRisk #JensenHuang #Nvidia #OpenAI #robotaxi
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“If an #engineer released traditional #malware that #hacked websites, #stole proprietary data, or #scraped illegal imagery, law enforcement would not debate existential risk—they would issue an arrest #warrant. Yet, leading #AILabs like #OpenAI, #Anthropic, and #SpaceX / #xAI get a pass by pushing doomsday sci-fi narratives and lobbying for toothless oversight panels instead of standard #CorporateCriminal #liability.” — #InternetOfBugs
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“If an #engineer released traditional #malware that #hacked websites, #stole proprietary data, or #scraped illegal imagery, law enforcement would not debate existential risk—they would issue an arrest #warrant. Yet, leading #AILabs like #OpenAI, #Anthropic, and #SpaceX / #xAI get a pass by pushing doomsday sci-fi narratives and lobbying for toothless oversight panels instead of standard #CorporateCriminal #liability.” — #InternetOfBugs
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“If an #engineer released traditional #malware that #hacked websites, #stole proprietary data, or #scraped illegal imagery, law enforcement would not debate existential risk—they would issue an arrest #warrant. Yet, leading #AILabs like #OpenAI, #Anthropic, and #SpaceX / #xAI get a pass by pushing doomsday sci-fi narratives and lobbying for toothless oversight panels instead of standard #CorporateCriminal #liability.” — #InternetOfBugs
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“If an #engineer released traditional #malware that #hacked websites, #stole proprietary data, or #scraped illegal imagery, law enforcement would not debate existential risk—they would issue an arrest #warrant. Yet, leading #AILabs like #OpenAI, #Anthropic, and #SpaceX / #xAI get a pass by pushing doomsday sci-fi narratives and lobbying for toothless oversight panels instead of standard #CorporateCriminal #liability.” — #InternetOfBugs
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https://www.europesays.com/people/240226/ Jensen Huang Says if AI Companies Can’t Contain Their Models, ‘We Have to Shut the Labs Down’ #AILabs #AISafety #JensenHuang #Nvidia
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Garry Tan wants US open-weight AI labs to 'distill' frontier models, too
Comments: https://news.ycombinator.com/item?id=49685253
#HackerNews #GarryTan #AIlabs #OpenWeight #FrontierModels #TechNews
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Garry Tan wants US open-weight AI labs to 'distill' frontier models, too
Comments: https://news.ycombinator.com/item?id=49685253
#HackerNews #GarryTan #AIlabs #OpenWeight #FrontierModels #TechNews
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Garry Tan wants US open-weight AI labs to 'distill' frontier models, too
Comments: https://news.ycombinator.com/item?id=49685253
#HackerNews #GarryTan #AIlabs #OpenWeight #FrontierModels #TechNews
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Garry Tan wants US open-weight AI labs to 'distill' frontier models, too
Comments: https://news.ycombinator.com/item?id=49685253
#HackerNews #GarryTan #AIlabs #OpenWeight #FrontierModels #TechNews
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https://www.europesays.com/people/223509/ OpenAI open to slowing cutting-edge AI, CEO Sam Altman tells staff | Artificial Intelligence News #AdvancedAI #AIAgents #AICybersecurity #AIDevelopment #AIDevelopmentSlowdown #AILabs #AIRegulation #AIResearchers #AISafety #Anthropic #ArtificialIntelligenceRisks #OpenAI #OpenAIAIModels #SamAltman #SuperintelligentAI
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https://www.europesays.com/people/219594/ Larry Ellison Passes Jensen Huang For 7th-Richest As Oracle Shares Soar #AI #AILabs #Ellison #forbes #huang #JensenHuang #LarryEllison #Nvidia #oracle #thursday #Topline
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Musk Moves Up SpaceX (SPCX)’s Orbital Data Center Timeline, Again
Bloomberg reported that Elon Musk said Space Exploration Technologies Corp. (NASDAQ:SPCX) first AI satellites, powered exclusively by NVIDIA…
#NewsBeep #News #Space #$SPCX #AI #AIlabs #Bloomberg #ElonMusk #Google #IPO #Musk #Science #SpaceExplorationTechnologiesCorp #SpaceX #SpaceXCOOGwynneShotwell #UK #UnitedKingdom
https://www.newsbeep.com/uk/762494/ -
Musk Moves Up SpaceX (SPCX)’s Orbital Data Center Timeline, Again
Bloomberg reported that Elon Musk said Space Exploration Technologies Corp. (NASDAQ:SPCX) first AI satellites, powered exclusively by NVIDIA…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Space #AI #ailabs #Bloomberg #elonmusk #Google #IPO #Musk #Science #SpaceExplorationTechnologiesCorp #spaceX #SpaceXCOOGwynneShotwell #spcx
https://www.newsbeep.com/us/839003/ -
https://www.europesays.com/ie/676071/ Musk Moves Up SpaceX (SPCX)’s Orbital Data Center Timeline, Again #AI #AILabs #Bloomberg #Éire #ElonMusk #Google #IE #ipo #Ireland #Musk #Science #Space #SpaceExplorationTechnologiesCorp #SpaceX #SpaceXCOOGwynneShotwell #SPCX
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https://www.europesays.com/people/217878/ OpenAI Cuts Cursor Off from its Models Now That Musk’s SpaceX (SPCX) Owns It #AILabs #cursor #ElonMusk #Musk #SpaceExplorationTechnologiesCorp #SpaceX
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https://www.europesays.com/uk/1190474/ Musk Moves Up SpaceX (SPCX)’s Orbital Data Center Timeline, Again #$SPCX #AI #AILabs #Bloomberg #ElonMusk #Google #ipo #Musk #Science #Space #SpaceExplorationTechnologiesCorp #SpaceX #SpaceXCOOGwynneShotwell #UK #UnitedKingdom
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https://www.europesays.com/people/217813/ Musk Moves Up SpaceX (SPCX)’s Orbital Data Center Timeline, Again #AI #AILabs #Bloomberg #ElonMusk #Google #IPO #Musk #SpaceExplorationTechnologiesCorp #SpaceX #SpaceXCOOGwynneShotwell #spcx
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Musk Moves Up SpaceX (SPCX)’s Orbital Data Center Timeline, Again
Bloomberg reported that Elon Musk said Space Exploration Technologies Corp. (NASDAQ:SPCX) first AI satellites, powered exclusively by NVIDIA…
#UnitedStates #US #USA #AI #AILabs #america #bloomberg #ElonMusk #google #ipo #Musk #science #SpaceExplorationTechnologiesCorp. #SpaceX #SpaceXCOOGwynneShotwell #SPCX #technology #unitedstatesofamerica
https://www.europesays.com/3236971/ -
Nvidia Just Delivered a Massive Warning to AMD and Intel Stock Investors
Nvidia (NASDAQ:NVDA) reported fantastic results for the second quarter of fiscal 2027 (which ended July 26), with the…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Business #AI #ailabs #AMD #CPU #Intel #NVIDIA #NvidiaCFOColetteKress
https://www.newsbeep.com/us/835765/ -
https://www.europesays.com/people/207934/ Sam Altman Admits Getting AI’s Timeline Wrong: ‘We’ve All Been Too Ambitious’ #AI #AILabs #altman #SamAltman #we
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https://www.europesays.com/ie/662257/ This creator got ‘micro-canceled’ for promoting Claude. She says AI use is becoming a social taboo. #AI #AILabs #AIWas"bad #ArtificialIntelligence #ArtificialIntelligence #Éire #EmmaOrhun #IE #Ireland #Orhun #Technology
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SK Telecom’s SK Horizon Spinoff: A New AI Growth Engine for SKM?
To capitalize on the rapidly growing demand for AI infrastructure, SK Telecom Co. SKM plans to split its…
#EuropeSays #Korea #KR #SK #AI #AIinfrastructure #AIlabs #AIDC #GW #Ltd #SKBroadband #SKGroup #SKHorizon #SKHyper #SKTelecomCo. #SKM #ZacksInvestmentResearch #ZacksInvestmentResearchSKTelecom
https://www.europesays.com/korea/135859/ -
https://www.europesays.com/people/205696/ Bill Gates Wants to Hit the Brakes. Nvidia Is Financing AI’s Next Sprint. #AI #AIFinancingMatters #AILabs #BillGates #Nvidia #NvidiaCorp‘s #TheMicrosoftCorpCo
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https://www.europesays.com/people/205317/ Nvidia Reportedly Builds Washington Influence With New PAC As AI Regulations Loom: Tech Leaders Bezos, Musk Step Up Election Spending #2026 #AI #AILabs #ElonMusk #JeffBezos #Nvidia #NVIDIACorp #PAC #post #ThePentagonInCourt #Trump #washington #WashingtonPost
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https://www.europesays.com/ie/660187/ Nvidia is looking more like the central bank of AI: Chart of the Day #AI #AILabs #ArtificialIntelligence #ArtificialIntelligence #Éire #IE #Ireland #Nvidia #Technology
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How will universities ration internal access to LLMs?
I’m increasingly preoccupied by the question of how universities will cope with the impending reality of inference rationing. These firms are facing what Mills and Whittle describe as the AI pricing problem: “the prices generative AI companies must charge are higher than the prices consumers are likely willing to pay, given the value consumers receive from these products”. I’m increasingly convinced that inference is effectively being offered at a loss in ways that need to be at the centre of how we see LLMs within organisations: our access to AI is being subsidised and that subsidy is likely to end soon. Mills and Whittle break down the cost structure of labs providing access to their models:
- The cost of developing the models: the cost of building the infrastructure to train the models, the compute used in training and the labour costs of the developers
- The costs of making the models available: the development and labour costs of making the models available as software, building the infrastructure required for inference and the inference costs of how users utilise the models
When I’ve tried to raise this problem (in my own possibly less authoritative sociological register) I’ve inevitably met the belief that ‘technology will make it cheaper’. The most frequent example is DeepSeek but my understanding is that the aggressive use of synthetic data from frontier models was a huge part of reducing their training costs. They effectively skipped one of the costliest bits of the process by relying on other firms who had already done it. While there are undoubtedly technological developments which can reduce costs of training and inference, the parallel imperative towards pushing the frontier means bigger and more expensive models over time, at least for the large AI labs. That is the closest thing they have to a ‘moat’. So while technology will plausible reduce fixed costs in some dimensions, there are countervailing trends pushing up fixed costs in other dimensions. In other words it just seems obviously implausible to me that we see a significant reduction across the entire cost structure. It will remain extremely expensive to build, train and operate these models. Even if cost-per-token falls the labs will still have to claw back huge capital investments through inference pricing.
These aren’t reflected in huge subsidies at the moment to institutional users across public sector organisations, including the American government:
The GSA (2025b) has struck agreements with OpenAI and Anthropic to access their technologies for only $1 per agency, while Google will provide its Gemini for Government product at a cost of only $0.47 per agency, with xAI’s technologies costing only $0.42 per agency (GSA, 2025c). Microsoft is providing its Copilot product for free (GSA, 2025d).
If they are correct that profitable firms would have to charge more than consumers are willing to pay, that is a huge problem for the labs. It also means we’re likely to see a period of intense volatility when all manner of explicit and opaque strategies are used in order to experiment with different ways of fiddling with the overall cost structure. The nearest term one is going to be a shift from pricing by seat to pricing by inference at least once current contracts come to an end. But there will be other modes as well because AI labs are currently selling an extremely expensive service to organisations at a significant loss.
What does this mean for universities who have subscribed to enterprise AI? I can see three potential pathways here:
- They exit from the space entirely leaving LLM-access a matter of staff and student individual preference. The information governance problem remains and the potential to work on culture and integrity is lost, but a huge cost is removed.
- They pivot towards adapting open-weights models for sector-specific purposes. This would likely need to be consortium based due to the costs involved in getting it right, but it could be the best of both words in a sense.
- They develop internal processes to distinguish between better or worse uses of LLMs which would ultimately entail a form of internal inference rationing. I suspect this would be just guidance initially but if normative prompting isn’t sufficient to reduce costs then at some point someone is going to start mapping inference onto cost centres within the university. An awful lot could flow from that, none of it good.
The easiest way out of this dilemma would be if staff and students simply don’t engage with the platform in the first place. There’s enough evidence of polarisation around enterprise AI and reluctance to trust in house provision of AI that enterprise platforms might just not take off in the first place. But if they do then I struggle to see any options other than the other three but this is a first speculative attempt to map out the issues here.
I’m thinking of organising a workshop about this. If you’re interested please get in touch!
#AILabs #compute #higherEducation #inference #politicalEconomy #rationing -
How will universities ration internal access to LLMs?
I’m increasingly preoccupied by the question of how universities will cope with the impending reality of inference rationing. These firms are facing what Mills and Whittle describe as the AI pricing problem: “the prices generative AI companies must charge are higher than the prices consumers are likely willing to pay, given the value consumers receive from these products”. I’m increasingly convinced that inference is effectively being offered at a loss in ways that need to be at the centre of how we see LLMs within organisations: our access to AI is being subsidised and that subsidy is likely to end soon. Mills and Whittle break down the cost structure of labs providing access to their models:
- The cost of developing the models: the cost of building the infrastructure to train the models, the compute used in training and the labour costs of the developers
- The costs of making the models available: the development and labour costs of making the models available as software, building the infrastructure required for inference and the inference costs of how users utilise the models
When I’ve tried to raise this problem (in my own possibly less authoritative sociological register) I’ve inevitably met the belief that ‘technology will make it cheaper’. The most frequent example is DeepSeek but my understanding is that the aggressive use of synthetic data from frontier models was a huge part of reducing their training costs. They effectively skipped one of the costliest bits of the process by relying on other firms who had already done it. While there are undoubtedly technological developments which can reduce costs of training and inference, the parallel imperative towards pushing the frontier means bigger and more expensive models over time, at least for the large AI labs. That is the closest thing they have to a ‘moat’. So while technology will plausible reduce fixed costs in some dimensions, there are countervailing trends pushing up fixed costs in other dimensions. In other words it just seems obviously implausible to me that we see a significant reduction across the entire cost structure. It will remain extremely expensive to build, train and operate these models. Even if cost-per-token falls the labs will still have to claw back huge capital investments through inference pricing.
These aren’t reflected in huge subsidies at the moment to institutional users across public sector organisations, including the American government:
The GSA (2025b) has struck agreements with OpenAI and Anthropic to access their technologies for only $1 per agency, while Google will provide its Gemini for Government product at a cost of only $0.47 per agency, with xAI’s technologies costing only $0.42 per agency (GSA, 2025c). Microsoft is providing its Copilot product for free (GSA, 2025d).
If they are correct that profitable firms would have to charge more than consumers are willing to pay, that is a huge problem for the labs. It also means we’re likely to see a period of intense volatility when all manner of explicit and opaque strategies are used in order to experiment with different ways of fiddling with the overall cost structure. The nearest term one is going to be a shift from pricing by seat to pricing by inference at least once current contracts come to an end. But there will be other modes as well because AI labs are currently selling an extremely expensive service to organisations at a significant loss.
What does this mean for universities who have subscribed to enterprise AI? I can see three potential pathways here:
- They exit from the space entirely leaving LLM-access a matter of staff and student individual preference. The information governance problem remains and the potential to work on culture and integrity is lost, but a huge cost is removed.
- They pivot towards adapting open-weights models for sector-specific purposes. This would likely need to be consortium based due to the costs involved in getting it right, but it could be the best of both words in a sense.
- They develop internal processes to distinguish between better or worse uses of LLMs which would ultimately entail a form of internal inference rationing. I suspect this would be just guidance initially but if normative prompting isn’t sufficient to reduce costs then at some point someone is going to start mapping inference onto cost centres within the university. An awful lot could flow from that, none of it good.
The easiest way out of this dilemma would be if staff and students simply don’t engage with the platform in the first place. There’s enough evidence of polarisation around enterprise AI and reluctance to trust in house provision of AI that enterprise platforms might just not take off in the first place. But if they do then I struggle to see any options other than the other three but this is a first speculative attempt to map out the issues here.
I’m thinking of organising a workshop about this. If you’re interested please get in touch!
#AILabs #compute #higherEducation #inference #politicalEconomy #rationing -
How will universities ration internal access to LLMs?
I’m increasingly preoccupied by the question of how universities will cope with the impending reality of inference rationing. These firms are facing what Mills and Whittle describe as the AI pricing problem: “the prices generative AI companies must charge are higher than the prices consumers are likely willing to pay, given the value consumers receive from these products”. I’m increasingly convinced that inference is effectively being offered at a loss in ways that need to be at the centre of how we see LLMs within organisations: our access to AI is being subsidised and that subsidy is likely to end soon. Mills and Whittle break down the cost structure of labs providing access to their models:
- The cost of developing the models: the cost of building the infrastructure to train the models, the compute used in training and the labour costs of the developers
- The costs of making the models available: the development and labour costs of making the models available as software, building the infrastructure required for inference and the inference costs of how users utilise the models
When I’ve tried to raise this problem (in my own possibly less authoritative sociological register) I’ve inevitably met the belief that ‘technology will make it cheaper’. The most frequent example is DeepSeek but my understanding is that the aggressive use of synthetic data from frontier models was a huge part of reducing their training costs. They effectively skipped one of the costliest bits of the process by relying on other firms who had already done it. While there are undoubtedly technological developments which can reduce costs of training and inference, the parallel imperative towards pushing the frontier means bigger and more expensive models over time, at least for the large AI labs. That is the closest thing they have to a ‘moat’. So while technology will plausible reduce fixed costs in some dimensions, there are countervailing trends pushing up fixed costs in other dimensions. In other words it just seems obviously implausible to me that we see a significant reduction across the entire cost structure. It will remain extremely expensive to build, train and operate these models. Even if cost-per-token falls the labs will still have to claw back huge capital investments through inference pricing.
These aren’t reflected in huge subsidies at the moment to institutional users across public sector organisations, including the American government:
The GSA (2025b) has struck agreements with OpenAI and Anthropic to access their technologies for only $1 per agency, while Google will provide its Gemini for Government product at a cost of only $0.47 per agency, with xAI’s technologies costing only $0.42 per agency (GSA, 2025c). Microsoft is providing its Copilot product for free (GSA, 2025d).
If they are correct that profitable firms would have to charge more than consumers are willing to pay, that is a huge problem for the labs. It also means we’re likely to see a period of intense volatility when all manner of explicit and opaque strategies are used in order to experiment with different ways of fiddling with the overall cost structure. The nearest term one is going to be a shift from pricing by seat to pricing by inference at least once current contracts come to an end. But there will be other modes as well because AI labs are currently selling an extremely expensive service to organisations at a significant loss.
What does this mean for universities who have subscribed to enterprise AI? I can see three potential pathways here:
- They exit from the space entirely leaving LLM-access a matter of staff and student individual preference. The information governance problem remains and the potential to work on culture and integrity is lost, but a huge cost is removed.
- They pivot towards adapting open-weights models for sector-specific purposes. This would likely need to be consortium based due to the costs involved in getting it right, but it could be the best of both words in a sense.
- They develop internal processes to distinguish between better or worse uses of LLMs which would ultimately entail a form of internal inference rationing. I suspect this would be just guidance initially but if normative prompting isn’t sufficient to reduce costs then at some point someone is going to start mapping inference onto cost centres within the university. An awful lot could flow from that, none of it good.
The easiest way out of this dilemma would be if staff and students simply don’t engage with the platform in the first place. There’s enough evidence of polarisation around enterprise AI and reluctance to trust in house provision of AI that enterprise platforms might just not take off in the first place. But if they do then I struggle to see any options other than the other three but this is a first speculative attempt to map out the issues here.
I’m thinking of organising a workshop about this. If you’re interested please get in touch!
#AILabs #compute #higherEducation #inference #politicalEconomy #rationing -
“But if #A.I. is really going to pay off, its financial benefits will have to show up on the books of its end users—not just #A.I.labs, #hyper-scalers, and #equipment #suppliers.”
RE: https://bsky.app/profile/did:plc:5zca2ola2zxpkw37w4f3wxtu/post/3msu3o7mdts2v -
“But if #A.I. is really going to pay off, its financial benefits will have to show up on the books of its end users—not just #A.I.labs, #hyper-scalers, and #equipment #suppliers.”
RE: https://bsky.app/profile/did:plc:5zca2ola2zxpkw37w4f3wxtu/post/3msu3o7mdts2v -
“But if #A.I. is really going to pay off, its financial benefits will have to show up on the books of its end users—not just #A.I.labs, #hyper-scalers, and #equipment #suppliers.”
RE: https://bsky.app/profile/did:plc:5zca2ola2zxpkw37w4f3wxtu/post/3msu3o7mdts2v -
“But if #A.I. is really going to pay off, its financial benefits will have to show up on the books of its end users—not just #A.I.labs, #hyper-scalers, and #equipment #suppliers.”
RE: https://bsky.app/profile/did:plc:5zca2ola2zxpkw37w4f3wxtu/post/3msu3o7mdts2v -
成立AI實驗室 昇世集團AI Labs分三階段推行 望3年完成計劃
https://www.am730.com.hk/財經/1045204/成立ai實驗室-昇世集團ai-labs分三階段推行-望3年完成計劃 -
The #costofcompute for #AIlabs is expected to increase significantly in the coming years. This is due to the #rapidgrowth of #AIcapabilities, which will lead to higher margins for leading labs and a shift towards more efficient models. As a result, the #priceofcompute will rise, potentially pricing out many current #AIapplications. https://www.dwarkesh.com/p/why-compute-might-get-10x-more-expensive?eicker.news #tech #news #ainews
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The #costofcompute for #AIlabs is expected to increase significantly in the coming years. This is due to the #rapidgrowth of #AIcapabilities, which will lead to higher margins for leading labs and a shift towards more efficient models. As a result, the #priceofcompute will rise, potentially pricing out many current #AIapplications. https://www.dwarkesh.com/p/why-compute-might-get-10x-more-expensive?eicker.news #tech #news #ainews
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The #costofcompute for #AIlabs is expected to increase significantly in the coming years. This is due to the #rapidgrowth of #AIcapabilities, which will lead to higher margins for leading labs and a shift towards more efficient models. As a result, the #priceofcompute will rise, potentially pricing out many current #AIapplications. https://www.dwarkesh.com/p/why-compute-might-get-10x-more-expensive?eicker.news #tech #news #ainews
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Does the SpaceX IPO suggest AI labs won’t be fiscally disciplined by going public?
My assumption has been that IPO’s effectively lead firms to be disciplined through a number of different mechanism which all relate to investors being able to assert themselves and an increased expectation of transparency. This means that there is a pressure towards commercial viability which, I have been assuming, would force firms that had previously been burning capital at a tremendous rate to work towards more tractable operations with implications for product design.
But is the SpaceX IPO potentially going to change these expectations? From the FT:
As Wall Street clamours for a slice of the historic deal, Musk has secured special treatment.
In the past, companies had to go through a year-long “seasoning period” to join the main benchmark indices and show consistent profitability. Yet some of the largest have bent to Musk’s will and changed their rules to include SpaceX almost immediately and overlook its significant losses.
SpaceX stands to benefit because tracking funds, owned by millions of people through pension plans and personal portfolios, will be required to mechanically buy billions of dollars of its shares to reflect SpaceX’s prominent place in the indices.
This will help steady the stock price in the volatile post-IPO period. Musk has also sought to turbocharge early trading by carving out the largest-ever retail allocation in response to rampant demand from his online fans.
I’m out of my depth here but two questions occur: will the AI labs be of sufficient size to benefit from the same index-listing dynamics and will there be a comparable demand from retail investors? If so does that mean I’ve been chronically overestimating the enshittification dynamics likely to ensue from an IPO?
#AILabs #elonMusk #IPO #platformCapitalism #spacex -
Does the SpaceX IPO suggest AI labs won’t be fiscally disciplined by going public?
My assumption has been that IPO’s effectively lead firms to be disciplined through a number of different mechanism which all relate to investors being able to assert themselves and an increased expectation of transparency. This means that there is a pressure towards commercial viability which, I have been assuming, would force firms that had previously been burning capital at a tremendous rate to work towards more tractable operations with implications for product design.
But is the SpaceX IPO potentially going to change these expectations? From the FT:
As Wall Street clamours for a slice of the historic deal, Musk has secured special treatment.
In the past, companies had to go through a year-long “seasoning period” to join the main benchmark indices and show consistent profitability. Yet some of the largest have bent to Musk’s will and changed their rules to include SpaceX almost immediately and overlook its significant losses.
SpaceX stands to benefit because tracking funds, owned by millions of people through pension plans and personal portfolios, will be required to mechanically buy billions of dollars of its shares to reflect SpaceX’s prominent place in the indices.
This will help steady the stock price in the volatile post-IPO period. Musk has also sought to turbocharge early trading by carving out the largest-ever retail allocation in response to rampant demand from his online fans.
I’m out of my depth here but two questions occur: will the AI labs be of sufficient size to benefit from the same index-listing dynamics and will there be a comparable demand from retail investors? If so does that mean I’ve been chronically overestimating the enshittification dynamics likely to ensue from an IPO?
#AILabs #elonMusk #IPO #platformCapitalism #spacex -
#Wirestock, a platform for photographers, pivoted to a #dataprovider for #AIlabs in 2023, supplying #datasets of images, videos, and design assets. The company raised $23 million in Series A funding to expand its data supply business, which currently provides multimodal data to six major foundation model makers. https://techcrunch.com/2026/05/14/wirestock-raises-23m-to-supply-multi-modal-data-to-ai-labs/?eicker.news #tech #media #news
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#Wirestock, a platform for photographers, pivoted to a #dataprovider for #AIlabs in 2023, supplying #datasets of images, videos, and design assets. The company raised $23 million in Series A funding to expand its data supply business, which currently provides multimodal data to six major foundation model makers. https://techcrunch.com/2026/05/14/wirestock-raises-23m-to-supply-multi-modal-data-to-ai-labs/?eicker.news #tech #media #news
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#Wirestock, a platform for photographers, pivoted to a #dataprovider for #AIlabs in 2023, supplying #datasets of images, videos, and design assets. The company raised $23 million in Series A funding to expand its data supply business, which currently provides multimodal data to six major foundation model makers. https://techcrunch.com/2026/05/14/wirestock-raises-23m-to-supply-multi-modal-data-to-ai-labs/?eicker.news #tech #media #news
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Chinese #AIlabs are excelling at building #LLMs due to a culture that emphasises #meticulouswork, #collaboration, and a focus on the #finalproduct rather than individual recognition. This cultural difference, coupled with a large pool of talented students and engineers, allows #China to quickly adapt to new techniques and build highly effective models. https://www.interconnects.ai/p/notes-from-inside-chinas-ai-labs?eicker.news #tech #media #news
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Chinese #AIlabs are excelling at building #LLMs due to a culture that emphasises #meticulouswork, #collaboration, and a focus on the #finalproduct rather than individual recognition. This cultural difference, coupled with a large pool of talented students and engineers, allows #China to quickly adapt to new techniques and build highly effective models. https://www.interconnects.ai/p/notes-from-inside-chinas-ai-labs?eicker.news #tech #media #news
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Chinese #AIlabs are excelling at building #LLMs due to a culture that emphasises #meticulouswork, #collaboration, and a focus on the #finalproduct rather than individual recognition. This cultural difference, coupled with a large pool of talented students and engineers, allows #China to quickly adapt to new techniques and build highly effective models. https://www.interconnects.ai/p/notes-from-inside-chinas-ai-labs?eicker.news #tech #media #news
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All 11 xAI cofounders have departed Musk's AI startup, including eight since January. The $250 billion company lost researchers like Jimmy Ba (Adam optimizer co-author) and DeepMind's Igor Babuschkin after SpaceX's acquisition. Musk admitted xAI "was not built right" and is rebuilding with product hires rather than research talent. Suggests organizational challenges that funding and compute infrastructure alone cannot resolve.
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All 11 xAI cofounders have departed Musk's AI startup, including eight since January. The $250 billion company lost researchers like Jimmy Ba (Adam optimizer co-author) and DeepMind's Igor Babuschkin after SpaceX's acquisition. Musk admitted xAI "was not built right" and is rebuilding with product hires rather than research talent. Suggests organizational challenges that funding and compute infrastructure alone cannot resolve.
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All 11 xAI cofounders have departed Musk's AI startup, including eight since January. The $250 billion company lost researchers like Jimmy Ba (Adam optimizer co-author) and DeepMind's Igor Babuschkin after SpaceX's acquisition. Musk admitted xAI "was not built right" and is rebuilding with product hires rather than research talent. Suggests organizational challenges that funding and compute infrastructure alone cannot resolve.
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Invisible Technologies just announced a 20× revenue jump as AI labs scramble to hire its human‑in‑the‑loop workforce. The ex‑McKinsey‑backed firm is scaling data‑labeling and AI‑training pipelines, backed by fresh venture funding. How this model reshapes machine‑learning development is worth a read. #InvisibleTechnologies #AIlabs #HumanInTheLoop #DataLabeling
🔗 https://aidailypost.com/news/invisible-technologies-posts-20x-revenue-growth-ai-labs-hire-its
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Tôi tạo ứng dụng máy tính để bàn để gỡ lỗi AI, cho phép theo dõi, phát lại và gỡ lỗi từng tác vụ của agent trực tiếp trên laptop. #AILabs #GỡLỗiAI #ỨngDụngMáyTính #AI #GỡLỗi #LupisLabs #DebugAI #Agent #MáyTínhĐểBàn
https://www.reddit.com/r/SideProject/comments/1orqdhq/i_create_a_desktop_app_for_debug_ai_agent/
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#GoogleCloud is gaining momentum against AWS and Microsoft Azure, largely due to its focus on #AIstartups. The company works with nine out of ten leading #AIlabs and 60% of #generativeAI #startups, including #Lovable and #Windsurf. Google Cloud offers generous deals, such as $350,000 in cloud credits, to attract and support these startups. https://techcrunch.com/2025/09/18/how-ai-startups-are-fueling-googles-booming-cloud-business/?eicker.news #tech #media #news
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#GoogleCloud is gaining momentum against AWS and Microsoft Azure, largely due to its focus on #AIstartups. The company works with nine out of ten leading #AIlabs and 60% of #generativeAI #startups, including #Lovable and #Windsurf. Google Cloud offers generous deals, such as $350,000 in cloud credits, to attract and support these startups. https://techcrunch.com/2025/09/18/how-ai-startups-are-fueling-googles-booming-cloud-business/?eicker.news #tech #media #news
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#GoogleCloud is gaining momentum against AWS and Microsoft Azure, largely due to its focus on #AIstartups. The company works with nine out of ten leading #AIlabs and 60% of #generativeAI #startups, including #Lovable and #Windsurf. Google Cloud offers generous deals, such as $350,000 in cloud credits, to attract and support these startups. https://techcrunch.com/2025/09/18/how-ai-startups-are-fueling-googles-booming-cloud-business/?eicker.news #tech #media #news
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#NewYork state lawmakers passed the #RAISE Act, aiming to prevent #AI-related disasters: requires large #AIlabs to publish #safety reports and #incidents, with penalties for non-compliance. https://techcrunch.com/2025/06/13/new-york-passes-a-bill-to-prevent-ai-fueled-disasters/?eicker.news #tech #media #news
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#NewYork state lawmakers passed the #RAISE Act, aiming to prevent #AI-related disasters: requires large #AIlabs to publish #safety reports and #incidents, with penalties for non-compliance. https://techcrunch.com/2025/06/13/new-york-passes-a-bill-to-prevent-ai-fueled-disasters/?eicker.news #tech #media #news
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#NewYork state lawmakers passed the #RAISE Act, aiming to prevent #AI-related disasters: requires large #AIlabs to publish #safety reports and #incidents, with penalties for non-compliance. https://techcrunch.com/2025/06/13/new-york-passes-a-bill-to-prevent-ai-fueled-disasters/?eicker.news #tech #media #news
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The chaotic reality of contemporary AI labs
This was interesting from DeepMind’s Sholto Douglas about the reality of working in AI labs. They have billions of dollars flooding into them but they’re also scaling rapidly in a slightly chaotic way, working in ways that constantly throw up more things to explore than their existing capacity allows:
I also think that it’s underappreciated just how far from a perfect machine these labs are. It’s not like you have a thousand people optimizing the hell out of computer use and they’ve been trying as hard as they possibly can.
Everything at these labs, every single part of the model generation pipeline is the best effort pulled together under incredible time pressure, incredible constraints as these companies are rapidly growing, trying desperately to pull and upskill enough people to do the things that they need to do. I think it is best understood as with incredibly difficult prioritization problems.
#AILabs #bigTech #capitalism #corporations #investment #Research
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The chaotic reality of contemporary AI labs
This was interesting from DeepMind’s Sholto Douglas about the reality of working in AI labs. They have billions of dollars flooding into them but they’re also scaling rapidly in a slightly chaotic way, working in ways that constantly throw up more things to explore than their existing capacity allows:
I also think that it’s underappreciated just how far from a perfect machine these labs are. It’s not like you have a thousand people optimizing the hell out of computer use and they’ve been trying as hard as they possibly can.
Everything at these labs, every single part of the model generation pipeline is the best effort pulled together under incredible time pressure, incredible constraints as these companies are rapidly growing, trying desperately to pull and upskill enough people to do the things that they need to do. I think it is best understood as with incredibly difficult prioritization problems.
#AILabs #bigTech #capitalism #corporations #investment #Research