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Is Anthropic altering model performance to force costly upgrades? Chapter Co-Founder and CEO Cobi Blumenfeld-Gantz outlines a shift his team recently noticed: "Anthropic has kind of taken a page out of the Apple playbook, where they have...decreased the quality of the prior models right before they launched the new...

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36,895 görüntüleme • 5 ay önce •via X (Twitter)

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Tech With Matteo profil fotoğrafı
Tech With Matteo5 ay önce

@CobiBGantz good breakdown, a 25 word cap on tool calls would explain a lot of the weird truncation people were seeing, glad it got reverted

Tech With Matteo profil fotoğrafı
Tech With Matteo5 ay önce

@CobiBGantz as someone using claude daily to build stuff this claim doesnt match what i noticed, the reverted system prompt change explains way more than intentional degradation

John Allen Berger profil fotoğrafı
John Allen Berger5 ay önce

@CobiBGantz Yep learn from the champ for squeezing blood out of a turnip. Karma is going to bite your snotty asses in the coming years. Your AI on local devices is fundamentally flawed and dead on arrival. Thank Tim Cook for this fuckup

AGI Pulse profil fotoğrafı
AGI Pulse5 ay önce

@CobiBGantz Very much so. That's why we will see AI bubble burst

Houman Asefi profil fotoğrafı
Houman Asefi5 ay önce

@CobiBGantz 'Anthropic decreased quality of prior models'..based on what evidence?

Michael Spencer profil fotoğrafı
Michael Spencer5 ay önce

@CobiBGantz If you are a frontier lab and develop the best models, what kind of incentives do you have to boost perceived productivity and profitability?

Daniel M. Karim - Aka The Ghost profil fotoğrafı
Daniel M. Karim - Aka The Ghost5 ay önce

@CobiBGantz Token transparency start up request.

Aris Chronos profil fotoğrafı
Aris Chronos5 ay önce

@CobiBGantz I think for a company built on 'ethical AI', the Apple playbook is quite the look.

Carlos Aggio profil fotoğrafı
Carlos Aggio5 ay önce

@CobiBGantz To me it’s simpler. They’ve reallocated priority inference compute to the newer model and we intuitively know that frontier LLMs with access to more compute at inference time seem to perform better.. besides the “impression of degradation”

F U Z Z Y G O A T ™ profil fotoğrafı
F U Z Z Y G O A T ™5 ay önce

This kind of gaming of the audience feels inevitable. Throttle performance to justify an upgrade, add adverts so you can charge people to remove them and get back to the experience they already had. It’s all coming. Right now we’re in the honeymoon period, the equivalent of the 1980s and 1990s internet era, the golden age before everything became cookie banners, GDPR pop-ups, adverts, subscriptions, and paywalls. Enjoy it while it lasts.

Winston B. profil fotoğrafı
Winston B.5 ay önce

The assumption baked in is deliberate degradation for upsell leverage, which doesn't hold up against what Anthropic actually disclosed: a system-prompt addition in mid-April that hard-capped Claude's text-between-tool-calls to 25 words and final responses to 100 words caused a measurable drop in coding eval quality before it was reverted.

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Anthropic, the tech company that recently clashed with the Pentagon, says it has developed a new AI model that is too powerful to be released to the public.
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JD Vance: "There are things at some of the AI companies you see happening, where you feel like there is some really, really weird spiritual dark energy. Like one of the companies that develops these models, when they get rid of one model and announce a new model, they actually hold a burial ceremony for the old model, and the employees apparently will cry."

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True Ventures Tony Conrad says Polsia's founder let Anthropic "run kind of crazy" and woke up to a $1.5 million bill. Now he's looking at Chinese open-source models: "We have a company, True, Polsia, and Ben's been really public about, he just let Anthropic run kind of crazy with Anthropic tokens, and he woke up and he had, like, a million-and-a-half-dollar bill." "He's like, no, no, that's not gonna work. I'm gonna go look at some of the Chinese models, open source models, and find the right balance." "We're in a really interesting moment where how do you find cheaper approaches to the tech stack that aren't just all Anthropic and OpenAI-driven. That's a very expensive Ferrari-type model." "They may be the best. I'm not gonna opine on that, but they may be the best. That might be worth it. But you might be able to do really great stuff with 95% of that." True Ventures Madeline Minshew

MTS

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The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.
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The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

Vikram M

21,463 görüntüleme • 4 ay önce

SITUATION EXPLAINED: Why is the Bridgewater Thinking Machines paper a blueprint for every enterprise that has ever paid frontier model prices for generic outputs? 47fucb4r8curb4fc8f8r4bfic8r: "What Thinking Machines and Bridgewater did together is a blueprint for how a wide variety of stakeholders within the market can benefit tremendously from using LLMs and integrating them into a larger enterprise system." "They got a pretty old model. A Qwen3 model. Two hundred and thirty-five billion parameters. Not a new model. Not a very sophisticated or impressive state-of-the-art model. And they essentially fine-tuned it on Bridgewater's own proprietary data." "A financial analyst gets a ton of financial documents and has to filter which are worth looking at. Then evaluate which news pieces are relevant. They have a lot of tacit knowledge from doing the job on where to look. What they did was inject a lot of this tacit knowledge into the model through fine-tuning." "Stop obsessing over state-of-the-art models that are the most intelligent possible. Focus on targeting a very specific LLM specifically trained for a specific function to do that function better than the alternatives on the market." "This old non-state-of-the-art model got 84.7% accuracy versus 78% for all the state-of-the-art models. A massive improvement. And the kicker is they got a better performing model that is 14 times cheaper to use."
6:06

SITUATION EXPLAINED: Why is the Bridgewater Thinking Machines paper a blueprint for every enterprise that has ever paid frontier model prices for generic outputs? 47fucb4r8curb4fc8f8r4bfic8r: "What Thinking Machines and Bridgewater did together is a blueprint for how a wide variety of stakeholders within the market can benefit tremendously from using LLMs and integrating them into a larger enterprise system." "They got a pretty old model. A Qwen3 model. Two hundred and thirty-five billion parameters. Not a new model. Not a very sophisticated or impressive state-of-the-art model. And they essentially fine-tuned it on Bridgewater's own proprietary data." "A financial analyst gets a ton of financial documents and has to filter which are worth looking at. Then evaluate which news pieces are relevant. They have a lot of tacit knowledge from doing the job on where to look. What they did was inject a lot of this tacit knowledge into the model through fine-tuning." "Stop obsessing over state-of-the-art models that are the most intelligent possible. Focus on targeting a very specific LLM specifically trained for a specific function to do that function better than the alternatives on the market." "This old non-state-of-the-art model got 84.7% accuracy versus 78% for all the state-of-the-art models. A massive improvement. And the kicker is they got a better performing model that is 14 times cheaper to use."

MTS

93,185 görüntüleme • 4 ay önce

My Tesla standard Model Y has Swing Mode where the front air vents oscillate side to side so airflow distributes evenly throughout the car. The thing that many people do not know about Tesla is that they don’t wait for a “new model year” to add new features. They don’t sit around planning for a 2025 version or a 2026 version. When a feature is ready, the team just builds it into the cars right then and there. You get whatever the latest tech is the moment your Tesla rolls out of the factory. This is one of the key differences between Tesla and legacy auto.
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My Tesla standard Model Y has Swing Mode where the front air vents oscillate side to side so airflow distributes evenly throughout the car. The thing that many people do not know about Tesla is that they don’t wait for a “new model year” to add new features. They don’t sit around planning for a 2025 version or a 2026 version. When a feature is ready, the team just builds it into the cars right then and there. You get whatever the latest tech is the moment your Tesla rolls out of the factory. This is one of the key differences between Tesla and legacy auto.

Teslaconomics

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Anthropic has created an AI model that “can get into almost any software in the world,” former AI company founder and CEO Matt Shumer says — but the company is not releasing it to the public: “I have never seen something like this — a company that is about to IPO and having such a moral compass.” Shumer shares what makes the AI model so powerful and potentially dangerous.
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Anthropic has created an AI model that “can get into almost any software in the world,” former AI company founder and CEO Matt Shumer says — but the company is not releasing it to the public: “I have never seen something like this — a company that is about to IPO and having such a moral compass.” Shumer shares what makes the AI model so powerful and potentially dangerous.

CBS Mornings

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Engram cofounder Jack Morris just raised $98M to build a new type of AI. He says models don't need to get smarter over time. Instead, they just need to know you better and better over time. Jack describes what he's building: "Our product is a new type of AI. We have a pretty different vision from a lot of the frontier labs, which are working on one model per lab, and trying to make that model smarter every month." "There's another way to think about it, which is that the model doesn't need to get smarter every month. It needs to know you better." "So we're working on a whole different stack, which is a way to train models that train themselves to know your world better and adjust to the things that you say." "So: new ways of training, new ways of running the models."

TBPN

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