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$MSFT just introduced Critique which is a new multi-model deep research system inside M365 Copilot that lets users combine models to generate stronger reports & responses. Microsoft is increasingly positioning itself as the interface layer for enterprise AI by monetizing software subscriptions, Azure usage & inference demand without needing... show more

Shay Boloor

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41,260 Aufrufe • vor 6 Monaten •via X (Twitter)

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Introducing Critique, a new multi-model deep research system in M365 Copilot. You can use multiple models together to generate optimal responses and reports.

Introducing Critique, a new multi-model deep research system in M365 Copilot. You can use multiple models together to generate optimal responses and reports.

Satya Nadella

1,393,888 Aufrufe • vor 6 Monaten

The market is underappreciating that $MSFT already owns the enterprise workflow layer where AI adoption is likely to happen. Workers already live inside Outlook, Excel, PowerPoint, Teams, GitHub and Azure which gives Microsoft a built-in distribution advantage as AI gets embedded across the enterprise. Copilot is still early but the numbers are getting more meaningful with over 20M paid seats, paid seat additions up 250% YoY and customers with more than 50K seats quadrupling YoY Every 10M Copilot seats at ~$30 per user per month represents ~$3.6B of annualized revenue so if Copilot scales to 100M seats over the next several years then it becomes a major software revenue layer on top of Microsoft 365.

The market is underappreciating that $MSFT already owns the enterprise workflow layer where AI adoption is likely to happen. Workers already live inside Outlook, Excel, PowerPoint, Teams, GitHub and Azure which gives Microsoft a built-in distribution advantage as AI gets embedded across the enterprise. Copilot is still early but the numbers are getting more meaningful with over 20M paid seats, paid seat additions up 250% YoY and customers with more than 50K seats quadrupling YoY Every 10M Copilot seats at ~$30 per user per month represents ~$3.6B of annualized revenue so if Copilot scales to 100M seats over the next several years then it becomes a major software revenue layer on top of Microsoft 365.

Shay Boloor

117,810 Aufrufe • vor 3 Monaten

“Scientific progress is becoming computable.” DeepMind CEO Demis Hassabis says AI is becoming a scientific infrastructure layer, not just a productivity tool. At Google I/O, he introduced Gemini for Science: a new set of AI systems designed to help researchers read papers, write code, and generate hypotheses faster. The real shift is that research itself is starting to scale like software.

“Scientific progress is becoming computable.” DeepMind CEO Demis Hassabis says AI is becoming a scientific infrastructure layer, not just a productivity tool. At Google I/O, he introduced Gemini for Science: a new set of AI systems designed to help researchers read papers, write code, and generate hypotheses faster. The real shift is that research itself is starting to scale like software.

Jon Hernandez

25,478 Aufrufe • vor 4 Monaten

$MSFT is trading at a decade-low valuation because the market is focused on the near-term cost of AI while underpricing Microsoft’s role as one of the most important enterprise AI platforms in the world. The bigger shift is that Microsoft may move from only monetizing users to monetizing work where companies eventually pay not just for employee licenses but for the AI work being performed around each employee. That creates a ton of upside if Microsoft can monetize agents, tokens, workflows, security, governance and productivity across the enterprise stack in the new AI economy.

$MSFT is trading at a decade-low valuation because the market is focused on the near-term cost of AI while underpricing Microsoft’s role as one of the most important enterprise AI platforms in the world. The bigger shift is that Microsoft may move from only monetizing users to monetizing work where companies eventually pay not just for employee licenses but for the AI work being performed around each employee. That creates a ton of upside if Microsoft can monetize agents, tokens, workflows, security, governance and productivity across the enterprise stack in the new AI economy.

Shay Boloor

607,101 Aufrufe • vor 3 Monaten

FOMO IS LIVE ON ROBINHOOD CHAIN. Robinhood Chain is building an AI-native onchain economy, purpose-built to make real-world assets investable onchain. FLock now brings FOMO, the first Fair Open Model Offering, to Robinhood Crypto, extending that frontier from financial assets to artificial intelligence. AI Model Tokens aren’t just another crypto asset. They’re a new onchain primitive, backed by model inference through FLock API Platform, FLock’s enterprise-grade gateway for open-source AI models. Built for the agentic economy, FOMO connects Model Token value to real AI usage, giving anyone a new way to launch, trade, and back AI models. Thousands of AI agents are being tokenised. Now, the open-source intelligence powering them can be too. This is not a phase. This is a supercycle. Explore Model Tokens on FOMO. Now live for trading on Uniswap.

FOMO IS LIVE ON ROBINHOOD CHAIN. Robinhood Chain is building an AI-native onchain economy, purpose-built to make real-world assets investable onchain. FLock now brings FOMO, the first Fair Open Model Offering, to Robinhood Crypto, extending that frontier from financial assets to artificial intelligence. AI Model Tokens aren’t just another crypto asset. They’re a new onchain primitive, backed by model inference through FLock API Platform, FLock’s enterprise-grade gateway for open-source AI models. Built for the agentic economy, FOMO connects Model Token value to real AI usage, giving anyone a new way to launch, trade, and back AI models. Thousands of AI agents are being tokenised. Now, the open-source intelligence powering them can be too. This is not a phase. This is a supercycle. Explore Model Tokens on FOMO. Now live for trading on Uniswap.

FLock.io

17,326 Aufrufe • vor 1 Monat

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.

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 Aufrufe • vor 3 Monaten

At today's AI Summit, Runway CTO Kamil Sindi shared more on our first World Interface Model, Solaris. A new kind of operating system that builds itself as you interact with it. Every frame is generated in real-time in response to your intention. In his keynote, Kamil shared a clear vision for a future where all software is generated. Without a single line of code. Learn more about Solaris:

At today's AI Summit, Runway CTO Kamil Sindi shared more on our first World Interface Model, Solaris. A new kind of operating system that builds itself as you interact with it. Every frame is generated in real-time in response to your intention. In his keynote, Kamil shared a clear vision for a future where all software is generated. Without a single line of code. Learn more about Solaris:

Runway

16,974 Aufrufe • vor 13 Tagen

We’re building Copilot as a new OS for work that spans every model, every form factor, and every task. Today, we’re announcing our biggest update to Copilot to date, bringing four things together: · Autopilot: proactive and long-running agent built for the enterprise · Code: build apps with Copilot, hosted inside your company’s tenant · Home: Chat + Cowork together · Office: now fully embedded in Copilot (and Copilot embedded in Office, of course!) Plus, you can invoke Copilot in Teams, and we’re introducing Today, a proactive experience that surfaces the most important information from across M365 without needing to ask for it. The way we work is changing and so are our workflows. This update brings AI into that flow, from answering a question, to building an app, to getting work done on your behalf.

We’re building Copilot as a new OS for work that spans every model, every form factor, and every task. Today, we’re announcing our biggest update to Copilot to date, bringing four things together: · Autopilot: proactive and long-running agent built for the enterprise · Code: build apps with Copilot, hosted inside your company’s tenant · Home: Chat + Cowork together · Office: now fully embedded in Copilot (and Copilot embedded in Office, of course!) Plus, you can invoke Copilot in Teams, and we’re introducing Today, a proactive experience that surfaces the most important information from across M365 without needing to ask for it. The way we work is changing and so are our workflows. This update brings AI into that flow, from answering a question, to building an app, to getting work done on your behalf.

Satya Nadella

3,828,171 Aufrufe • vor 19 Tagen

In Ackman's latest interview he divides the AI market, stating that it is not a bubble at the foundational layer, but highly risky at the software/model layer. He sees "near infinite demand" for compute. He believes infrastructure providers such as Microsoft, Amazon, Meta, and even SpaceX will earn massive returns on their data center investments. He is highly skeptical of pure model layer businesses like OpenAI and Anthropic. He notes they face massive capital burn rates and an existential threat from open-source models such as Chinese model and SpaceX that are driving the cost of AI access effectively to zero. A business model reliant on charging for proprietary models will collapse if open-source alternatives are essentially free. $AMZN $META $MSFT $SPCX

In Ackman's latest interview he divides the AI market, stating that it is not a bubble at the foundational layer, but highly risky at the software/model layer. He sees "near infinite demand" for compute. He believes infrastructure providers such as Microsoft, Amazon, Meta, and even SpaceX will earn massive returns on their data center investments. He is highly skeptical of pure model layer businesses like OpenAI and Anthropic. He notes they face massive capital burn rates and an existential threat from open-source models such as Chinese model and SpaceX that are driving the cost of AI access effectively to zero. A business model reliant on charging for proprietary models will collapse if open-source alternatives are essentially free. $AMZN $META $MSFT $SPCX

CapexAndChill

138,165 Aufrufe • vor 3 Monaten

🚨 do you understand what just happened to Microsoft.. - They spent $13 billion on OpenAI. - Branded Copilot on every product they own. - Teams, Word, Excel, GitHub, Outlook, Windows itself. The result: fewer than 4.5% of 450 million Microsoft 365 users actually pay for it. So the fix is - another Copilot app.. → GitHub Copilot: separate app → Copilot Chat: separate app → Copilot Cowork: separate app → New "Autopilot": separate app → Super app to combine all of them: new separate app Internal slogan: "Delivering one Copilot" Meanwhile GitHub Copilot - the one product that actually works - is getting eaten alive by Cursor, Claude Code, and Grok Build. Microsoft didn't build too little AI. They built too much, in too many places, for too few people to care.

🚨 do you understand what just happened to Microsoft.. - They spent $13 billion on OpenAI. - Branded Copilot on every product they own. - Teams, Word, Excel, GitHub, Outlook, Windows itself. The result: fewer than 4.5% of 450 million Microsoft 365 users actually pay for it. So the fix is - another Copilot app.. → GitHub Copilot: separate app → Copilot Chat: separate app → Copilot Cowork: separate app → New "Autopilot": separate app → Super app to combine all of them: new separate app Internal slogan: "Delivering one Copilot" Meanwhile GitHub Copilot - the one product that actually works - is getting eaten alive by Cursor, Claude Code, and Grok Build. Microsoft didn't build too little AI. They built too much, in too many places, for too few people to care.

Kursakov

31,262 Aufrufe • vor 4 Monaten

As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️

As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️

NVIDIA AI

16,053 Aufrufe • vor 8 Monaten

THE PENTAGON WANTED ANTHROPIC’S AI WITH NO GUARDRAILS. NOW THEIR CEO WARNS OF A 10% TO 25% COLLAPSE RISK anthropic already had claude inside military workflows, but when the dod pushed for deeper access without safety limits, they refused to hand over a model that could help make lethal decisions without a human in the loop this is the openai-defector company now framed as a $965b ai giant after its models started eating enterprise software and helped erase $285b from software stocks almost overnight dario amodei drew the line at autonomous weapons and said they were willing to risk the company rather than let ai decide who lives and dies by itself now their internal model mythos is being framed as a cyber superweapon capable of prying open state-level secrets private ai labs blocking governments from weaponizing frontier models is wild and it’s only getting more serious from here

THE PENTAGON WANTED ANTHROPIC’S AI WITH NO GUARDRAILS. NOW THEIR CEO WARNS OF A 10% TO 25% COLLAPSE RISK anthropic already had claude inside military workflows, but when the dod pushed for deeper access without safety limits, they refused to hand over a model that could help make lethal decisions without a human in the loop this is the openai-defector company now framed as a $965b ai giant after its models started eating enterprise software and helped erase $285b from software stocks almost overnight dario amodei drew the line at autonomous weapons and said they were willing to risk the company rather than let ai decide who lives and dies by itself now their internal model mythos is being framed as a cyber superweapon capable of prying open state-level secrets private ai labs blocking governments from weaponizing frontier models is wild and it’s only getting more serious from here

Gipp 🦅

10,446 Aufrufe • vor 3 Monaten

We asked James Wang why Cerebras can run large models so quickly. His answer: Inference speed is mostly a memory problem. Instead of constantly pulling weights from external memory, Cerebras splits the model across multiple wafers and pipelines the layers together. “Inference is all about memory bandwidth.” “You don’t want to store weights external to the wafer, because the second it’s outside, it becomes much slower.” “We put the weights on the inside.” “The team built a new software stack that basically lets us split the models by layer and store them layer by layer across multiple wafers.” “That allows us to never have to read from external memory, and that’s what makes it so fast.” “For context, Claude is about 100 tokens per second.”

We asked James Wang why Cerebras can run large models so quickly. His answer: Inference speed is mostly a memory problem. Instead of constantly pulling weights from external memory, Cerebras splits the model across multiple wafers and pipelines the layers together. “Inference is all about memory bandwidth.” “You don’t want to store weights external to the wafer, because the second it’s outside, it becomes much slower.” “We put the weights on the inside.” “The team built a new software stack that basically lets us split the models by layer and store them layer by layer across multiple wafers.” “That allows us to never have to read from external memory, and that’s what makes it so fast.” “For context, Claude is about 100 tokens per second.”

MTS

27,935 Aufrufe • vor 4 Monaten

This is why Nebius will be a trillion dollar hyperscaler (Save this). Nebius is not building another GPU rental shop but rather building a vertically integrated hyperscaler that owns everything from the physical data center, to the server rack hardware it designs in house, to the software stack, to the inference delivery layer. Nearly every other neocloud is essentially a reseller of someone else's infrastructure but Nebius owns the full stack end to end and that distinction is the entire thesis. Here is why vertical integration is the winning architecture for the inference era. AWS and Azure were architected for general purpose computing and every AI workload they run sits on top of infrastructure that was never designed for it, patched, adapted and optimized after the fact. Nebius was built from day one specifically for AI which means every layer of the stack is purpose built and co optimized. The rack design, the networking topology, the cooling systems and the software that orchestrates it all are engineered together as a single system rather than assembled from parts that were never meant to work together. That architectural difference compounds with every passing quarter as AI workloads grow more complex and the performance gap between purpose built and general purpose infrastructure widens. The software layer is where the real competitive moat lives. Most infrastructure companies think of software as a wrapper around hardware while Nebius thinks of software as the product with hardware as the substrate it controls. The company is building an AI native cloud platform where the software layer handles model serving, inference optimization, fine tuning pipelines and developer tooling as first-class primitives. This matters because inference efficiency is almost entirely a software problem. Two companies running identical GPUs can deliver dramatically different performance and cost per token depending on how intelligently the software schedules, batches and routes inference requests across the cluster. Nebius is also building for a fundamental shift in how AI infrastructure gets consumed. Today, enterprise developers navigate massive cloud service catalogs spinning up clusters, managing configurations and building deep expertise in AWS or GCP-specific tooling. The next generation of builders will simply provision agents to interface with infrastructure directly. Nebius is architecting its software layer for that future , one where the interface between the developer and the compute abstraction layer looks nothing like what AWS built in 2006. The entire available capacity has been sold out every quarter. And that is the best possible validation that what Nebius is building is exactly what the market needs and that the market is willing to commit at a scale that makes the current valuation look like the beginning of a much longer story. Long Nebius and make sure to follow me Melvin for more overlooked AI stocks.

This is why Nebius will be a trillion dollar hyperscaler (Save this). Nebius is not building another GPU rental shop but rather building a vertically integrated hyperscaler that owns everything from the physical data center, to the server rack hardware it designs in house, to the software stack, to the inference delivery layer. Nearly every other neocloud is essentially a reseller of someone else's infrastructure but Nebius owns the full stack end to end and that distinction is the entire thesis. Here is why vertical integration is the winning architecture for the inference era. AWS and Azure were architected for general purpose computing and every AI workload they run sits on top of infrastructure that was never designed for it, patched, adapted and optimized after the fact. Nebius was built from day one specifically for AI which means every layer of the stack is purpose built and co optimized. The rack design, the networking topology, the cooling systems and the software that orchestrates it all are engineered together as a single system rather than assembled from parts that were never meant to work together. That architectural difference compounds with every passing quarter as AI workloads grow more complex and the performance gap between purpose built and general purpose infrastructure widens. The software layer is where the real competitive moat lives. Most infrastructure companies think of software as a wrapper around hardware while Nebius thinks of software as the product with hardware as the substrate it controls. The company is building an AI native cloud platform where the software layer handles model serving, inference optimization, fine tuning pipelines and developer tooling as first-class primitives. This matters because inference efficiency is almost entirely a software problem. Two companies running identical GPUs can deliver dramatically different performance and cost per token depending on how intelligently the software schedules, batches and routes inference requests across the cluster. Nebius is also building for a fundamental shift in how AI infrastructure gets consumed. Today, enterprise developers navigate massive cloud service catalogs spinning up clusters, managing configurations and building deep expertise in AWS or GCP-specific tooling. The next generation of builders will simply provision agents to interface with infrastructure directly. Nebius is architecting its software layer for that future , one where the interface between the developer and the compute abstraction layer looks nothing like what AWS built in 2006. The entire available capacity has been sold out every quarter. And that is the best possible validation that what Nebius is building is exactly what the market needs and that the market is willing to commit at a scale that makes the current valuation look like the beginning of a much longer story. Long Nebius and make sure to follow me Melvin for more overlooked AI stocks.

Melvin

34,306 Aufrufe • vor 3 Monaten

Cyber defense is entering the superintelligence era. ⚡ Introducing the CrowdStrike Cyber Superintelligence Lab, the first frontier AI research organization built for cyberdefense and AI safety. The Lab delivers CrowdStrike SafeMind, a family of purpose-built security models and harnesses. SafeMind runs as one system designed to deliver AI safety: an offensive model that finds the attack path, a defensive model that closes it, and the harnesses that operate both in the same loop. Built using NVIDIA Nemotron open models and using CoreWeave’s AI Cloud for training and inference, SafeMind goes beyond AI that simply identifies threats, it defeats them. Learn more:

Cyber defense is entering the superintelligence era. ⚡ Introducing the CrowdStrike Cyber Superintelligence Lab, the first frontier AI research organization built for cyberdefense and AI safety. The Lab delivers CrowdStrike SafeMind, a family of purpose-built security models and harnesses. SafeMind runs as one system designed to deliver AI safety: an offensive model that finds the attack path, a defensive model that closes it, and the harnesses that operate both in the same loop. Built using NVIDIA Nemotron open models and using CoreWeave’s AI Cloud for training and inference, SafeMind goes beyond AI that simply identifies threats, it defeats them. Learn more:

CrowdStrike

58,094 Aufrufe • vor 1 Monat

Fireworks AI is now available first-party in Microsoft Foundry, as part of a multi-year partnership, starting today in private preview. Enterprise AI has a speed problem. Most infrastructure can't keep up with how fast open models are moving. New architectures, fine-tuned weights, models that aren't on anyone's approved list yet. The usual answer was "manage your own serving infrastructure." That's a real cost. Fireworks runs the inference. Azure owns the governance. Your weights stay yours. Beyond custom weights, teams can also access the latest open models on day zero. No waiting for the marketplace to catch up. Serverless inference for prototyping, and a path to production that doesn't require re-architecting anything. Fireworks handles the serving layer. Azure handles the rest. Your boss handles the congratulations. Want in →

Fireworks AI is now available first-party in Microsoft Foundry, as part of a multi-year partnership, starting today in private preview. Enterprise AI has a speed problem. Most infrastructure can't keep up with how fast open models are moving. New architectures, fine-tuned weights, models that aren't on anyone's approved list yet. The usual answer was "manage your own serving infrastructure." That's a real cost. Fireworks runs the inference. Azure owns the governance. Your weights stay yours. Beyond custom weights, teams can also access the latest open models on day zero. No waiting for the marketplace to catch up. Serverless inference for prototyping, and a path to production that doesn't require re-architecting anything. Fireworks handles the serving layer. Azure handles the rest. Your boss handles the congratulations. Want in →

Fireworks AI

51,657 Aufrufe • vor 7 Monaten

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."

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

70,731 Aufrufe • vor 3 Monaten

The new Copilot is Microsoft’s bet that the AI race is moving from models to products. It doesn’t need to own the best model if Copilot can choose among them and keep the customer inside Microsoft. The potential secret weapon is Autopilot (what I’ve been calling Muse for Business). It knows your workflows, sits across the apps and data you already use, and keeps working even when you step away. It’s a preview of the battle to come in enterprise AI. OpenAI and Anthropic are racing from models into products. Can they build their own version of this? And can they match Microsoft on the boring-but-critical stuff: permissions, identity, auditability and control, as agents become more autonomous? My full conversation with Satya Nadella. We also talk open vs. closed models, US-China Summit, regulation and the infrastructure buildout. 00:00 Microsoft’s new Copilot 02:02 Why not just give us Autopilot? 05:18 Who pays for always-on AI? 08:06 Copilot picks the model 10:27 Chinese models and OpenAI’s lead 12:04 Competing with OpenAI and Anthropic 13:17 US–China AI talks 16:11 Does AI need new rules? 18:05 The data center backlash 20:40 Is AI being overbuilt? 23:44 Keeping humans in control

The new Copilot is Microsoft’s bet that the AI race is moving from models to products. It doesn’t need to own the best model if Copilot can choose among them and keep the customer inside Microsoft. The potential secret weapon is Autopilot (what I’ve been calling Muse for Business). It knows your workflows, sits across the apps and data you already use, and keeps working even when you step away. It’s a preview of the battle to come in enterprise AI. OpenAI and Anthropic are racing from models into products. Can they build their own version of this? And can they match Microsoft on the boring-but-critical stuff: permissions, identity, auditability and control, as agents become more autonomous? My full conversation with Satya Nadella. We also talk open vs. closed models, US-China Summit, regulation and the infrastructure buildout. 00:00 Microsoft’s new Copilot 02:02 Why not just give us Autopilot? 05:18 Who pays for always-on AI? 08:06 Copilot picks the model 10:27 Chinese models and OpenAI’s lead 12:04 Competing with OpenAI and Anthropic 13:17 US–China AI talks 16:11 Does AI need new rules? 18:05 The data center backlash 20:40 Is AI being overbuilt? 23:44 Keeping humans in control

Deirdre Bosa

343,940 Aufrufe • vor 19 Tagen

Emerging as the global frontier of model-reasoning has been a core vision of Affine since its inception. In pursuit of this vision, we've been working relentlessly to forge a repeatable training loop: a mechanism that uses benchmark driven post-training methodology to facilitate the improvement of reasoning itself. Through applying our training methodology onto Qwen3-32B as our base model, Affine’s miners have been able to push the Pareto frontier outward. We’ve now reached saturation; our models have become so good at reasoning that any further improvements are producing diminishing returns. As a result we now move our base model to Qwen3.6-35B-A3B. It provides a stronger basis, offering an extended context window, as well as an improved foundation for agentic work. The same training methodology will be applied going forward. Our objective is no longer simply to improve a single model, but to demonstrate that the process in itself is repeatable, scalable, and model agnostic. We view the commoditization of reasoning as vital to ensure our technology is not only transparent, but also co-owned by humanity.

Emerging as the global frontier of model-reasoning has been a core vision of Affine since its inception. In pursuit of this vision, we've been working relentlessly to forge a repeatable training loop: a mechanism that uses benchmark driven post-training methodology to facilitate the improvement of reasoning itself. Through applying our training methodology onto Qwen3-32B as our base model, Affine’s miners have been able to push the Pareto frontier outward. We’ve now reached saturation; our models have become so good at reasoning that any further improvements are producing diminishing returns. As a result we now move our base model to Qwen3.6-35B-A3B. It provides a stronger basis, offering an extended context window, as well as an improved foundation for agentic work. The same training methodology will be applied going forward. Our objective is no longer simply to improve a single model, but to demonstrate that the process in itself is repeatable, scalable, and model agnostic. We view the commoditization of reasoning as vital to ensure our technology is not only transparent, but also co-owned by humanity.

Affine

14,866 Aufrufe • vor 3 Monaten