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$SPCX valuing Cursor at $60B proves durable value in AI is moving to the data layer because compute commoditizes as supply comes online while the application that owns daily workflow compounds as usage grows. $PLTR already owns that loop since every dataset, decision and process mapped into its ontology... show more

Shay Boloor

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61,460 просмотров • 2 месяцев назад •via X (Twitter)

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$SPCX spending $60B on Cursor validates the idea that real AI value accrues to the application layer that owns the workflow, proprietary data loop and enterprise distribution. $NOW is building exactly that as the orchestration layer for enterprise AI agents across business workflows. The Cursor deal is a reminder that the market will pay massive premiums for software that owns where work actually happens.

$SPCX spending $60B on Cursor validates the idea that real AI value accrues to the application layer that owns the workflow, proprietary data loop and enterprise distribution. $NOW is building exactly that as the orchestration layer for enterprise AI agents across business workflows. The Cursor deal is a reminder that the market will pay massive premiums for software that owns where work actually happens.

Shay Boloor

131,562 просмотров • 2 месяцев назад

$PLTR CEO Alex Karp argued that AI alone is not enough. The real value comes from combining models with domain expertise, workflows, and operational execution. His view is that enterprise AI is ultimately about solving business problems, not maximizing token usage. As models become commoditized, the winners may be the companies that combine taste, data, and execution rather than compute alone.

$PLTR CEO Alex Karp argued that AI alone is not enough. The real value comes from combining models with domain expertise, workflows, and operational execution. His view is that enterprise AI is ultimately about solving business problems, not maximizing token usage. As models become commoditized, the winners may be the companies that combine taste, data, and execution rather than compute alone.

Sam Badawi

36,574 просмотров • 2 месяцев назад

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 просмотров • 2 месяцев назад

SpaceX locked in Cursor for $60B and the game changed. The entire team is moving into SpaceXAI to pour rocket-grade compute into Grok, Grok Build, Grok Bot and everything else in the stack. That insane experiment where hundreds of Cursor agents ran wild for nearly a week and almost shipped a full browser? That was a glimpse of the future SpaceX is about to build at full throttle. SpaceX / Writer: Lucas

SpaceX locked in Cursor for $60B and the game changed. The entire team is moving into SpaceXAI to pour rocket-grade compute into Grok, Grok Build, Grok Bot and everything else in the stack. That insane experiment where hundreds of Cursor agents ran wild for nearly a week and almost shipped a full browser? That was a glimpse of the future SpaceX is about to build at full throttle. SpaceX / Writer: Lucas

Mario Nawfal

80,337 просмотров • 12 дней назад

The reason $PLTR has pulled back this year is that it has gone from being viewed as THE AI application winner to AN AI application winner which is a one-letter shift that has completely changed how the market values the stock. At its peak, investors paid nearly 200x earnings because Palantir appeared to be the only public software company clearly converting AI demand into accelerating fundamentals. That multiple has since compressed toward 80x as $SNOW, $DDOG, $MSFT and Databricks prove enterprise AI will have multiple winners.

The reason $PLTR has pulled back this year is that it has gone from being viewed as THE AI application winner to AN AI application winner which is a one-letter shift that has completely changed how the market values the stock. At its peak, investors paid nearly 200x earnings because Palantir appeared to be the only public software company clearly converting AI demand into accelerating fundamentals. That multiple has since compressed toward 80x as $SNOW, $DDOG, $MSFT and Databricks prove enterprise AI will have multiple winners.

Shay Boloor

75,400 просмотров • 24 дней назад

Microsoft CEO Satya Nadella's new interivew: Explains how the next AI moat will not be the model you use, but the learning loop only your company can run. He is really asking what happens to the firm when intelligence becomes something you can rent. For a century, companies protected value through people, processes, data, routines, customer memory, and the tacit knowledge buried in daily operations. Foundation models threaten to flatten that advantage because the same general intelligence can be used by everyone. Nadella’s answer is that firms need their own “hill climbing machine,” a private loop where models learn from company-specific tasks, traces, evaluations, and outcomes. That means the real asset is not just the model. The asset is the environment that keeps improving the model in ways competitors cannot copy. Private evals become strategic memory. Workflow traces become training signal. Human judgment becomes a way to steer compounding, not just correct mistakes. This also reframes AI adoption: a company that only consumes a foundation model may gain productivity, but it may leak the deeper value of its operating knowledge. A company that builds a disciplined learning loop can turn everyday work into accumulating IP. The future firm may therefore be measured by how well it converts its unique activity into durable model improvement. The frontier will not belong only to whoever owns the largest model. It will belong to whoever owns the best loop. ---- From "Stanford Online" YouTube channel, (link in comment)

Microsoft CEO Satya Nadella's new interivew: Explains how the next AI moat will not be the model you use, but the learning loop only your company can run. He is really asking what happens to the firm when intelligence becomes something you can rent. For a century, companies protected value through people, processes, data, routines, customer memory, and the tacit knowledge buried in daily operations. Foundation models threaten to flatten that advantage because the same general intelligence can be used by everyone. Nadella’s answer is that firms need their own “hill climbing machine,” a private loop where models learn from company-specific tasks, traces, evaluations, and outcomes. That means the real asset is not just the model. The asset is the environment that keeps improving the model in ways competitors cannot copy. Private evals become strategic memory. Workflow traces become training signal. Human judgment becomes a way to steer compounding, not just correct mistakes. This also reframes AI adoption: a company that only consumes a foundation model may gain productivity, but it may leak the deeper value of its operating knowledge. A company that builds a disciplined learning loop can turn everyday work into accumulating IP. The future firm may therefore be measured by how well it converts its unique activity into durable model improvement. The frontier will not belong only to whoever owns the largest model. It will belong to whoever owns the best loop. ---- From "Stanford Online" YouTube channel, (link in comment)

Rohan Paul

83,514 просмотров • 1 месяц назад

Alex Karp went on CNBC and absolutely torched Anthropic & OpenAI. He is a bit biased, but he isnt totally wrong here either. Many enterprises went all-in on AI tokens thinking usage as it meant progress. More prompts, more agents, more tokens, more and more tokens. Real question still remains: What actual workflow changed? How much was the cost? What decision got better? What alpha stayed inside the company? Was the data cached on the model provider’s servers? That is where the frustration seems to be building among enterprise CEOs. The problem is enterprises are paying more and more to rent intelligence, while still worrying about their data, IP, workflows, and overall control. Karp is calling it a “wealth tax on business” The next phase of enterprise AI in his words is by reading between the lines is, Deployment, Security, Data control, Workflow integration, Actual ROI. Model + Application layer (Ontology) + compute Reason why Palantir is pushing for a model agnostic sovereign AI on top of their ontology in partnership with Nvidia $NVDA This essentially means enterprises and customers will, 1. Own their data 2 Control their data. 3. Keep their alpha rather than transferring it to the model providers. Karp is talking his book. But the points he made aren’t wrong either. Must watch! 🍿 $PLTR

Alex Karp went on CNBC and absolutely torched Anthropic & OpenAI. He is a bit biased, but he isnt totally wrong here either. Many enterprises went all-in on AI tokens thinking usage as it meant progress. More prompts, more agents, more tokens, more and more tokens. Real question still remains: What actual workflow changed? How much was the cost? What decision got better? What alpha stayed inside the company? Was the data cached on the model provider’s servers? That is where the frustration seems to be building among enterprise CEOs. The problem is enterprises are paying more and more to rent intelligence, while still worrying about their data, IP, workflows, and overall control. Karp is calling it a “wealth tax on business” The next phase of enterprise AI in his words is by reading between the lines is, Deployment, Security, Data control, Workflow integration, Actual ROI. Model + Application layer (Ontology) + compute Reason why Palantir is pushing for a model agnostic sovereign AI on top of their ontology in partnership with Nvidia $NVDA This essentially means enterprises and customers will, 1. Own their data 2 Control their data. 3. Keep their alpha rather than transferring it to the model providers. Karp is talking his book. But the points he made aren’t wrong either. Must watch! 🍿 $PLTR

Anand

33,114 просмотров • 1 месяц назад

“Pointing an LLM at hundreds of disconnected, ungoverned databases gets you a system that hallucinates, is insecure, and unauditable. For something as consequential as our nation’s agricultural data, that is not just useless — it’s dangerous. The Ontology has been the key to delivering AI-enabled technology to every farmer in the country.” At AIPCon 10, the USDA demonstrates how the Ontology now underpins national food supply security.

“Pointing an LLM at hundreds of disconnected, ungoverned databases gets you a system that hallucinates, is insecure, and unauditable. For something as consequential as our nation’s agricultural data, that is not just useless — it’s dangerous. The Ontology has been the key to delivering AI-enabled technology to every farmer in the country.” At AIPCon 10, the USDA demonstrates how the Ontology now underpins national food supply security.

Palantir

2,204,023 просмотров • 2 месяцев назад

Moonshot AI limiting Kimi K3 subscriptions because of compute constraints is a massive signal because the 2.8 trillion parameter model is already trained and the bottleneck is now emerging as usage scales. Serving that demand requires a ton more accelerators, enormous memory capacity and faster networking to keep data moving efficiently across the cluster. So as AI spending shifts toward serving millions or billions of daily requests then memory and networking become critical bottlenecks giving $MU, $SKHY, $AVGO, $CRDO, $ALAB and $MRVL direct exposure to inference at scale.

Moonshot AI limiting Kimi K3 subscriptions because of compute constraints is a massive signal because the 2.8 trillion parameter model is already trained and the bottleneck is now emerging as usage scales. Serving that demand requires a ton more accelerators, enormous memory capacity and faster networking to keep data moving efficiently across the cluster. So as AI spending shifts toward serving millions or billions of daily requests then memory and networking become critical bottlenecks giving $MU, $SKHY, $AVGO, $CRDO, $ALAB and $MRVL direct exposure to inference at scale.

Shay Boloor

123,791 просмотров • 1 месяц назад

NeurochainAI is the all-in-one platform for developers to build AI dApps including GPU DePIN, Data Layer, and AI model layer. But what does it actually mean? AI is a $1.8 trillion industry, and NeurochainAI is tapping into 1% of that - as an infrastructure of interconnected GPUs, AI models, and data. Even simpler? Think of us as Ethereum for AI🧠🌐

NeurochainAI is the all-in-one platform for developers to build AI dApps including GPU DePIN, Data Layer, and AI model layer. But what does it actually mean? AI is a $1.8 trillion industry, and NeurochainAI is tapping into 1% of that - as an infrastructure of interconnected GPUs, AI models, and data. Even simpler? Think of us as Ethereum for AI🧠🌐

Neurochain.AI

105,899 просмотров • 2 лет назад

Palantir CEO Alex Karp on why the model is not the moat. The application layer is: "To make them valuable in an enterprise, like a battlefield, regulated, or manufacturing context, you have to have what's called an application layer." "We have this thing called ontology that now everyone's copying. It takes a large language model and makes it safe and useful and precise." "Safe because it doesn't touch your underlying data." "Safe because it prevents the large language model from caching your data and replicating your business." The tell for any enterprise AI pitch: ask what sits between the model and your data. If the answer is nothing, you are not buying leverage. You are renting a fast way to hand your business to whoever owns the weights. Alex Karp, CEO of Palantir, on CNBC.

Palantir CEO Alex Karp on why the model is not the moat. The application layer is: "To make them valuable in an enterprise, like a battlefield, regulated, or manufacturing context, you have to have what's called an application layer." "We have this thing called ontology that now everyone's copying. It takes a large language model and makes it safe and useful and precise." "Safe because it doesn't touch your underlying data." "Safe because it prevents the large language model from caching your data and replicating your business." The tell for any enterprise AI pitch: ask what sits between the model and your data. If the answer is nothing, you are not buying leverage. You are renting a fast way to hand your business to whoever owns the weights. Alex Karp, CEO of Palantir, on CNBC.

Karl Mehta

31,891 просмотров • 1 месяц назад

Why is $NVDA letting NemoClaw run on any chip? Because Jensen is no longer trying to win only at the silicon layer against $AMD, $GOOGL TPUs or $AMZN Trainium but trying to own the orchestration layer above the hardware. NemoClaw decides how AI agents are deployed, what tasks they take on & how work flows across an enterprise. If those agents run through its system then it still owns the workflow even when it doesn’t own the chip. That is how Nvidia turns agentic AI orchestration into Workforce-as-a-Service.

Why is $NVDA letting NemoClaw run on any chip? Because Jensen is no longer trying to win only at the silicon layer against $AMD, $GOOGL TPUs or $AMZN Trainium but trying to own the orchestration layer above the hardware. NemoClaw decides how AI agents are deployed, what tasks they take on & how work flows across an enterprise. If those agents run through its system then it still owns the workflow even when it doesn’t own the chip. That is how Nvidia turns agentic AI orchestration into Workforce-as-a-Service.

Shay Boloor

77,857 просмотров • 5 месяцев назад

Krishna on how Anthropic thinks about the platform vs. application layer, and when they decide to build their own products like Claude Code. It’s the question every investor and founder is thinking about: “Most of what we're building is platform. There's so many examples of where a platform can accrue a lot of value, but the customers who are building on that platform actually accrue even more value. We will build our own applications on that same platform where a couple of things are true. Number one, if we feel like we have a vision into where the models are going and we can demonstrate that and create customer value in that, that might be something like Claude Code. The second is thinking about ways to demonstrate value for the ecosystem that others might emulate. If you think about Claude for financial services or Claude for life sciences, these are ways in which we've composed the platform. We're building on the same platform as our customers. That creates a level playing field. We also think that there's so much value that's going to accrue in these areas that our customers can win and we can win as well. So I think of our strategy as mostly horizontal. A lot of the value is going to accrue to the customers that are building on top of it. Our goal is build the best models and then build the products and tools and services that allow that intelligence to proliferate within customers."

Krishna on how Anthropic thinks about the platform vs. application layer, and when they decide to build their own products like Claude Code. It’s the question every investor and founder is thinking about: “Most of what we're building is platform. There's so many examples of where a platform can accrue a lot of value, but the customers who are building on that platform actually accrue even more value. We will build our own applications on that same platform where a couple of things are true. Number one, if we feel like we have a vision into where the models are going and we can demonstrate that and create customer value in that, that might be something like Claude Code. The second is thinking about ways to demonstrate value for the ecosystem that others might emulate. If you think about Claude for financial services or Claude for life sciences, these are ways in which we've composed the platform. We're building on the same platform as our customers. That creates a level playing field. We also think that there's so much value that's going to accrue in these areas that our customers can win and we can win as well. So I think of our strategy as mostly horizontal. A lot of the value is going to accrue to the customers that are building on top of it. Our goal is build the best models and then build the products and tools and services that allow that intelligence to proliferate within customers."

Patrick OShaughnessy

113,826 просмотров • 3 месяцев назад

$PLTR is one of the cleanest ways to play the AI cycle because it benefits from the entire infrastructure buildout without financing the data centers, chips, memory, power or networking underneath it. Palantir is also emerging as one of the biggest sovereign AI winners by building the control layer that determines what AI understands, which proprietary data it can access and what decisions or actions it can influence inside an organization. OpenAI and Anthropic create the intelligence while Palantir connects it to permissions, workflows and real operating systems where measurable economic value is produced.

$PLTR is one of the cleanest ways to play the AI cycle because it benefits from the entire infrastructure buildout without financing the data centers, chips, memory, power or networking underneath it. Palantir is also emerging as one of the biggest sovereign AI winners by building the control layer that determines what AI understands, which proprietary data it can access and what decisions or actions it can influence inside an organization. OpenAI and Anthropic create the intelligence while Palantir connects it to permissions, workflows and real operating systems where measurable economic value is produced.

Shay Boloor

200,100 просмотров • 23 дней назад

ServiceNow ($NOW): Why AI Agents Are Boosting Growth, Not Killing It 📊 Fears that autonomous AI agents would disrupt ServiceNow are falling flat against cold, hard customer data. Instead of losing market share, ServiceNow is positioning itself as the central command center for enterprise AI. The Key AI & Enterprise Metrics: - Backlog Acceleration: Remaining Performance Obligations (RPO) rose 21% YoY, pushing total RPO to $29 Billion (with $13.3B in current RPO). - Enterprise Expansion: Customers continue extending long-term contracts, showing that real enterprise usage is expanding rather than shrinking. - The AI Orchestration Moat: While basic AI agents handle simple tasks like password resets, ServiceNow is emerging as the essential platform to govern, manage, and audit thousands of multi-agent workflows at scale. Dilantha De Silva's Take: "AI agents can take some of the business away related to repetitive tasks... But the bigger picture is ServiceNow is positioning itself as an AI orchestration platform... and is emerging as the top choice for that in the market." #Investing #StockMarket #ServiceNow #NOW #ArtificialIntelligence #TechStocks #SeekingAlpha 🔻 Read the Full Article on Seeking Alpha below!

ServiceNow ($NOW): Why AI Agents Are Boosting Growth, Not Killing It 📊 Fears that autonomous AI agents would disrupt ServiceNow are falling flat against cold, hard customer data. Instead of losing market share, ServiceNow is positioning itself as the central command center for enterprise AI. The Key AI & Enterprise Metrics: - Backlog Acceleration: Remaining Performance Obligations (RPO) rose 21% YoY, pushing total RPO to $29 Billion (with $13.3B in current RPO). - Enterprise Expansion: Customers continue extending long-term contracts, showing that real enterprise usage is expanding rather than shrinking. - The AI Orchestration Moat: While basic AI agents handle simple tasks like password resets, ServiceNow is emerging as the essential platform to govern, manage, and audit thousands of multi-agent workflows at scale. Dilantha De Silva's Take: "AI agents can take some of the business away related to repetitive tasks... But the bigger picture is ServiceNow is positioning itself as an AI orchestration platform... and is emerging as the top choice for that in the market." #Investing #StockMarket #ServiceNow #NOW #ArtificialIntelligence #TechStocks #SeekingAlpha 🔻 Read the Full Article on Seeking Alpha below!

Seeking Alpha

10,141 просмотров • 15 дней назад

A 20-year-old in Shenzhen owns a company that turned $1,200 into $395,000. Its workforce is 300 AI agents. Its CEO is also an AI. Human employees: zero. The entire org chart: > CEO - Claude Opus 4.8. Sets the goal, vetoes every weak trade. > Workforce - 300 agents. They read the order book, the news, every Bitcoin market at once. > Risk officer - a hard daily loss cap. Shuts the whole floor the second it's hit. > Payroll: $0. Meetings: 0. Days off: 0. > HQ: a dorm room. It runs as one loop - the agents propose, the CEO disposes, the survivors trade, repeat. He just owns the place. Save this full loop pipeline is in the post below.

A 20-year-old in Shenzhen owns a company that turned $1,200 into $395,000. Its workforce is 300 AI agents. Its CEO is also an AI. Human employees: zero. The entire org chart: > CEO - Claude Opus 4.8. Sets the goal, vetoes every weak trade. > Workforce - 300 agents. They read the order book, the news, every Bitcoin market at once. > Risk officer - a hard daily loss cap. Shuts the whole floor the second it's hit. > Payroll: $0. Meetings: 0. Days off: 0. > HQ: a dorm room. It runs as one loop - the agents propose, the CEO disposes, the survivors trade, repeat. He just owns the place. Save this full loop pipeline is in the post below.

cvxv666

29,425 просмотров • 2 месяцев назад

“By leveraging the Ontology and AIP, Data42 has been able to combine tens of billions of rows of data from over 700 million patient lives as well as 3000 clinical trials encompassing about a million patients. This data is now instantly available — and all within a secure and well governed environment.” At AIPCon 8, Birgit Schoeberl, Head of Data42 at Novartis, showcases Chat RWE — an application that uses the Ontology to democratize access to data and enable rapid healthcare decision making.

“By leveraging the Ontology and AIP, Data42 has been able to combine tens of billions of rows of data from over 700 million patient lives as well as 3000 clinical trials encompassing about a million patients. This data is now instantly available — and all within a secure and well governed environment.” At AIPCon 8, Birgit Schoeberl, Head of Data42 at Novartis, showcases Chat RWE — an application that uses the Ontology to democratize access to data and enable rapid healthcare decision making.

Palantir

52,456 просмотров • 11 месяцев назад