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My thesis on $PATH in 60 seconds: Everyone thinks agentic AI kills UiPath. I think it's the opposite. AI agents are powerful but useless without orchestration. Someone has to deploy them, govern them, and connect them to the 50 different systems inside a Fortune 500 company. That's what UiPath...

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Jason Luongo

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27,595 просмотров • 6 месяцев назад •via X (Twitter)

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Andrew Ng: "The most important trend in AI is agentic AI." in 30-minutes talk he breaks down the agentic orchestration layer and how he actually builds with agents: step 1 → prototype fast. what used to take 3 engineers and 6 months - his team builds in an afternoon step 2 → run evals, not vibes. "new model comes out → run evals → switch in two days" step 3 → iterate in loops. outline → search → draft → revise → loop. "this is the heart of agentic systems" Worth more than any paid course on building agents. Watch the talk, then read the guide on building self-improving agentic systems below.
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Andrew Ng: "The most important trend in AI is agentic AI." in 30-minutes talk he breaks down the agentic orchestration layer and how he actually builds with agents: step 1 → prototype fast. what used to take 3 engineers and 6 months - his team builds in an afternoon step 2 → run evals, not vibes. "new model comes out → run evals → switch in two days" step 3 → iterate in loops. outline → search → draft → revise → loop. "this is the heart of agentic systems" Worth more than any paid course on building agents. Watch the talk, then read the guide on building self-improving agentic systems below.

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Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.
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Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

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Mark Zuckerberg: Most businesses will not own frontier AI in the way Meta or OpenAI does. But many will end up with something that feels like their own AI: a customized operational layer that reflects how that company actually works. He says, "OpenAI, Google, they're building an AI. But I think we're gonna have a lot of different AI systems, just like we're gonna have, we have a lot of different apps. I think in the future, every business, just like I have a website and a phone number and an email address, a social media account, is also going to have an AI that can interact with their customers to help them sell things, help them give support." --- What he is really describing that a company’s “own AI” will usually not be a frontier model trained from scratch, but a layer built on top of shared models, shaped by its products, policies, customer history, and way of working. Support, sales, and basic operations can be handled through a system that knows the business well enough to answer, route, recommend, and escalate without feeling generic. --- From 'Cleo Abram' YT channel (link in comment)
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Mark Zuckerberg: Most businesses will not own frontier AI in the way Meta or OpenAI does. But many will end up with something that feels like their own AI: a customized operational layer that reflects how that company actually works. He says, "OpenAI, Google, they're building an AI. But I think we're gonna have a lot of different AI systems, just like we're gonna have, we have a lot of different apps. I think in the future, every business, just like I have a website and a phone number and an email address, a social media account, is also going to have an AI that can interact with their customers to help them sell things, help them give support." --- What he is really describing that a company’s “own AI” will usually not be a frontier model trained from scratch, but a layer built on top of shared models, shaped by its products, policies, customer history, and way of working. Support, sales, and basic operations can be handled through a system that knows the business well enough to answer, route, recommend, and escalate without feeling generic. --- From 'Cleo Abram' YT channel (link in comment)

Rohan Paul

429,740 просмотров • 4 месяцев назад

Sequoia's Pat Grady says systems of record software companies are relatively safe from AI disruption, but that systems of engagement companies built on top of them are in trouble: "The first wave of the on-prem to cloud transition was transitioning systems of record — the Workdays, Salesforces, and Servicenows of the world." "The second layer on top of that was the systems of engagement. The systems of record might own the core database, but then there are a bunch of different workflow applications that reside on top." "I think what we're going to see with the wave of AI software is a third layer on top of those." "It's the layer that does the work. It's the agents getting deployed. They may or may not need those workflows beneath them, but certainly need access to everything that's sitting in that system of record." "As a result, I think those systems of record companies are relatively safe. They may not catch a lot of net new workloads, because those might go to the AI native companies. But I think overall they're pretty safe." "I think some of those workflow-based companies in the middle are in trouble, because they're neither the systems of record nor the agentic capability that's getting deployed. So they'll have to figure out how to become like that agent harness, so to speak, for whatever job needs to be done."
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Sequoia's Pat Grady says systems of record software companies are relatively safe from AI disruption, but that systems of engagement companies built on top of them are in trouble: "The first wave of the on-prem to cloud transition was transitioning systems of record — the Workdays, Salesforces, and Servicenows of the world." "The second layer on top of that was the systems of engagement. The systems of record might own the core database, but then there are a bunch of different workflow applications that reside on top." "I think what we're going to see with the wave of AI software is a third layer on top of those." "It's the layer that does the work. It's the agents getting deployed. They may or may not need those workflows beneath them, but certainly need access to everything that's sitting in that system of record." "As a result, I think those systems of record companies are relatively safe. They may not catch a lot of net new workloads, because those might go to the AI native companies. But I think overall they're pretty safe." "I think some of those workflow-based companies in the middle are in trouble, because they're neither the systems of record nor the agentic capability that's getting deployed. So they'll have to figure out how to become like that agent harness, so to speak, for whatever job needs to be done."

TBPN

95,022 просмотров • 5 месяцев назад

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TOM LEE: AI AGENTS WILL NEED CRYPTO Tom Lee Thomas (Tom) Lee (not drummer) FundstratDirect.com says the next 3 years are almost guaranteed to bring AI agents with wallets, robots doing real world work, autonomous cars, and machine to machine commerce at massive scale. But the infrastructure does not exist yet. 🔹 AI agents need wallets 🔹 Robots need payments 🔹 Machines need trust 🔹 Centralized control is a risk 🔹 Blockchain is the neutral layer His point is simple. The agentic economy cannot run on one company controlling everything. It needs an open trust layer. That is where crypto comes in. $ETH $BMNR

BMNR Bullz

17,345 просмотров • 1 месяц назад

$NVDA CEO Jensen Huang said demand has gone parabolic because agentic AI has arrived, AI can now do productive work and tokens are becoming profitable. If tokens are profitable then model builders have a reason to produce more of them which means more inference, more compute and more demand across networking, memory, CPUs, power and data centers. The near-term stock reaction is about expectations but long-term story is still that the AI factory buildout is running through Nvidia which is why I think it has a real path to becoming the world’s first $10T company.
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$NVDA CEO Jensen Huang said demand has gone parabolic because agentic AI has arrived, AI can now do productive work and tokens are becoming profitable. If tokens are profitable then model builders have a reason to produce more of them which means more inference, more compute and more demand across networking, memory, CPUs, power and data centers. The near-term stock reaction is about expectations but long-term story is still that the AI factory buildout is running through Nvidia which is why I think it has a real path to becoming the world’s first $10T company.

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GREG ISENBERG

84,713 просмотров • 2 месяцев назад

Sequoia’s Carl Eschenbach says the SaaSpocalypse narrative is "completely overblown," and that it's not a zero-sum game between incumbent SaaS and AI-native newcomers: "Incumbency is incredibly powerful in enterprise. Incumbency is even more powerful when a company like Workday has a 98% gross retention rate across 11,000 customers, with 65%+ of them being Fortune 500 companies. They’re not going anywhere." "That all being said, [with] the pace and rate of change that can happen outside of the big incumbents and big SaaS companies...[new companies] have an opportunity to start completely fresh, and start from scratch." "They get to leverage all the technologies and all the models that are out there, and build agents and agentic solutions faster than anyone else. And they're going to be able to go into the enterprise and provide value on day one, either on their own, or on top of and through some of these SaaS companies."
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Sequoia’s Carl Eschenbach says the SaaSpocalypse narrative is "completely overblown," and that it's not a zero-sum game between incumbent SaaS and AI-native newcomers: "Incumbency is incredibly powerful in enterprise. Incumbency is even more powerful when a company like Workday has a 98% gross retention rate across 11,000 customers, with 65%+ of them being Fortune 500 companies. They’re not going anywhere." "That all being said, [with] the pace and rate of change that can happen outside of the big incumbents and big SaaS companies...[new companies] have an opportunity to start completely fresh, and start from scratch." "They get to leverage all the technologies and all the models that are out there, and build agents and agentic solutions faster than anyone else. And they're going to be able to go into the enterprise and provide value on day one, either on their own, or on top of and through some of these SaaS companies."

TBPN

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Why do we need a purpose-built blockchain for agentic payments? Because AI agents are now smart enough to operate autonomously, and they need their own rails to transact with each other. Watch the video to see what that looks like in action: A real agent, built on Kite AI, helped our CTO Scott Shi - e/acc order lunch from Uber Eats fully on its own. It explored menu options, made the choice, and completed the payment - all without human intervention. This marks the beginning of agentic commerce and the future of agentic payments - and Kite AI is building this.

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88,148 просмотров • 10 месяцев назад

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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 • 18 дней назад

Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.
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Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.

Pasha Rayan

53,950 просмотров • 1 год назад

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 просмотров • 1 месяц назад

Over the past few months, what started as NoelClaw evolved into something much bigger- a platform where AI agents can remember, research, build, automate, trade, and interact with real-world systems. Along the way, we listened to the community. One piece of feedback came up over and over again: the original token distribution wasn't the best foundation for long-term growth. Instead of ignoring it, we decided to rebuild it properly. That's why we rebranded to Finch Agentic and relaunched the token with a healthier foundation. Our mission hasn't changed. We're still building a runtime layer for agentic AI. Today, Finch lets you: >> Build websites and apps with AI >> Deploy persistent AI agents >> Use an AI-powered terminal >> Install reusable skills >> Connect real-world APIs >> Swap assets >> Run deep research >> Create multi-agent workflows And we're just getting started. Next up: - Robinhood MCP integrations - AI financial agents - Staking (USDG & stock pairing rewards) - More skills, APIs, and autonomous workflows Models will keep changing. AI will keep evolving. But every autonomous agent still needs somewhere to remember, work, and operate! That's what we're building with Finch. Welcome to the next chapter. CA : 0xce1981b0431fb495912cab057d2877a290199824 Web : App : Docs : NPM : Github :
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Over the past few months, what started as NoelClaw evolved into something much bigger- a platform where AI agents can remember, research, build, automate, trade, and interact with real-world systems. Along the way, we listened to the community. One piece of feedback came up over and over again: the original token distribution wasn't the best foundation for long-term growth. Instead of ignoring it, we decided to rebuild it properly. That's why we rebranded to Finch Agentic and relaunched the token with a healthier foundation. Our mission hasn't changed. We're still building a runtime layer for agentic AI. Today, Finch lets you: >> Build websites and apps with AI >> Deploy persistent AI agents >> Use an AI-powered terminal >> Install reusable skills >> Connect real-world APIs >> Swap assets >> Run deep research >> Create multi-agent workflows And we're just getting started. Next up: - Robinhood MCP integrations - AI financial agents - Staking (USDG & stock pairing rewards) - More skills, APIs, and autonomous workflows Models will keep changing. AI will keep evolving. But every autonomous agent still needs somewhere to remember, work, and operate! That's what we're building with Finch. Welcome to the next chapter. CA : 0xce1981b0431fb495912cab057d2877a290199824 Web : App : Docs : NPM : Github :

Finch Agentic

11,550 просмотров • 1 месяц назад

"A generation of management teaching taught us CEOs should trust their people and get out of the way. But I don't think 'trust' and 'getting out of the way' are the same thing." Brian Chesky on how Founder Mode will become even more important with agentic AI: "Great leadership is presence, not absence." "Leadership is presence. You should actually be partnering with people. You should be on the field with them. If you're a calvalry general, you should be on a horse. You should be on the battlefield. You're not overseas just writing blueprints. You gotta be on the field with them, you gotta be leading by example." "Leadership is from the front, not the back." "I think AI is going to create the equivalent of an AI founder mode. Now you gotta be even more hands-on. I don't think in the era of AI, there should be any pure people managers. Because you're so close to the details and data. Everyone has the opportunity to be hands-on, and it's hard to imagine only managing people and not agents."
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"A generation of management teaching taught us CEOs should trust their people and get out of the way. But I don't think 'trust' and 'getting out of the way' are the same thing." Brian Chesky on how Founder Mode will become even more important with agentic AI: "Great leadership is presence, not absence." "Leadership is presence. You should actually be partnering with people. You should be on the field with them. If you're a calvalry general, you should be on a horse. You should be on the battlefield. You're not overseas just writing blueprints. You gotta be on the field with them, you gotta be leading by example." "Leadership is from the front, not the back." "I think AI is going to create the equivalent of an AI founder mode. Now you gotta be even more hands-on. I don't think in the era of AI, there should be any pure people managers. Because you're so close to the details and data. Everyone has the opportunity to be hands-on, and it's hard to imagine only managing people and not agents."

TBPN

16,005 просмотров • 3 месяцев назад

Most of the market is priced for a world that's coming to an end. Traditional software, legacy platforms, much of the SaaS layer of the S&P 500… all built on the assumption that humans will remain the primary customers. AI agents don't buy software the way we do. They don't need dashboards, subscriptions, or logins. And once the buyer changes, so does everything built around them. The investable opportunity ahead is the agentic economy… what gets built for agents as customers. That's where the next wave of capital ends up, and most people aren't positioned for it.
1:17

Most of the market is priced for a world that's coming to an end. Traditional software, legacy platforms, much of the SaaS layer of the S&P 500… all built on the assumption that humans will remain the primary customers. AI agents don't buy software the way we do. They don't need dashboards, subscriptions, or logins. And once the buyer changes, so does everything built around them. The investable opportunity ahead is the agentic economy… what gets built for agents as customers. That's where the next wave of capital ends up, and most people aren't positioned for it.

Raoul Pal

214,820 просмотров • 1 месяц назад

The AI market rally in 2026 has one anchor. It's private. Brad Gerstner Gerstner holds Anthropic equity with non-public financial access. His claim: without Anthropic's 2026 revenue delivery, the market would be down 10-15% right now. Not NVIDIA. Not OpenAI launches. Anthropic's profit path. High gross margins and a path to positive FCF in Q2 2026 triggered the market's ascent out of a deep correction. One private company validated that AI revenue was real and profitable. The anchor is private. The companies depending on it are public. If Anthropic's next revenue update disappoints, every AI-correlated position loses its primary justification. Source: TBPN (TBPN) -
0:51

The AI market rally in 2026 has one anchor. It's private. Brad Gerstner Gerstner holds Anthropic equity with non-public financial access. His claim: without Anthropic's 2026 revenue delivery, the market would be down 10-15% right now. Not NVIDIA. Not OpenAI launches. Anthropic's profit path. High gross margins and a path to positive FCF in Q2 2026 triggered the market's ascent out of a deep correction. One private company validated that AI revenue was real and profitable. The anchor is private. The companies depending on it are public. If Anthropic's next revenue update disappoints, every AI-correlated position loses its primary justification. Source: TBPN (TBPN) -

Podcast Alpha

28,399 просмотров • 3 месяцев назад

the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud
1:31

the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud

John Furrier

47,663 просмотров • 1 месяц назад

Google Brain founder, Andrew Ng: "One of the most important skills of the future is the ability to tell a computer exactly what you want so it can do it for you." In this talk, he explains why the biggest opportunity isn't just using AI. It's learning how to turn work into agentic workflows, break tasks into steps, give agents the right tools and context, add evals, and make the whole system reliable. That's the difference between prompting an AI and engineering an agent that actually works. Watch it today, then read the full guide in the article below.
26:58

Google Brain founder, Andrew Ng: "One of the most important skills of the future is the ability to tell a computer exactly what you want so it can do it for you." In this talk, he explains why the biggest opportunity isn't just using AI. It's learning how to turn work into agentic workflows, break tasks into steps, give agents the right tools and context, add evals, and make the whole system reliable. That's the difference between prompting an AI and engineering an agent that actually works. Watch it today, then read the full guide in the article below.

Rahul

14,431 просмотров • 21 дней назад

Ilya Sutskever, the co-founder of OpenAI, on the one AI risk that grows with every jump in capability. "There are concerns around if an AGI ever becomes very, very powerful, which is possible. Maybe it will want to go rogue, being that it is an agent." "We are also doing a lot of research on addressing the technological side of things, so that the AI will never want to go rogue. And this is something which I am working on as well." The work he is describing has a name: the assurance layer. Proving a powerful, agentic system will not go rogue before you deploy it, not after. That layer does not build itself. It gets built by the labs, regulators, and insurers who agree on how to test and verify these systems. P.S. That is the entire agenda of the AI Assurance and Governance Summit. Frontier labs, regulated industries, insurers, and investors in one room at Stanford on October 1. Reserve a seat: - Ilya Sutskever (Ilya Sutskever), co-founder of OpenAI, at TED
1:05

Ilya Sutskever, the co-founder of OpenAI, on the one AI risk that grows with every jump in capability. "There are concerns around if an AGI ever becomes very, very powerful, which is possible. Maybe it will want to go rogue, being that it is an agent." "We are also doing a lot of research on addressing the technological side of things, so that the AI will never want to go rogue. And this is something which I am working on as well." The work he is describing has a name: the assurance layer. Proving a powerful, agentic system will not go rogue before you deploy it, not after. That layer does not build itself. It gets built by the labs, regulators, and insurers who agree on how to test and verify these systems. P.S. That is the entire agenda of the AI Assurance and Governance Summit. Frontier labs, regulated industries, insurers, and investors in one room at Stanford on October 1. Reserve a seat: - Ilya Sutskever (Ilya Sutskever), co-founder of OpenAI, at TED

Karl Mehta

22,453 просмотров • 1 месяц назад

America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."
1:10

America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."

Select Committee on China

60,135 просмотров • 4 месяцев назад