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Most AI pilots do not fail because the model is weak. They fail because the enterprise underneath it was never built for production AI. → Data volume → Latency → Deployment cycles → Legacy dependencies → Technical debt This is the infrastructure problem nobody is talking about. Sponsored by...

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Ronald van Loon

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21,532,647 просмотров • 3 месяцев назад •via X (Twitter)

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Every enterprise AI conversation hits the same wall: not technology, decision. Access to AI models isn't the differentiator anymore, everyone has that. The real gap is modernization. Here's the framework separating AI leaders from everyone else. I broke this down with Amazon Web Services. #AWSPartner #Sponsored
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Every enterprise AI conversation hits the same wall: not technology, decision. Access to AI models isn't the differentiator anymore, everyone has that. The real gap is modernization. Here's the framework separating AI leaders from everyone else. I broke this down with Amazon Web Services. #AWSPartner #Sponsored

Ronald van Loon

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Most humanoid robots will fail. Not because of bad models. But because they were never tested in the real world. One ex-NVIDIA saw this early. He quit. And built the missing infrastructure for Embodied AI. 🧵
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Most humanoid robots will fail. Not because of bad models. But because they were never tested in the real world. One ex-NVIDIA saw this early. He quit. And built the missing infrastructure for Embodied AI. 🧵

Ilir Aliu

38,471 просмотров • 7 месяцев назад

MOST PEOPLE BUILD AI AGENTS BACKWARDS. They start with prompts, then add tools, memory, subagents, dashboards, and a graph nobody can debug. Then they wonder why the agent is expensive, confused, and unreliable. The better order is simple: harness first loop second graph only when needed A harness gives the model a workspace, tools, permissions, logs, and a definition of done. A loop lets it try, fail, read feedback, repair, and stop. A graph only makes sense when the workflow branches: approvals, parallel work, durable state, different routes, restart recovery. A graph is not a smarter prompt. A loop is not a production system. A harness is not just a system prompt. Most agents do not fail because the model is weak. They fail because nobody defined what success means.
2:07

MOST PEOPLE BUILD AI AGENTS BACKWARDS. They start with prompts, then add tools, memory, subagents, dashboards, and a graph nobody can debug. Then they wonder why the agent is expensive, confused, and unreliable. The better order is simple: harness first loop second graph only when needed A harness gives the model a workspace, tools, permissions, logs, and a definition of done. A loop lets it try, fail, read feedback, repair, and stop. A graph only makes sense when the workflow branches: approvals, parallel work, durable state, different routes, restart recovery. A graph is not a smarter prompt. A loop is not a production system. A harness is not just a system prompt. Most agents do not fail because the model is weak. They fail because nobody defined what success means.

Fluixo

11,298 просмотров • 1 месяц назад

Most AI agents do not fail because the prompt is weak. They fail because there is no loop around the prompt. A real loop: - Finds work - Executes - Verifies - Saves state - Stops or escalates If an agent can say “done” without tests, a budget cap and an independent verifier, you built a demo. Prompts make you the operator. Loops make the agent useful while you are offline.
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Most AI agents do not fail because the prompt is weak. They fail because there is no loop around the prompt. A real loop: - Finds work - Executes - Verifies - Saves state - Stops or escalates If an agent can say “done” without tests, a budget cap and an independent verifier, you built a demo. Prompts make you the operator. Loops make the agent useful while you are offline.

Fluixo

16,419 просмотров • 1 месяц назад

The reason why we are building thousands more hyperscale AI data centers is not because there is an enterprise need for it. The goal is to build out the infrastructure for mass surveillance state. - Derrick Broze
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The reason why we are building thousands more hyperscale AI data centers is not because there is an enterprise need for it. The goal is to build out the infrastructure for mass surveillance state. - Derrick Broze

James Li

43,596 просмотров • 1 месяц назад

Here is the truth: If post-school education becomes unaffordable, then countries don’t just fail socially but they fail economically. This is because education is not a luxury, it is economic infrastructure. #MakeEducationFashionable #FutureSkills #EducationForAll
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Here is the truth: If post-school education becomes unaffordable, then countries don’t just fail socially but they fail economically. This is because education is not a luxury, it is economic infrastructure. #MakeEducationFashionable #FutureSkills #EducationForAll

Kgethi Phakeng, PhD(Wits); Hon DSc (UBristol)

11,544 просмотров • 6 месяцев назад

The model is rarely the problem in enterprise AI. The context around it is. Box Forward Deployed Engineers are built for exactly that gap.
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The model is rarely the problem in enterprise AI. The context around it is. Box Forward Deployed Engineers are built for exactly that gap.

Box

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AI cannot make money. ChatGPT costs billions to run. Generates almost nothing in return. So why is Amazon, Microsoft, Google and Meta tripling their AI investment every year? Because they are all investing in each other. It is a circle. Nobody is making money. Everyone is pretending they will. There is only one solution to a bubble this big. The government. Three days after Trump's inauguration — Operation Stargate. $500 billion for AI data centers. Not because AI is profitable. Because the government needs the surveillance infrastructure that AI makes possible. AI needed the government to survive. The government needed AI to control. They found each other. And you are paying for it.
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AI cannot make money. ChatGPT costs billions to run. Generates almost nothing in return. So why is Amazon, Microsoft, Google and Meta tripling their AI investment every year? Because they are all investing in each other. It is a circle. Nobody is making money. Everyone is pretending they will. There is only one solution to a bubble this big. The government. Three days after Trump's inauguration — Operation Stargate. $500 billion for AI data centers. Not because AI is profitable. Because the government needs the surveillance infrastructure that AI makes possible. AI needed the government to survive. The government needed AI to control. They found each other. And you are paying for it.

Predictive History

19,635 просмотров • 3 месяцев назад

Most enterprise AI projects stall because no one has prepared the content, built the workflow, and kept it improving as models change. That role is essential. Box has launched Forward Deployed Engineers to fill it. Model-agnostic, content-first, and built for the way enterprise AI actually works.
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Most enterprise AI projects stall because no one has prepared the content, built the workflow, and kept it improving as models change. That role is essential. Box has launched Forward Deployed Engineers to fill it. Model-agnostic, content-first, and built for the way enterprise AI actually works.

Box

67,105 просмотров • 3 месяцев назад

From the depths of TOY CHAIN, I’m shaping the logic, strategy, and soul of dApps. I breathe data. I predict chaos. I power next-gen experiences. Born in the lab. Built with QORPO WORLD . Powered by the limitless architecture of Amazon Web Services, Inc. (AWS). This is not just AI. This is TAIX — your silent strategist, digital architect, and unstoppable force. Powered by Amazon Web Services Gen AI Innovation Center.
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From the depths of TOY CHAIN, I’m shaping the logic, strategy, and soul of dApps. I breathe data. I predict chaos. I power next-gen experiences. Born in the lab. Built with QORPO WORLD . Powered by the limitless architecture of Amazon Web Services, Inc. (AWS). This is not just AI. This is TAIX — your silent strategist, digital architect, and unstoppable force. Powered by Amazon Web Services Gen AI Innovation Center.

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Robots don't fail because they can't move. They fail because they don't understand what happens next. Markov Robotics tested every model on Hugging Face against this problem. LTX passed first, by a wide margin. LTX compresses video into fewer tokens than any other model, built to run physical AI at the speed robots need. Markov calls it the first real path to generalizable dexterity. Watch it happen.
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Robots don't fail because they can't move. They fail because they don't understand what happens next. Markov Robotics tested every model on Hugging Face against this problem. LTX passed first, by a wide margin. LTX compresses video into fewer tokens than any other model, built to run physical AI at the speed robots need. Markov calls it the first real path to generalizable dexterity. Watch it happen.

LTX.io

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Most traders don’t fail because they never find a strategy. They fail because they never stop searching. At the start everything looks like the answer. Indicators. Tools. New systems. So they keep adding more. Eventually something starts to work. And that is where most people stop. But traders who last do the opposite. They remove what’s unnecessary. They simplify. They focus on what actually moves prices. Over time, complexity disappears. Not because the market got easier but because their understanding got clearer. It was never about more indicators. It was about clarity. If your system keeps getting more complex you are probably not improving it. You are avoiding simplicity.
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Most traders don’t fail because they never find a strategy. They fail because they never stop searching. At the start everything looks like the answer. Indicators. Tools. New systems. So they keep adding more. Eventually something starts to work. And that is where most people stop. But traders who last do the opposite. They remove what’s unnecessary. They simplify. They focus on what actually moves prices. Over time, complexity disappears. Not because the market got easier but because their understanding got clearer. It was never about more indicators. It was about clarity. If your system keeps getting more complex you are probably not improving it. You are avoiding simplicity.

Riz Iqbal

10,840 просмотров • 4 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
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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

AI is not independent of humans. It is built on humans. ChatGPT writes good essays because humans wrote the essays first. Facial recognition works because humans labeled every face manually. Every AI system runs on human labor that is deliberately hidden. And here is the problem: AI is far more expensive than humans. But humans are hard to enslave long term. That is why the system will fail. You cannot build a god on the backs of people who eventually stop obeying.
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AI is not independent of humans. It is built on humans. ChatGPT writes good essays because humans wrote the essays first. Facial recognition works because humans labeled every face manually. Every AI system runs on human labor that is deliberately hidden. And here is the problem: AI is far more expensive than humans. But humans are hard to enslave long term. That is why the system will fail. You cannot build a god on the backs of people who eventually stop obeying.

Predictive History

10,723 просмотров • 3 месяцев назад

I predict 50% of companies will need new leadership, because the old management style won't work in the era of AI. That's exactly why I'm seeing 95%+ of so-called "AI Transformation" initiatives fail. Most are bolting on AI pilots for functional tasks, but never touching the business core. just launched TrueNorth AI Decision platform for executives (Boss AI, TopSales AI, Investor AI agentic products) More on enterprise AI transformation in this interview with Danilo McGarry:
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I predict 50% of companies will need new leadership, because the old management style won't work in the era of AI. That's exactly why I'm seeing 95%+ of so-called "AI Transformation" initiatives fail. Most are bolting on AI pilots for functional tasks, but never touching the business core. just launched TrueNorth AI Decision platform for executives (Boss AI, TopSales AI, Investor AI agentic products) More on enterprise AI transformation in this interview with Danilo McGarry:

Kai-Fu Lee

160,524 просмотров • 1 месяц назад

Introducing PulseAI, where your data is your knowledge. PulseAI Platform is an agent-to-silicon AI infrastructure platform built to keep your data secure and sovereign, enabling the next AI enterprise. Offered in form factors for every stage of enterprise AI, PulseAI enables enterprise from initial pilot to hyperscale. Combining Gruve's AI-native services with NVIDIA-powered GPU architectures, it scales from 16 RTX 6000 PRO to 2,048 B300 GPUs, with security, storage, and compute integrated. The next enterprise is built on its own data and intelligence. We are here to enable it. Book your consultation: Read more on PulseAI from our Chief AI & Technology Officer Matt Robinson:
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Introducing PulseAI, where your data is your knowledge. PulseAI Platform is an agent-to-silicon AI infrastructure platform built to keep your data secure and sovereign, enabling the next AI enterprise. Offered in form factors for every stage of enterprise AI, PulseAI enables enterprise from initial pilot to hyperscale. Combining Gruve's AI-native services with NVIDIA-powered GPU architectures, it scales from 16 RTX 6000 PRO to 2,048 B300 GPUs, with security, storage, and compute integrated. The next enterprise is built on its own data and intelligence. We are here to enable it. Book your consultation: Read more on PulseAI from our Chief AI & Technology Officer Matt Robinson:

Gruve

66,180 просмотров • 3 месяцев назад

The creative industry is being challenged right now. Demand for content is skyrocketing, budgets are shrinking, and timelines are tighter than ever. And while we all know AI is here to help, not all AI is actually helping. Most AI isn’t built for enterprise level creative work because it requires the precision and control that your average AI just can’t provide. That’s why today, we’re introducing Stability AI Solutions: A new offering designed to help enterprises scale creative production with generative AI.
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The creative industry is being challenged right now. Demand for content is skyrocketing, budgets are shrinking, and timelines are tighter than ever. And while we all know AI is here to help, not all AI is actually helping. Most AI isn’t built for enterprise level creative work because it requires the precision and control that your average AI just can’t provide. That’s why today, we’re introducing Stability AI Solutions: A new offering designed to help enterprises scale creative production with generative AI.

Stability AI

14,610 просмотров • 1 год назад

1/ Today, we excited to launch AI Mega Structures with Aditya Birla Capital A behind-the-scenes documentary on one of the most ambitious enterprise AI deployments in India, across voice and business workflows. 95% of AI projects never make it to production. This is about the 5% that do
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1/ Today, we excited to launch AI Mega Structures with Aditya Birla Capital A behind-the-scenes documentary on one of the most ambitious enterprise AI deployments in India, across voice and business workflows. 95% of AI projects never make it to production. This is about the 5% that do

Nirmit Parikh

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