Muhammad Ali

🚨 Researchers just built an AI agent system that gets smarter every time anyone uses it. Not just smarter for you. Smarter for everyone. Simultaneously. It's called SkillClaw. And it solves the single most frustrating thing about every AI agent you've ever used. Here's the problem. Every AI agent today starts from scratch with every new user. The skills it uses — the workflows, the tool calling patterns, the ways of handling edge cases — are static. They were written once, by developers, and they never change. User A discovers a clever way to handle a complex data extraction task. That knowledge lives in their session and nowhere else. User B hits the same problem tomorrow and the agent fails the same way it failed yesterday. Meanwhile User C figured out the exact fix two weeks ago. Nobody knows. The agent doesn't know. Every user rediscovers the same failures independently and the system never learns. SkillClaw fixes this at the system level. Here's how it works. Every interaction any user has with the agent generates a trajectory — a record of what was tried, what worked, what failed, and how it was resolved. SkillClaw continuously aggregates these trajectories across all users and feeds them to an autonomous evolver. The evolver isn't just logging usage. It's analyzing it. It identifies recurring behavioral patterns — things that multiple users keep doing that work, edge cases that keep failing the same way, tool usage patterns that consistently produce better results. Then it translates those patterns into actual skill updates. The updated skills go into a shared repository and synchronize across all users automatically. What one user discovers, everyone gets. Without anyone asking. Without any extra effort. Without any user even knowing the system updated. Here's the wildest part. The improvements compound. As more users interact with the agent, the skill set gets continuously refined. Early users help the agent handle common cases better. Power users push the edge cases. Failed attempts teach the evolver what not to do. Every interaction from every user flows back into making the agent better for everyone. It's the first AI agent system designed to improve like a product — through collective use — rather than staying frozen like software. Here's why this matters right now. Every AI coding agent, every AI work assistant, every agent platform deployed to thousands of users right now is wasting the most valuable signal it has: what actually works in practice, across the full range of real usage, at scale. SkillClaw captures that signal and puts it back into the system automatically. The agent your team uses on Friday is better than the one you used on Monday — because everyone who used it between Monday and Friday made it better without trying to. That is not how any current agent system works. And it's exactly how they should work. #1 paper on Hugging Face today. Code available on GitHub.

Props to the Mutiny team for re-building from scratch. So many companies’ value props and biz models are getting crushed by AI. Most take small steps or try retrofitting AI into a crumbling foundation to avoid near-term revenue hit. None of those have worked.

Why the Best AI Architecture Is Losing to the Best Alibi In 2026, the most important metric in enterprise AI isn't Tokens Per Second. It's CRM — Career Risk Management. That's why the wrong architecture is winning. The industry has converged on the AI Factory: a centralized, training-heavy monolith wrapped in a Gartner TRiSM governance layer. It's politically safe. It's Board-presentable. And for most enterprises, it isn't working. The reason isn't technical. It's incentive structure. If you build a Gartner-approved AI Factory and it fails, you don't get fired. The failure gets attributed to "market maturity." You followed the industry standard. The Magic Quadrant is your alibi. If you build a distributed Reasoning Plane — sovereign inference, logic at the edge, authority placed at Layer 2C — you own the outcome. If it fails, you're the architect who over-engineered it. There's no analyst report to hide behind. So practitioners make the rational career move. They build the Factory. The result is what I'm calling the Headless Factory: technically impressive infrastructure with no reasoning plane to guide it. Massive clusters. Serious power draw. Agents with nowhere coherent to think. The architectural problem isn't compute. It's that enterprises are trying to haul 20 years of distributed, sovereign data into a central refinery — and govern the mess with a wrapper bolted on after the fact. TRiSM is a governance framework. It was never an architecture. Conflating the two is where the project fails. The practitioners who are actually getting agents to work aren't choosing between the Factory and the Reasoning Plane. They're doing both — deliberately. Buy the Factory to satisfy the board's need for a visible big bet. Build the Layer 2C mesh to make the agents actually function. The Factory is the alibi. The Reasoning Plane is the result. The question worth asking your team: are we building for ROI, or for I-told-you-so? #EnterpriseAI #CTOAdvisor #AIFactory #Layer2C #DAPM #InfrastructureArchitecture #AIStrategy

Most enterprise AI doesn’t fail at the model. It fails because no one defined what “good” looks like. We saw this with an eCommerce AI assistant that: - missed conversions - called the wrong tools - added friction So we started there. Defined the metrics → built the evaluation system → created the data pipeline → fine-tuned the model. Our engineers embedded with the team to take it into production. Read more: aion.xyz/case-studies/ecomme…

Why the Best AI Architecture Is Losing to the Best Alibi In 2026, the most important metric in enterprise AI isn't Tokens Per Second. It's CRM — Career Risk Management. That's why the wrong architecture is winning. The industry has converged on the AI Factory: a centralized, training-heavy monolith wrapped in a Gartner TRiSM governance layer. It's politically safe. It's Board-presentable. And for most enterprises, it isn't working. The reason isn't technical. It's incentive structure. If you build a Gartner-approved AI Factory and it fails, you don't get fired. The failure gets attributed to "market maturity." You followed the industry standard. The Magic Quadrant is your alibi. If you build a distributed Reasoning Plane — sovereign inference, logic at the edge, authority placed at Layer 2C — you own the outcome. If it fails, you're the architect who over-engineered it. There's no analyst report to hide behind. So practitioners make the rational career move. They build the Factory. The result is what I'm calling the Headless Factory: technically impressive infrastructure with no reasoning plane to guide it. Massive clusters. Serious power draw. Agents with nowhere coherent to think. The architectural problem isn't compute. It's that enterprises are trying to haul 20 years of distributed, sovereign data into a central refinery — and govern the mess with a wrapper bolted on after the fact. TRiSM is a governance framework. It was never an architecture. Conflating the two is where the project fails. The practitioners who are actually getting agents to work aren't choosing between the Factory and the Reasoning Plane. They're doing both — deliberately. Buy the Factory to satisfy the board's need for a visible big bet. Build the Layer 2C mesh to make the agents actually function. The Factory is the alibi. The Reasoning Plane is the result. The question worth asking your team: are we building for ROI, or for I-told-you-so? #EnterpriseAI #CTOAdvisor #AIFactory #Layer2C #DAPM #InfrastructureArchitecture #AIStrategy

Props to the Mutiny team for re-building from scratch. So many companies’ value props and biz models are getting crushed by AI. Most take small steps or try retrofitting AI into a crumbling foundation to avoid near-term revenue hit. None of those have worked.

🚨 Researchers just built an AI agent system that gets smarter every time anyone uses it. Not just smarter for you. Smarter for everyone. Simultaneously. It's called SkillClaw. And it solves the single most frustrating thing about every AI agent you've ever used. Here's the problem. Every AI agent today starts from scratch with every new user. The skills it uses — the workflows, the tool calling patterns, the ways of handling edge cases — are static. They were written once, by developers, and they never change. User A discovers a clever way to handle a complex data extraction task. That knowledge lives in their session and nowhere else. User B hits the same problem tomorrow and the agent fails the same way it failed yesterday. Meanwhile User C figured out the exact fix two weeks ago. Nobody knows. The agent doesn't know. Every user rediscovers the same failures independently and the system never learns. SkillClaw fixes this at the system level. Here's how it works. Every interaction any user has with the agent generates a trajectory — a record of what was tried, what worked, what failed, and how it was resolved. SkillClaw continuously aggregates these trajectories across all users and feeds them to an autonomous evolver. The evolver isn't just logging usage. It's analyzing it. It identifies recurring behavioral patterns — things that multiple users keep doing that work, edge cases that keep failing the same way, tool usage patterns that consistently produce better results. Then it translates those patterns into actual skill updates. The updated skills go into a shared repository and synchronize across all users automatically. What one user discovers, everyone gets. Without anyone asking. Without any extra effort. Without any user even knowing the system updated. Here's the wildest part. The improvements compound. As more users interact with the agent, the skill set gets continuously refined. Early users help the agent handle common cases better. Power users push the edge cases. Failed attempts teach the evolver what not to do. Every interaction from every user flows back into making the agent better for everyone. It's the first AI agent system designed to improve like a product — through collective use — rather than staying frozen like software. Here's why this matters right now. Every AI coding agent, every AI work assistant, every agent platform deployed to thousands of users right now is wasting the most valuable signal it has: what actually works in practice, across the full range of real usage, at scale. SkillClaw captures that signal and puts it back into the system automatically. The agent your team uses on Friday is better than the one you used on Monday — because everyone who used it between Monday and Friday made it better without trying to. That is not how any current agent system works. And it's exactly how they should work. #1 paper on Hugging Face today. Code available on GitHub.

Net dollar retention has settled into 110-120% across most B2B companies — stabilizing after years of decline from pandemic highs. Gross retention is holding at 94-97%. The churn isn't the problem. Expansion is. Companies that own the AI agent layer on top of their platform are seeing NRR above 130%. Everyone else is watching expansion slow and still calling it "macro" or a "downturn."

Sometimes when I talk to founders about their startup, they work very hard to sound like their idea has no flaws Obviously it does and they just end up sounding disingenuous You should be okay with making the bear case for your own company Of course you can follow that up with what you are doing about it But every company has flaws, even the big guys Just look at Google, their models still aren’t great but they have more GPU capacity than the rest combined Apple is still figuring out the I in AI but they own the consumer relationship and are going to aggregate the model labs Microsoft bet hard on OpenAI only to realize that Claude is the real enterprise AI, but they still own the enterprise relationship and they are pivoting Meta fumbled their open source and Llama lead but now their internal AI models are making a dent on their ad revenue

Most enterprise AI doesn’t fail at the model. It fails because no one defined what “good” looks like. We saw this with an eCommerce AI assistant that: - missed conversions - called the wrong tools - added friction So we started there. Defined the metrics → built the evaluation system → created the data pipeline → fine-tuned the model. Our engineers embedded with the team to take it into production. Read more: aion.xyz/case-studies/ecomme…

Most agencies chasing "AI-native" positioning are optimizing the wrong thing. They're updating their decks. Refreshing their website copy. Practicing how to answer "so how do you use AI?" on discovery calls. And their P&L looks identical to 2021. Here's the contrarian take: an AI-native agency doesn't sound different. It prices differently. The real signal isn't vocabulary. It's margin structure. Traditional agency: more clients means more headcount. Capacity is linear. Margin compresses as you grow because delivery cost scales with revenue. AI-native agency: more clients doesn't automatically mean more headcount. Delivery cost grows slower than revenue. Margin expands at volume instead of shrinking. That's the structural difference. Not the pitch. You can't hear it on a sales call. You can't see it in a proposal. You see it when you look at what it actually costs to deliver the work versus what you're billing. The agencies winning right now aren't the ones who figured out how to talk about AI. They're the ones who rebuilt delivery so that a second client doesn't cost as much as the first. If your gross margin looks the same as it did before you adopted any AI tools, you haven't gone AI-native. You've gone AI-adjacent. That's a positioning claim with no operational backing. What does your margin structure actually look like at two clients versus ten?

Sometimes when I talk to founders about their startup, they work very hard to sound like their idea has no flaws Obviously it does and they just end up sounding disingenuous You should be okay with making the bear case for your own company Of course you can follow that up with what you are doing about it But every company has flaws, even the big guys Just look at Google, their models still aren’t great but they have more GPU capacity than the rest combined Apple is still figuring out the I in AI but they own the consumer relationship and are going to aggregate the model labs Microsoft bet hard on OpenAI only to realize that Claude is the real enterprise AI, but they still own the enterprise relationship and they are pivoting Meta fumbled their open source and Llama lead but now their internal AI models are making a dent on their ad revenue

VCs in 2026: "Show me native AI agents or I'm passing." SaaS without agents went from competitive disadvantage to unfundable in one quarter. The threshold shifted from product-market fit to agent-market fit and most founders haven't noticed yet.

Self-serve AI products have 90% churn and everyone acts surprised. The companies are too different from each other. We've been deploying at companies for months. Same ERP, same industry, completely different data structures. Nobody writes anything down

The next wave of AI SaaS failures won't be about bad code. It'll be about bad experience. Founders will vibe code their product in a weekend. Ship it. Get signups. Then watch everyone leave. Because users don't churn from missing features. They churn from confusion. From friction. From this doesn't feel right. AI made it easy to build. It made users harder to impress. If you face same problem DM me, Let's work.

the startup nobody sees coming in 2026 gen Z vibe coder: → ships in a weekend → finds product market fit by instinct → grows to 1000 users before writing proper code senior engineer: → refactors everything → fixes what's about to collapse → builds what actually scales neither can win alone but together they're the most dangerous team alive

obsidian: 3 engineers, 3.4m+ users. 3 people built the most popular note-taking app fully remote, no vc pressure growing to 4 engineers only now the lesson: / small teams move faster / no meetings = more shipping every startup hiring 20 engineers before product-market fit should see this.

the SAAS MELTDOWN is real 📉💀 ▫️ Amplitude: -93% ▫️ Unity: -89% ▫️ UiPath: -88% ▫️ Atlassian: -87% ▫️ monday: -86% ▫️ GitLab: -85% ▫️ HubSpot: -75% ▫️ Adobe: -66% ▫️ Workday: -62% ▫️ Salesforce: -54% These are companies that defined the last decade of enterprise software. All down 50-90% from their peaks AI commoditized the feature layer. The moat most of these companies thought they had was the UI on top of a workflow. That moat is gone Which of these do you think recovers? And which ones are structurally finished?

2/6 The 5 fatal mistakes that were silently destroying his business: 1. No rate limiting anywhere → AI agents were getting abused. Costs exploded the moment traffic grew. 2. No Stripe webhooks for payment confirmation → Users could cancel mid-flow and still get access. Revenue leakage everywhere. 3. Vercel for everything → Great for prototypes. At real scale? Bandwidth + function duration bills went nuclear. Unpredictable costs. 4. Supabase as the only DB → Easy start, but storage + compute + egress fees skyrocketed as users + data grew. Margins got crushed. 5. Clerk for auth → Per-active-user pricing looked cheap at 100 users. At 1,000+ it became a silent profit killer. He was renting his entire infrastructure. Zero ownership. One price hike or outage and his product was dead.