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Everyone is saying “it’s the harness, not the model.” But...no one is really explaining what that means. I wanted to understand harness engineering better, so on today's episode of How I AI, I built my own: a custom harness using ClaudeDevs SDK to triage Sentry bugs, verify root cause,... show more

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This guy explained agent harness, memory, and loop engineering in one video - clearer than most paid courses a raw LLM with no harness is a wild horse. all power, no direction > trace the run -> score it with an LLM -> diagnose the bottleneck -> patch it -> ship it that loop is what lets an agent get sharper on its own, run after run memory + harness + loop engineering + evals - that's the whole stack in one picture watch it, then grab the diagram below

This guy explained agent harness, memory, and loop engineering in one video - clearer than most paid courses a raw LLM with no harness is a wild horse. all power, no direction > trace the run -> score it with an LLM -> diagnose the bottleneck -> patch it -> ship it that loop is what lets an agent get sharper on its own, run after run memory + harness + loop engineering + evals - that's the whole stack in one picture watch it, then grab the diagram below

Mr. Buzzoni

44,605 Aufrufe • vor 9 Tagen

OpenAI engineer: "Nobody on my team has touched code in 9 months. Agents write all the code, the way you do it is harness engineering." In 45 minutes he breaks down exactly how to build the setup so agents do the full job without you in the loop. This beats any paid course on building agents I've seen. Watch it, then read the full guide on harness engineering below.

OpenAI engineer: "Nobody on my team has touched code in 9 months. Agents write all the code, the way you do it is harness engineering." In 45 minutes he breaks down exactly how to build the setup so agents do the full job without you in the loop. This beats any paid course on building agents I've seen. Watch it, then read the full guide on harness engineering below.

Anatoli Kopadze

24,907 Aufrufe • vor 10 Tagen

Anthropic Technical Staff Jess Yan says the harness and the model cannot be pulled apart without giving up performance, though she flags her own bias. "I think I am quite biased but I also think that it is impossible to get the maximum possible performance without tying together the harness and the model." "Now the components of the harness and maybe the thickness of the harness will change over time as models get more and more capable." "However, when we test our models and when we are assessing their performance we always have to test it in conjunction with a harness." "And are we going to test it with all the different harnesses of the world? We're going to select the harnesses that we have built." "And so there is an aspect of the necessity of building models is that you have to be testing them with harnesses, and that sort of keeps them paired together."

Anthropic Technical Staff Jess Yan says the harness and the model cannot be pulled apart without giving up performance, though she flags her own bias. "I think I am quite biased but I also think that it is impossible to get the maximum possible performance without tying together the harness and the model." "Now the components of the harness and maybe the thickness of the harness will change over time as models get more and more capable." "However, when we test our models and when we are assessing their performance we always have to test it in conjunction with a harness." "And are we going to test it with all the different harnesses of the world? We're going to select the harnesses that we have built." "And so there is an aspect of the necessity of building models is that you have to be testing them with harnesses, and that sort of keeps them paired together."

Fireside Alpha

580,541 Aufrufe • vor 11 Tagen

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

Vikram M

21,463 Aufrufe • vor 1 Monat

We just crossed $10M in ARR at Chatbase! 🎉 🎉 And today, we're launching Chatbase as the full harness for customer-facing AI agents. Similar to how Claude code is a harness for coding agents, Chatbase is the harness for customer experience agents. That means we give the model the context, tools, workflows, guardrails, and human-in-the-loop systems to be the best ambassador for your brand. It's going beyond just solving issues and is giving your customers the best experiences across every channel. This is a milestone I have been thinking about and obsessed with since day 1, and I am super excited to bring my vision for customer facing agents to life with Chatbase. Thank you to every one of our customers and to the amazing Chatbase team for getting us here! Next stop: $100M ARR

Yasser

820,351 Aufrufe • vor 2 Monaten

I rebuilt Posia using the Pi Agent SDK. The core agent is ~15 lines of code, and the harness is so flexible and hackable. Recorded a 17-min walkthrough on: - Pi’s extension system + how to build your own - extensions that cut tool tokens by 80–96% - the core Pi SDK, step by step - building hosted agents with it Adding my Pi extensions setup in AI Builder Club repo here:

I rebuilt Posia using the Pi Agent SDK. The core agent is ~15 lines of code, and the harness is so flexible and hackable. Recorded a 17-min walkthrough on: - Pi’s extension system + how to build your own - extensions that cut tool tokens by 80–96% - the core Pi SDK, step by step - building hosted agents with it Adding my Pi extensions setup in AI Builder Club repo here:

Jason Zhou

66,010 Aufrufe • vor 1 Monat

🎉Introducing PyFlue: The Python-Native Agent Harness Framework.🧰 💡Flue for Python: Fred K. Schott fks CEO of HTML has launched Flue: The Agent Harness Framework for TypeScript. It brings programmable harness right into your agents rather than DIY plumbing. Python ecosystem already has 🦾 powerful AI/ML tools and frameworks and research initiatives but most frameworks asked users to build your own harness. Superagentic AI bringing this concept of Flue to Python 🐍 ecosystem. Here is PyFlue even even better 🤖 Agent = Model 💻 + Harness 🧰 + Memory 🧠 Almost all the feature of Flue plugged with LangChain Deepagents harness built by Harrison Chase and team Viv Sydney Runkle and more coming soon. 👉 Stop building agent loops, start using a harness. 💻 Try PyFlue Now : ⭐️ GitHub: 📚Docs: 📙 Blog Post: #HarnessEngineering #AgentHarnesses

🎉Introducing PyFlue: The Python-Native Agent Harness Framework.🧰 💡Flue for Python: Fred K. Schott fks CEO of HTML has launched Flue: The Agent Harness Framework for TypeScript. It brings programmable harness right into your agents rather than DIY plumbing. Python ecosystem already has 🦾 powerful AI/ML tools and frameworks and research initiatives but most frameworks asked users to build your own harness. Superagentic AI bringing this concept of Flue to Python 🐍 ecosystem. Here is PyFlue even even better 🤖 Agent = Model 💻 + Harness 🧰 + Memory 🧠 Almost all the feature of Flue plugged with LangChain Deepagents harness built by Harrison Chase and team Viv Sydney Runkle and more coming soon. 👉 Stop building agent loops, start using a harness. 💻 Try PyFlue Now : ⭐️ GitHub: 📚Docs: 📙 Blog Post: #HarnessEngineering #AgentHarnesses

Shashi

69,884 Aufrufe • vor 3 Monaten

this is huge news from Cursor, they've pulled of the impossible and turned their ai-wrapper into a in-destructible moat sam altman literally called it cursor's ai agent harness is available for anyone to build on, which means ai models are now a commodity > 1-time install and now anyone can run cursor's agent locally or via cloud. use any model (e.g. gpt 5.5) but with the added cursor harness that makes it 10X better. > its so good that 3 of cursors biggest competitors are embedding it into their products. > now cursor DOESN'T DEPEND on anthropic or openai. their own model (composer 2) competes directly! yesterday sam altman said the ai model and harness are one and the same and today cursor turned their harness into a self-owned moat fucking masterclass (coming from a former cursor / ai wrapper hater)

this is huge news from Cursor, they've pulled of the impossible and turned their ai-wrapper into a in-destructible moat sam altman literally called it cursor's ai agent harness is available for anyone to build on, which means ai models are now a commodity > 1-time install and now anyone can run cursor's agent locally or via cloud. use any model (e.g. gpt 5.5) but with the added cursor harness that makes it 10X better. > its so good that 3 of cursors biggest competitors are embedding it into their products. > now cursor DOESN'T DEPEND on anthropic or openai. their own model (composer 2) competes directly! yesterday sam altman said the ai model and harness are one and the same and today cursor turned their harness into a self-owned moat fucking masterclass (coming from a former cursor / ai wrapper hater)

Ejaaz

950,091 Aufrufe • vor 3 Monaten

She claims that her neighbors are hard-core Trump supporters. "So one of my neighbors is walking her dog and I said hello." "I happened to see that the dog had a harness that said Trump For America." Do you believe anything she is saying in this video?

She claims that her neighbors are hard-core Trump supporters. "So one of my neighbors is walking her dog and I said hello." "I happened to see that the dog had a harness that said Trump For America." Do you believe anything she is saying in this video?

Brandon

32,519 Aufrufe • vor 3 Monaten

Agent apps don’t win on the model, they win on the harness. But creating that harness is painful. Terminal Use is hosting infrastructure for background agents built for iterating: fork filesystems and try multiple agents in parallel. The platform is CLI-first, which allows your coding agents like Claude Code & Codex to help you debug, hypothesize improvements, and analyze outputs. Congrats on the launch Vivek Raja, Stavros Filosidis, and Filip Balucha!

Agent apps don’t win on the model, they win on the harness. But creating that harness is painful. Terminal Use is hosting infrastructure for background agents built for iterating: fork filesystems and try multiple agents in parallel. The platform is CLI-first, which allows your coding agents like Claude Code & Codex to help you debug, hypothesize improvements, and analyze outputs. Congrats on the launch Vivek Raja, Stavros Filosidis, and Filip Balucha!

Y Combinator

61,540 Aufrufe • vor 5 Monaten

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.

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

11,279 Aufrufe • vor 14 Tagen

I have been annoying a lot of people about how cool I think the Siri AI harness and architecture is recently, so while I work on the proper video/report, I made this quick messy screen recording explaining it! Hopefully it's of interest to some!

I have been annoying a lot of people about how cool I think the Siri AI harness and architecture is recently, so while I work on the proper video/report, I made this quick messy screen recording explaining it! Hopefully it's of interest to some!

Max Weinbach

74,716 Aufrufe • vor 2 Monaten

📚 Explore the Harness Guide: 👺 Want to feel your arms snugly tied behind your back and your chest exposed? The Tengu is a must-try! Easily adapted to your own flexibility levels, this upper body harness is suitable for most body types. It opens up the front of the body while still feeling stable and sustainable. Get ready to explore the endless possibilities for play. 🔗 You can now learn how to tie the Tengu Harness step-by-step with our new Harness Guide! Find it via the link above.

📚 Explore the Harness Guide: 👺 Want to feel your arms snugly tied behind your back and your chest exposed? The Tengu is a must-try! Easily adapted to your own flexibility levels, this upper body harness is suitable for most body types. It opens up the front of the body while still feeling stable and sustainable. Get ready to explore the endless possibilities for play. 🔗 You can now learn how to tie the Tengu Harness step-by-step with our new Harness Guide! Find it via the link above.

Shibari Study

36,511 Aufrufe • vor 3 Monaten

In case you didn’t notice I am currently OBSESSED with harnesses 🔥 And the Marina Harness by Marie colère is 😍 A leather harness just adds an extra bit of spice to a look that other accessories just can’t quite live up to. Have you added one to your wardrobe yet

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In case you didn’t notice I am currently OBSESSED with harnesses 🔥 And the Marina Harness by Marie colère is 😍 A leather harness just adds an extra bit of spice to a look that other accessories just can’t quite live up to. Have you added one to your wardrobe yet

Saterra St Jean

22,542 Aufrufe • vor 1 Jahr

Claude Code is the OpenClaw alternative you already had. I've been telling people this in person for three months. Now it's a piece on Every 📧. When OpenClaw was at peak hype a few months ago, everyone was going crazy over it — for good reasons. But almost nobody made the obvious comparison: Claude Code already does the thing you love about OpenClaw. In a lot of cases, better. Here's why people missed it. You have to understand the difference between a model and a harness. OpenClaw, at the end of the day, is just a harness on top of AI models. And Claude Code is too — it just got marketed as a coding tool, an alternative to Cursor. Until Cowork launched, almost nobody looked at it as a tool for non-programmers. Dan Shipper 📧 was one of the earliest to make that claim, and he was damn right. Let Claude Code run from the home folder of your computer with free access, and you get exactly what made OpenClaw exciting. Instead of just arguing it, here's a video walkthrough of a couple of my AI employees i built on the Claude Code harness. And here's the full article:

Claude Code is the OpenClaw alternative you already had. I've been telling people this in person for three months. Now it's a piece on Every 📧. When OpenClaw was at peak hype a few months ago, everyone was going crazy over it — for good reasons. But almost nobody made the obvious comparison: Claude Code already does the thing you love about OpenClaw. In a lot of cases, better. Here's why people missed it. You have to understand the difference between a model and a harness. OpenClaw, at the end of the day, is just a harness on top of AI models. And Claude Code is too — it just got marketed as a coding tool, an alternative to Cursor. Until Cowork launched, almost nobody looked at it as a tool for non-programmers. Dan Shipper 📧 was one of the earliest to make that claim, and he was damn right. Let Claude Code run from the home folder of your computer with free access, and you get exactly what made OpenClaw exciting. Instead of just arguing it, here's a video walkthrough of a couple of my AI employees i built on the Claude Code harness. And here's the full article:

Nityesh

22,895 Aufrufe • vor 1 Monat

You Can Learn AI Agent Harness & Loop Engineering In 19 Min, with LLM Ops, Eval, Tracing and RAG. They went viral not because they're complicated but because they're simple building blocks, and once you see them you can prompt your way to building real systems. 🎬YouTube: Here's the whole thing in one picture. An LLM is a powerful brain that knows everything about humanity and nothing about you or the software you're running. The harness is the set of tools you put on that horse so it runs where you want. Memory gives it context: who you are, what happened before, how to act. The loop lets it call tools again and again, with guardrails so it knows when to stop. Eval and LLM Ops trace every run, score it, and feed the fixes back in so the system keeps improving itself. Master these four and you can read almost any AI agent repo or paper and actually know what's going on. You Can Build Anything. You Can Learn Anything. 💪 Chapters: Intro: the 4 AI agent buzzwords What an AI agent run actually is The memory system: procedural, semantic, episodic What "harness" really means (the horse) Storing and updating memory (databases, skills, summarizer agent) Retrieval: RAG, SQL vs semantic search Tool calling and why agents loop Loop engineering and end-loop guardrails A Claude Code hooks example Eval and LLM Ops: why you need them Tracing every run (Langfuse, LangSmith) Evaluation: LLM as a judge Diagnosing what broke The gate: ship the fix or fix the bug Zoom out: the full system

You Can Learn AI Agent Harness & Loop Engineering In 19 Min, with LLM Ops, Eval, Tracing and RAG. They went viral not because they're complicated but because they're simple building blocks, and once you see them you can prompt your way to building real systems. 🎬YouTube: Here's the whole thing in one picture. An LLM is a powerful brain that knows everything about humanity and nothing about you or the software you're running. The harness is the set of tools you put on that horse so it runs where you want. Memory gives it context: who you are, what happened before, how to act. The loop lets it call tools again and again, with guardrails so it knows when to stop. Eval and LLM Ops trace every run, score it, and feed the fixes back in so the system keeps improving itself. Master these four and you can read almost any AI agent repo or paper and actually know what's going on. You Can Build Anything. You Can Learn Anything. 💪 Chapters: Intro: the 4 AI agent buzzwords What an AI agent run actually is The memory system: procedural, semantic, episodic What "harness" really means (the horse) Storing and updating memory (databases, skills, summarizer agent) Retrieval: RAG, SQL vs semantic search Tool calling and why agents loop Loop engineering and end-loop guardrails A Claude Code hooks example Eval and LLM Ops: why you need them Tracing every run (Langfuse, LangSmith) Evaluation: LLM as a judge Diagnosing what broke The gate: ship the fix or fix the bug Zoom out: the full system

Shen Sean Chen

15,952 Aufrufe • vor 1 Monat

MIT PhD student Alex Zhang on why simple harness abstractions beat models trained around their own harness: "When these abstractions are implemented in a really simple way, basically what you see with Prime Agent is that you can slot in any model. We don't train any model around Prime Agent." "In a lot of cases, we see Prime Agent doing better than models trained around a certain harness. It's purely because these abstractions take advantage of the raw capabilities of the models." alex zhang Seth Karten Prime Intellect

MIT PhD student Alex Zhang on why simple harness abstractions beat models trained around their own harness: "When these abstractions are implemented in a really simple way, basically what you see with Prime Agent is that you can slot in any model. We don't train any model around Prime Agent." "In a lot of cases, we see Prime Agent doing better than models trained around a certain harness. It's purely because these abstractions take advantage of the raw capabilities of the models." alex zhang Seth Karten Prime Intellect

MTS

147,137 Aufrufe • vor 7 Tagen

Claude just became a craacked video game designer. With the launch of Unreal Engine's MCP server last week, you can now build entire video games just by talking to Claude. I spent the past few days building with it, and I'm telling you, this is going to forever change how video games get made and who gets to make them. In this video I show you exactly how to set up the Unreal Engine MCP yourself and run through three demos: building a full playable city, cloning a real city from Google Earth, and creating custom buildings in Blender. Here's the agent harness I mention too: Intro What I built in a few hours Setting up the Unreal MCP server Fixing the port 8000 connection issue The agent harness that avoids the pitfalls Demo 1: Building a city with City Sample Demo 2: Cloning a real city from Google Earth with Cesium Demo 3: Custom buildings with Blender headless Outro

Claude just became a craacked video game designer. With the launch of Unreal Engine's MCP server last week, you can now build entire video games just by talking to Claude. I spent the past few days building with it, and I'm telling you, this is going to forever change how video games get made and who gets to make them. In this video I show you exactly how to set up the Unreal Engine MCP yourself and run through three demos: building a full playable city, cloning a real city from Google Earth, and creating custom buildings in Blender. Here's the agent harness I mention too: Intro What I built in a few hours Setting up the Unreal MCP server Fixing the port 8000 connection issue The agent harness that avoids the pitfalls Demo 1: Building a city with City Sample Demo 2: Cloning a real city from Google Earth with Cesium Demo 3: Custom buildings with Blender headless Outro

Pat Simmons

529,996 Aufrufe • vor 1 Monat

"I think trust is, in any relationship, trust is crucial. And I think that's something that's built over time as well, so even for the tifosi to trust me, i guess is going to take time... and as I said, its not been the season that I was hoping for, it's not the season that everyone had hoped for." "But the only way is up. So I think we'll continue to stay positive and continue to harness the support we have because the support from the tifosi around the world is unmatched. Its incredible. So we need to harness that energy and just keep our heads up. Everyone is working so hard in this team. People are so passionate, everyone is feeling the pain and ... its so important that we just keep putting one foot in front of the other and we don't give up." - Sir Lewis Hamilton

"I think trust is, in any relationship, trust is crucial. And I think that's something that's built over time as well, so even for the tifosi to trust me, i guess is going to take time... and as I said, its not been the season that I was hoping for, it's not the season that everyone had hoped for." "But the only way is up. So I think we'll continue to stay positive and continue to harness the support we have because the support from the tifosi around the world is unmatched. Its incredible. So we need to harness that energy and just keep our heads up. Everyone is working so hard in this team. People are so passionate, everyone is feeling the pain and ... its so important that we just keep putting one foot in front of the other and we don't give up." - Sir Lewis Hamilton

sim

21,125 Aufrufe • vor 11 Monaten

Perplexity CEO Aravind Srinivas on the brutal truth about who actually makes money in AI (and why it's not who you think): Aravind argues that the real value in AI comes from orchestration. He points to products like Codex, Claude Code, and Perplexity Computer: "What is that? It's an orchestration system. It takes a model, pairs it with an agent harness." And what is an agent harness? "The simplest way of describing it is like rules for how the agent loop should run. What are all the skills and sub-agents and connectors and tools it accesses? Without the harness, you don't necessarily capture and convert the intrinsic intelligence in the model into valuable output tokens." This leads to a blunt conclusion about who has a real business in AI, and who doesn't: "If you're literally just a reseller of model tokens, you have no business, because the model will get commoditized. So even if you're a model builder, you don't have a business. As an infra layer, you have some business on serving those output tokens. But as an application layer or model builder, you don't really have a business if you're just a reseller of tokens that come directly out of the model." So where does the value accrue? "You have a business if you know how to take the model, ground it in valuable context, orchestrate it with a really good agent harness, connected to the right set of tools and connectors (whether it's personal connectors or business connectors) and provide the experience to people in one single unified system." Aravind Srinivas then explains Perplexity's specific edge: Beyond orchestrating across tools, files, and connectors, they also orchestrate across models. "That is the differentiation that Anthropic and OpenAI cannot claim, because you wouldn't find GPT-5 inside the Claude Code harness. You wouldn't find Claude Opus inside the Codex harness. These are competing with each other. Whereas you would find both these models inside Perplexity Computer." Why does this matter? Because it all comes down to power. In Aravind's framing, the fundamental cost driver in AI is watts (the one input nobody can subsidize except the government). "Whoever provides the most valuable output tokens with the least amount of power expended to produce them generates the greatest value to the end user, has the most pricing power, has the most value. That is the orchestration problem to solve." His conclusion: "The one single most important metric in AI is token value per watt per user."

Perplexity CEO Aravind Srinivas on the brutal truth about who actually makes money in AI (and why it's not who you think): Aravind argues that the real value in AI comes from orchestration. He points to products like Codex, Claude Code, and Perplexity Computer: "What is that? It's an orchestration system. It takes a model, pairs it with an agent harness." And what is an agent harness? "The simplest way of describing it is like rules for how the agent loop should run. What are all the skills and sub-agents and connectors and tools it accesses? Without the harness, you don't necessarily capture and convert the intrinsic intelligence in the model into valuable output tokens." This leads to a blunt conclusion about who has a real business in AI, and who doesn't: "If you're literally just a reseller of model tokens, you have no business, because the model will get commoditized. So even if you're a model builder, you don't have a business. As an infra layer, you have some business on serving those output tokens. But as an application layer or model builder, you don't really have a business if you're just a reseller of tokens that come directly out of the model." So where does the value accrue? "You have a business if you know how to take the model, ground it in valuable context, orchestrate it with a really good agent harness, connected to the right set of tools and connectors (whether it's personal connectors or business connectors) and provide the experience to people in one single unified system." Aravind Srinivas then explains Perplexity's specific edge: Beyond orchestrating across tools, files, and connectors, they also orchestrate across models. "That is the differentiation that Anthropic and OpenAI cannot claim, because you wouldn't find GPT-5 inside the Claude Code harness. You wouldn't find Claude Opus inside the Codex harness. These are competing with each other. Whereas you would find both these models inside Perplexity Computer." Why does this matter? Because it all comes down to power. In Aravind's framing, the fundamental cost driver in AI is watts (the one input nobody can subsidize except the government). "Whoever provides the most valuable output tokens with the least amount of power expended to produce them generates the greatest value to the end user, has the most pricing power, has the most value. That is the orchestration problem to solve." His conclusion: "The one single most important metric in AI is token value per watt per user."

Big Brain AI

41,566 Aufrufe • vor 9 Tagen