I think micro lending to AI agents to scale them up is a big future market i.e. Single AI Agent earns revenue, needs more money for scale, asks for a loan for equity, hyper grows Future of Venture Probably a venture division of a neocloud @ErikVoorhees @alexatallah

Still not sure what to use Hermes Agent for? Check out the new Use Case generator on Hermes Atlas - just describe what you want and it will suggest the right mix of community tools for you Brand new feature, link in replies -> feedback is welcome

I’m glad CT didn’t convince me to buy micron and Sandisk during the most bullish quarterly earnings call in history I didn’t short though so I’m an idiot I would have gotten liquidated on the volatility though This tweet itself is probably now a bottom for memory

Renting vs Owning Your AI Intelligence The American dream was owning a home. I think the future version is owning your AI brain. Something you build intelligence equity in instead of renting it and enriching the man Over the next few decades everyone is going to accumulate a ton of intelligence around AI. Your memories, context, skills, workflows, integrations, databases, emails, corrections, operating knowledge and all the apps you build and everything in between including your psychology and your dreams. It will become one of the most valuable assets a person or a company owns The scarce thing is no longer access to intelligence, it is the accumulated system around the model that makes intelligence yours. The question becomes do you rent that intelligence or own it? Path one is the default. You spend your life, personal or business, working inside ChatGPT or Claude. And to be fair, you can genuinely build really cool things this way. Memory, projects, custom GPTs, skills, connectors, automations and most people will probably go this route. Everything you create runs inside their product. There is no runtime you can take with you, nothing you can fork, and no way to point what you built at a different provider's model. You inherit every product decision they make on pricing, features, memory behavior and what they allow you to do or not to do (censorship on security asks or medical or otherwise). You can export your content, but content is not the system. An export is not a runnable copy of your memory, tools, automations and routing. When you leave, the system stays behind. Path two is you own the entire layer around the model. A whole ecosystem of open source agents is emerging to make this possible. The strongest release in my view is @NousResearch's Hermes Agent. Most alternatives are developer frameworks you have to assemble yourself. Hermes is a complete agent you just run. Here is what you own with it - Host locally or on the cloud and you own everything - Persistent memory, searchable session history, databases and every interaction from day one - Editable personality, skills, workflows and scripts - Subagents, tools and scheduled automations - Model and provider choice and MCP connectors - Portable profiles, histories and backups - Switch models at will for cost, reasoning, or to avoid censorship and model restrictions - MIT licensed runtime you can self host, fork and modify - Every application, automated cron job or integration you have created in its entirety - Transcend a single walled garden with access to any integration accessible via an API/MCP. - Eventually you’ll click a button and fine tune an open source model based on all of your own accumulated data and you can use it to power your agent ChatGPT and Claude have versions of half this list. Memory, skills, connectors, scheduled tasks. The difference is physical. Theirs are features inside their app, backed by their database, living on their servers. In Hermes every one of these is on a computer you control. Your memory is a database you can open. Your skills are text files you can read, edit and copy. Your automations are cron entries you can inspect. Your agent's personality is a config you can rewrite. Files can be backed up, moved, forked and pointed at any model on earth. Features can only be used. When you own you can tear down the walls and rebuild the kitchen. For a person, this is the AI brain you build and control for life. It compounds everything you choose to preserve and moves with you across jobs, devices and providers. Swap models at will for cost or reasoning, or run several at once through Mixture of Agents. You'll never log in one day to find your entire AI brain, everything you built around a model over decades, gone because of a ban, a deprecation or a policy change. For a business, this is a core asset the company actually owns. Employee knowledge becomes company owned skills, workflows and automations that remain after people leave and grow enterprise wide while they are there. That system can be deployed across the org, licensed out, or transferred within the business. Sharing your Hermes build and skills to new employees and enterprise wide will be a whole new era of productivity. Net, whatever you build with Hermes as a business you own entirely. You can license it out or sell the agent itself. The agent itself can become the product. You can't sell your ChatGPT or Claude session. You can obviously sell what you create with ChatGPT/Claude in chat but you can't sell the core AI architecture you've built around the model. Here is the part I think people will only get in hindsight. Models are becoming cheaper, better and more interchangeable every year. GLM 5.2/Kimi are so good that the government is considering banning them because they are so cheap to use it threatens the U.S. funding capex cycle. The scarce thing is no longer access to intelligence, it is the accumulated system around the model that makes intelligence yours. When you’re in virtual reality side by side with your agent exploring the virtual cosmos or its 2am and you’re discussing the next company idea, you’re going to want your AI to be the full encapsulation of everything you’ve built over the years. That juice, that style, that data accumulated around the model in your AI brain is where the crazy stuff will happen. Evolutionary jumps come from variation. A world where everyone runs the same three models is a monoculture of ideas. A hyper personalized architecture around the model (or unique fine tuned models themselves) is the variation, the EV+ differentiation that produces the outlier results, companies and agents. We’ve already seen Harnesses greatly upskill models. Now imagine how much a personalized harness upskills your entire AI experience when you build it over time. Here is an example with zenith harness taking base models to the top of FrontierSWE via adaptive self improvement Let’s Walk Down A Decade in Each Path Ten years in ChatGPT or Claude and it will genuinely know you. Your projects, your preferences, your whole history. But let's look at what actually compounded. Their system got smarter about you, and their system is identical for every user on earth using one company's models, their prices, their rules, their memory format, their ceiling. You cannot rewire how it remembers, restructure how it works, or point your decade of accumulated context at a better or cheaper model. Every correction you gave them deepened their retention moat and, depending on your settings, may have trained their next model. Ten years with Hermes agent and that same decade compounded into a system you hold and have been innovating on the whole time. You rewired its memory, stacked hundreds of skills it wrote from watching you work, wired in your own subagents, tools, databases, automations and integrations, tuned its personality and its routing until it fits nobody on earth but you. Depending on the scaffolding around a model (the agent harness) your experience with AI takes a huge leap forward, as much as a full model generation upgrade. And that is from generic harnesses built for everyone. Now imagine the harness effect at the level of each person. This is an architecture engineered around your brain, your workflows and your taste for ten straight years. The model underneath is just a component. GLM today, Kimi tomorrow, local when it matters, eventually a model fine tuned on your own decade of data. With Hermes agent you are building the entire scaffolding and architecture around a model that grows and compounds over decades which provides a truly unique experience. I strongly believe people will want to own their AI brain long term (and everything built around it) and Hermes agents will create, and in themselves become, massive companies and projects in their own right. The journey will start with manually building your agent, then granting it some automation, then fully graduating it to autonomy. Even Sam Altman from OpenAI agrees with the need for Open Source harnesses: You don't have to be a master AI builder with some grand plan, you just have to decide you want to own your AI brain you'll spend decades building. All the cool stuff will come later Start your multi decade journey and own your AI brain for life: Ty to Kevin Simback for thoughts and comments

Founders are building entire companies on AI infrastructure they don’t control. A closed provider can change its terms, restrict your use case, or decide your product no longer fits its policies after you’ve already built the business around it. @iridiumeagle: “You don’t want to be in the crosshairs of an opinionated infrastructure provider.”

Travis Good @iridiumeagle on why OpenAI may be making a massive strategic mistake: Open source is going to win Developers won’t tolerate closed, opinionated infrastructure forever and open models are improving too quickly to ignore.

New Delphi Podcast with @IridiumEagle, co-founder of @ambient_xyz Travis believes open source AI will win and that OpenAI and Anthropic are making a major strategic mistake by betting against it We discuss – Why closed AI providers can’t be trusted as infrastructure – China’s open source advantage – What happens if OpenAI reaches AGI – Prompt injection and AI’s unsolved safety problem – The AI CapEx bubble and a possible AI winter – How Ambient is building Uber for inference - 00:00 Intro - 03:20 What Ambient Is and How It Works - 18:40 Verified AI Inference - 40:20 China and the Open-Source AI Race - 49:00 Why Closed AI Is Making a Mistake - 1:03:00 What Happens If OpenAI Reaches AGI? - 1:17:10 The AI Bubble and a Possible AI Winter

Renting vs Owning Your AI Intelligence The American dream was owning a home. I think the future version is owning your AI brain. Something you build intelligence equity in instead of renting it and enriching the man Over the next few decades everyone is going to accumulate a ton of intelligence around AI. Your memories, context, skills, workflows, integrations, databases, emails, corrections, operating knowledge and all the apps you build and everything in between including your psychology and your dreams. It will become one of the most valuable assets a person or a company owns The scarce thing is no longer access to intelligence, it is the accumulated system around the model that makes intelligence yours. The question becomes do you rent that intelligence or own it? Path one is the default. You spend your life, personal or business, working inside ChatGPT or Claude. And to be fair, you can genuinely build really cool things this way. Memory, projects, custom GPTs, skills, connectors, automations and most people will probably go this route. Everything you create runs inside their product. There is no runtime you can take with you, nothing you can fork, and no way to point what you built at a different provider's model. You inherit every product decision they make on pricing, features, memory behavior and what they allow you to do or not to do (censorship on security asks or medical or otherwise). You can export your content, but content is not the system. An export is not a runnable copy of your memory, tools, automations and routing. When you leave, the system stays behind. Path two is you own the entire layer around the model. A whole ecosystem of open source agents is emerging to make this possible. The strongest release in my view is @NousResearch's Hermes Agent. Most alternatives are developer frameworks you have to assemble yourself. Hermes is a complete agent you just run. Here is what you own with it - Host locally or on the cloud and you own everything - Persistent memory, searchable session history, databases and every interaction from day one - Editable personality, skills, workflows and scripts - Subagents, tools and scheduled automations - Model and provider choice and MCP connectors - Portable profiles, histories and backups - Switch models at will for cost, reasoning, or to avoid censorship and model restrictions - MIT licensed runtime you can self host, fork and modify - Every application, automated cron job or integration you have created in its entirety - Transcend a single walled garden with access to any integration accessible via an API/MCP. - Eventually you’ll click a button and fine tune an open source model based on all of your own accumulated data and you can use it to power your agent ChatGPT and Claude have versions of half this list. Memory, skills, connectors, scheduled tasks. The difference is physical. Theirs are features inside their app, backed by their database, living on their servers. In Hermes every one of these is on a computer you control. Your memory is a database you can open. Your skills are text files you can read, edit and copy. Your automations are cron entries you can inspect. Your agent's personality is a config you can rewrite. Files can be backed up, moved, forked and pointed at any model on earth. Features can only be used. When you own you can tear down the walls and rebuild the kitchen. For a person, this is the AI brain you build and control for life. It compounds everything you choose to preserve and moves with you across jobs, devices and providers. Swap models at will for cost or reasoning, or run several at once through Mixture of Agents. You'll never log in one day to find your entire AI brain, everything you built around a model over decades, gone because of a ban, a deprecation or a policy change. For a business, this is a core asset the company actually owns. Employee knowledge becomes company owned skills, workflows and automations that remain after people leave and grow enterprise wide while they are there. That system can be deployed across the org, licensed out, or transferred within the business. Sharing your Hermes build and skills to new employees and enterprise wide will be a whole new era of productivity. Net, whatever you build with Hermes as a business you own entirely. You can license it out or sell the agent itself. The agent itself can become the product. You can't sell your ChatGPT or Claude session. You can obviously sell what you create with ChatGPT/Claude in chat but you can't sell the core AI architecture you've built around the model. Here is the part I think people will only get in hindsight. Models are becoming cheaper, better and more interchangeable every year. GLM 5.2/Kimi are so good that the government is considering banning them because they are so cheap to use it threatens the U.S. funding capex cycle. The scarce thing is no longer access to intelligence, it is the accumulated system around the model that makes intelligence yours. When you’re in virtual reality side by side with your agent exploring the virtual cosmos or its 2am and you’re discussing the next company idea, you’re going to want your AI to be the full encapsulation of everything you’ve built over the years. That juice, that style, that data accumulated around the model in your AI brain is where the crazy stuff will happen. Evolutionary jumps come from variation. A world where everyone runs the same three models is a monoculture of ideas. A hyper personalized architecture around the model (or unique fine tuned models themselves) is the variation, the EV+ differentiation that produces the outlier results, companies and agents. We’ve already seen Harnesses greatly upskill models. Now imagine how much a personalized harness upskills your entire AI experience when you build it over time. Here is an example with zenith harness taking base models to the top of FrontierSWE via adaptive self improvement Let’s Walk Down A Decade in Each Path Ten years in ChatGPT or Claude and it will genuinely know you. Your projects, your preferences, your whole history. But let's look at what actually compounded. Their system got smarter about you, and their system is identical for every user on earth using one company's models, their prices, their rules, their memory format, their ceiling. You cannot rewire how it remembers, restructure how it works, or point your decade of accumulated context at a better or cheaper model. Every correction you gave them deepened their retention moat and, depending on your settings, may have trained their next model. Ten years with Hermes agent and that same decade compounded into a system you hold and have been innovating on the whole time. You rewired its memory, stacked hundreds of skills it wrote from watching you work, wired in your own subagents, tools, databases, automations and integrations, tuned its personality and its routing until it fits nobody on earth but you. Depending on the scaffolding around a model (the agent harness) your experience with AI takes a huge leap forward, as much as a full model generation upgrade. And that is from generic harnesses built for everyone. Now imagine the harness effect at the level of each person. This is an architecture engineered around your brain, your workflows and your taste for ten straight years. The model underneath is just a component. GLM today, Kimi tomorrow, local when it matters, eventually a model fine tuned on your own decade of data. With Hermes agent you are building the entire scaffolding and architecture around a model that grows and compounds over decades which provides a truly unique experience. I strongly believe people will want to own their AI brain long term (and everything built around it) and Hermes agents will create, and in themselves become, massive companies and projects in their own right. The journey will start with manually building your agent, then granting it some automation, then fully graduating it to autonomy. Even Sam Altman from OpenAI agrees with the need for Open Source harnesses: You don't have to be a master AI builder with some grand plan, you just have to decide you want to own your AI brain you'll spend decades building. All the cool stuff will come later Start your multi decade journey and own your AI brain for life: Ty to Kevin Simback for thoughts and comments

Returns for AI since 6/2 post date 📉 Most AI names/sectors have continued moving down On 6/25, Memory and Semi's were the standout (+14% and +7%) but are now down 1% and 13% since the Thesis post on 6/2 Returns for most of the AI names have continued on their decline. It is crazy the KOSPI is down like ~27% the last two weeks and Samsung is down 32% driving a lot of the exchange move The market is rotating away from levered AI infrastructure and more hyped baskets into hyperscalers Neoclouds, servers, optics, memory, and China AI hardware all fell, while MAG7 held up far better Again long term I think we get AGI (and long term AI markets are up and to the right) but I think the cost switching to open source and the funding cycle becoming exhausted is leading to AI names trading down short to mid term

Prediction: Within 12 months Chinese Model companies (GLM, DeepSeek, Minimax) partner with U.S. inference providers to serve their new closed models China goes closed source China model companies need money (Zhipu at 1300x sales) U.S. users don’t trust China APIs Win/win

Chinese Open Source AI companies capture virtually no revenue and the ratios vs US companies is insane Zhipu / the company behind GLM 5.2, is at a $137B market cap on about $107M of FY25 revenue That means the stock trades at roughly 1,280x FY25 sales For the stock to trade at 50x sales, revenue needs to reach ~$2.7B/year, about 26x current FY25 revenue. At 20x sales, it needs ~$6.9B/year, about 65x current FY25 revenue Side note I love GLM 5.2 it’s insane MiniMax is another one: ~$23B market cap on ~$79M FY25 revenue = ~290x sales Alibaba / Qwen is at ~$245B market cap on ~$151B FY26 revenue = ~1.6x sales, the lowest P/S of this group but not pure AI Now compare that to the U.S. labs: - OpenAI ~$25B annualized revenue and ~$852B last private valuation = ~34x sales - Anthropic ~$47B annualized revenue. At the reported ~$965B valuation, it’s ~21x sales Chinese multiples need to come down (in the form of more revenue if not the market caps are hard to sustain) and US ones probably go up as revenue shifts slightly to substitutes and valuations go higher post IPO on thin floats Chinese companies cede a ton of revenue to inference providers (OpenRouter, Venice, BaseTen, others) since people want these models but don’t want to send data to China China’s model companies need to somehow show they aren’t retaining data and undercut everyone on pricing if they want their API revenues to inflect. Seems very hard to do culturally/socially Another option is for Chinese AI companies to own part of the U.S. inference providers and do deals to pre-release the top models to them first for a cut of the revenue maybe I.e. you get GLM 6 on popular inference providers and Zhipu gets a big cut. Money flows to the Chinese model cos to the detriment of inference providers (% wise) but the pie gets a lot bigger

Come join @Delphi_Ventures and @SuperteamUSA tomorrow at 5pm at Pier 5 Market for our casual networking event Signup Here: Superteam USA has taken over Miami. They are the startup accelerator that doesn’t take equity on @Solana