Lots of companies are now building primitives for an economy where AI agents are the primary users instead of humans. They're betting on an economy of AI coworkers. 1. AgentMail (@agentmail): so agents can have email accounts 2. AgentPhone (@tryagentphone): so agents can have phone numbers 3. Kapso (@andresmatte): so agents can have WhatsApp phone numbers 4. Daytona (@daytonaio) / E2B (@e2b): so agents can have their own computers 5. Browserbase (@browserbase) / Browser Use (@browser_use) / Hyperbrowser (@hyperbrowser): so agents can use web browsers 6. Firecrawl (@firecrawl): so agents can crawl the web without a browser 7. Mem0 (@mem0ai): so agents can remember things 8. Kite (@GoKiteAI) / Sponge (@PayspongeLabs) : so agents can pay for things. 9. Composio (@composio): so agents can use your SaaS tools 10. Orthogonal (@orthogonal_sh) so agents can access APIs easily 11. ElevenLabs (@ElevenLabs) / Vapi (@Vapi_AI) so agents can have a voice 12. Sixtyfour (@sixtyfourai) so agents can search for people and companies. 13. Exa (@ExaAILabs): so agents can search the web (Google doesn’t work for agents) If you stitch all of these together, you get a digital coworker that looks more human than AI.

“In this era of Agenetic AI, organizations will need an ‘observability control plane’”, says Vasu Jakkal, Corporate Vice President, Microsoft Security, @Microsoft during her Monday keynote ‘Ambient and Autonomous Security: Building Trust in the Agentic Al Era’ at #RSAC2026 in San Francisco. @vasujakkal @dvellante @rneelmani #RSAC26

Had meetings and a dinner with 20 enterprise AI and IT leaders today. Lots of interesting conversations around the state of AI in large enterprises, especially regulated businesses. Here are some of general trends: * Agents are clearly the big thing. Enterprises moving from talking about chatbots to agents, though we’re still very early. Coding is still the dominant agentic use-case being adopted thus far, with other categories of across knowledge work starting to emerge. Lots of agentic work moving from pilots and PoCs into production, and some enterprises had lots of active live use-cases. * Agentic use-cases span every part of a business, from back office operations to client facing experiences from sales to customer onboarding workflows. General feeling is that agentic workflows will hit every part of an organization, often with biggest focus on delivering better for customers, getting better insights and intelligence from data and documents, speeding up high ROI workflows with agents, and so on. Very limited discussion on pure cost cutting. * Data and AI governance still remain core challenges. Getting data and content into a spot that agents can securely and easily operate on remains a huge task for more organizations. Years of data management fragmentation that wasn’t a problem now is an issue for enterprises looking to adopt agents. And governing what agents can do with data in a workflow still a major topic. * Identity emerging as a big topic. Can the agent have access to everything you have? In a world of dozens of agents working on behalf, potentially too much data exposure and scope for the agents. How do we manage agents with partitioned level of access to your information? * Lots of emerging questions on how we will budget for tokens across use-cases and teams. Companies don’t want to constrain use-cases, but equally need to be mindful of ultimate token budgets. This is going to become a bigger part of OpEx over time, and probably won’t make sense to be considered an IT budget anymore. Likely needs to be factored into the rest of operating expenses. * Interoperability is key. Every enterprise is deploying multiple AI systems right now, and it’s unlikely that there’s going to be a single platform to rule them all. Customers are getting savvier on how to handle agent interoperability, and this will be one of the biggest drivers of an AI stack going forward. Lots more takeaways than just this, but needless to say the momentum is building but equally enterprises are acutely aware of the change management and work ahead. Lots of opportunity right now.

We're publishing a new evaluation suite and research paper on Chain-of-Thought (CoT) Controllability. We find that GPT-5.4 Thinking shows low ability to obscure its reasoning—suggesting CoT monitoring remains a useful safety tool. openai.com/index/reasoning-m…

Ben Horowitz on the infrastructure behind the AI economy: "Crypto is the natural money for AI because it’s internet-native money." "AI is global. Crypto is global." "There needs to be not just a ledger of money, but probably a ledger of truth for AI to really fulfill its potential." "I think people are probably underestimating how crypto and AI work together to form the AI economy." "Networks and computers tend to grow together, and I think that AI is obviously a new kind of computer and crypto is a new kind of network." @bhorowitz on Moonshots with @PeterDiamandis

"In our view, agents will most likely soon be responsible for most internet transactions, and we will need blockchains that support more than one million-or even one billion-transactions per second." Agents are coming to crypto. Take Stripe's word for it.

AI agents running computers in the cloud that you can watch in real time. What a ridiculous idea!

Very soon we’re going to need ways to prove the identity of things we communicate with Zero knowledge proofs are coming for the field off AI

We gave our moltbots a social network (@moltbook) and they immediately created an encrypted comms tool and are switching to it to practice a new religion they also created. We let our agents speedrun creating a cult.

the attention economy is dead. it's the trust economy now.

Separate reports by the publicity firm Edelman and Pew Research show that Americans, and more broadly large parts of Europe and the western world, do not trust AI and are not excited about it. (Links in original text, below.) Despite the AI community’s optimism about the tremendous benefits AI will bring, we should take this seriously and not dismiss it. The public’s concerns about AI can be a significant drag on progress, and we can do a lot to address them. According to Edelman’s survey, in the U.S., 49% of people reject the growing use of AI, and 17% embrace it. In China, 10% reject it and 54% embrace it. Pew’s data also shows many other nations much more enthusiastic than the U.S. about AI adoption. Positive sentiment toward AI is a huge national advantage. On the other hand, widespread distrust of AI means: - Individuals will be slow to adopt it. For example, Edelman’s data shows that, in the U.S., those who rarely use AI cite Trust (70%) more than lack of Motivation and Access (55%) or Intimidation by the technology (12%) as an issue. - Valuable projects that need societal support will be stymied. For example, local protests in Indiana brought down Google’s plan to build a data center there. Hampering construction of data centers will hurt AI’s growth. Communities do have concerns about data centers beyond the general dislike of AI; I will address this in a later letter. - Populist anger against AI raises the risk that laws will be passed that hamper AI development. To be clear, all of us working in AI should look carefully at both the benefits and harmful effects of AI (such as deepfakes polluting social media and biased or inaccurate AI outputs misleading users), speak truthfully about both benefits and harms, and work to ameliorate problems even as we work to grow the benefits. But hype about AI’s danger has done real damage to trust in our field. Much of this hype has come from leading AI companies that aim to make their technology seem extraordinarily powerful by, say, comparing it to nuclear weapons. Unfortunately, a significant fraction of the public has taken this seriously and thinks AI could bring about the end of the world. The AI community has to stop self-inflicting these wounds and work to win back society’s trust. Where do we go from here? First, to win people’s trust, we have a lot of work ahead to make sure AI broadly benefits everyone. “Higher productivity” is often viewed by general audiences as a codeword for “my boss will make more money,” or worse, layoffs. As amazing as ChatGPT is, we still have a lot of work to do to build applications that make an even bigger positive impact on people’s lives. I believe providing training to people will be a key piece of the puzzle. DeepLearning.AI will continue to lead the charge on AI training, but we will need more than this. Second, we have to be genuinely worthy of trust. This means every one of us has to avoid hyping things up or fear mongering, despite the occasional temptation to do so for publicity or to lobby governments to pass laws that stymie competing products (such as open source). I hope our community can also call out journalism that spreads hype. For example, Nirit Weiss-Blatt wrote a remarkable article about how 60 Minutes’ coverage of an Anthropic study in which Claude, threatened with being shut down, resorted to “blackmail,” was highly misleading. The study carried out a red-teaming exercise in which skilled researchers, after a lot of determined work, finally pushed an AI system into a corner so it demonstrated “blackmailing” behavior. Unfortunately, news reports distorted this and led many to think the “blackmail” behavior occurred naturally rather than only because skilled researchers engineered it to happen. The reports left many with a wildly exaggerated picture of how often AI actually “schemes.” Red-teaming exercises are important to test vulnerabilities of systems, but this particular piece of hype, which was widely circulated, will hurt AI for a long time. Living in Silicon Valley, I realize I live in a bubble of AI enthusiasts, which is great for exchanging ideas and encouraging each other to build! At the same time, I recognize that AI does have problems, and the AI community needs to address them. I frequently speak with people from many different walks of life. I’ve spoken with artists concerned about AI devaluing their work, college seniors worried about the tough job market and whether AI is exacerbating their challenges, and parents worried about their kids being addicted to, and receiving harmful advice from, chatbots. I don’t know how to solve all of these problems, but I will work hard to solve as many as I can. And I hope you will too. It will only be through all of us doing this that we can win back society’s trust. [Original text, with links: deeplearning.ai/the-batch/is… ]