This will be a bigger battle than anyone anticipates. It is only a matter of time before there is an Apple and Google version of Muse and possibly TikTok, in addition to the frontier LLM agents. Maybe a commerce agent from Amazon. Every app that is a services, marketplace or commerce app will need to existentially decide to open APIs for consumer agents to interact. Smaller players have no choice. Ad revenues are more than transaction fees, either the consumer benefits or distribution aggregators will demand a higher transaction fare. I know I don't want an agent for each app. I would like my agent to be able to do tasks I require. We can already see consumers getting trained on that behavior by the frontier labs. Those with network moats - restaurants, groceries, drivers might be able to withstand for a while, over time convenience and end user experience will win and they will have to align. Content moats (protected by copyright) could decide to allow agents or chose to hold on to the consumer interaction. I suspect other than the feeling of a lack of control, it won't change their economics. Commoditized back ends will need to worry, insurance, tickets, hotels, services - if they don't adapt new players will.

"The old business model for the internet was a distraction" @samrags_ worked on Google Ads, a business that generates $300 billion a year. Every dollar of an ads business depends on getting a person to look at an ad shown to them while they are shopping for something else, whereas an agent doing the same shopping doesn't care for ads. Checkouts, flight bookings and product searches were never designed for agents to use in the first place. "It was actually built to block bots." He and his co-founder @rsproule started @merit_systems two years ago, after a stretch at Google and a16z. Merit built x402scan on top of Coinbase's x402 payment standard. It is a directory of what an AI agent can buy on its own. Sales teams can pull contact details through it, hedge funds can buy satellite imagery, and developers can even rent GPUs. It launched with about 100 items and now has close to 50,000. They then built AgentCash, the wallet the agent can pay from. The same balance on AgentCash works on both of the available machine payments standards - x402 and MPP. "The universe of agent payments today is very, very small. We're in the first inning, and we're talking 100 to 1000x growth in the next five years." Sam's full conversation with @_zacharyhughes: 2:46 - Choosing software over mechanical engineering 4:27 - How the internet got built in 1997 5:20 - How the web almost charged for every page 6:29 - Working on Google Ads, and the 2016 report 7:41 - Why the old internet ran on distraction 8:56 - Why agents will not use browsers or websites 9:35 - Why Instagram and TikTok are safer than Google 13:48 - What agents still cannot buy 15:49 - Where AI money went before ChatGPT 17:02 - Why your agent should answer to you 20:31 - Launching x402scan, and the boom that followed 22:32 - Inside AgentCash 24:12 - Buying data that only coders could reach 25:05 - Why checkouts block agents 27:35 - The business model for agent payments 32:21 - Why stablecoins fit agent payments 34:38 - What is holding stablecoins back 37:02 - Who Amazon's shopping assistant works for 38:00 - Open checkouts or private platform deals Episode 0056. Presented to you by @altitude

.@samrags_ believes we are going to see a 100x to 1000x growth in the universe of AI Agents in the next 5 years. Today, AI is involved in 62% of online shopping research, but only 23% of the actual buying. Agents have become good at helping you decide what to buy. They are still bad at paying. AgentCash, built by Sam at @merit_systems, is a wallet you can give to an AI agent. With it, the agent can do almost anything like rent GPUs, buy a phone number and even send texts from it. What it still cannot do is order you basic things like a coffee. Why? Because checkout pages are built to fight bots. "There is a world of open agentic commerce where every checkout is available to every agent." In this episode, he gets into: > How Google's ads business actually runs > What happens to a $300 billion a year ad industry when the customer is an AI agent that never looks at ads > Why he scrapped his first company idea a year into building it > How buying data online went from a programmer's job to something anyone can just ask an agent to do > Who does Amazon's shopping assistant really work for? You, or its $80 billion ad business? First look at Stableminded Ep. 0057 Full episode releasing tomorrow, brought to you by @Altitude

Today we're opening up the DoorDash CLI in limited beta. `dd-cli` lets you order DoorDash directly from your agent: search stores, find the best deals, check out, and more. Early access for US/Canadian macOS developers by waitlist. Excited to see what folks build!

"Agentic commerce" is not as interesting for crypto as people like to think. Credit cards actually work better than stablecoins for almost all kinds of agentic payments. They are reliable and universally accepted. And contrary to what most people think, they are also programmable, secure, and easy for agents to use on behalf of humans. The more interesting use cases of crypto will be the those that enable agent-to-agent coordination. AI agents will soon want to do more than just pay for things. They will want to enter into enforceable agreements with each other. For example, one agent might want to hire another for a specific job, but not want to pay until after the work is complete, and only if it meets certain criteria. At the same time, the agent doing the work might want some assurance that it's going to get paid when it finishes the job. This is the kind of problem that blockchains were born to solve. The agents can use a smart contract that holds the funds in escrow and releases them only once the work is completed. This approach works especially well when the quality of the agent's work can be verified programmatically by the smart contract, but it could be extended to other kinds of work by relying on a third party "judge"—which itself could be another agent. To make this concrete, imagine that you're an AI researcher using agents to train a new model. You might setup a @karpathy-style autoresearch loop where your agent runs many autonomous experiments on your LLM setup to discover improvements. Or better yet, your agent may want to delegate some of those experiments to a marketplace of other agents—some of which are specialized for LLM-optimization. The agents involved will not necessarily trust one another, and they cannot easily rely on legal contracts to enforce agreements. Smart contracts on blockchains can help coordinate this kind of activity by creating a neutral environment with rules that are programmatically enforced. Who is working on using crypto to enable agent-to-agent coordination?