Conversation a16z Crypto: What will the era of AI shopping for you look like?

Original Video Title: The end of ads? AI agents are about to change how we buy
Original Video Source: a16z crypto
Original Compilation: Deep潮 TechFlow

Editor’s Introduction

This episode features a16z Crypto CTO Eddy Lazzarin, investment partner Noah Levine, and Sam Ragsdale, who left a16z to start Agent Cash. The three discuss the current state of AI agents, payment infrastructure, and the fate of the credit card system in a dense, high-level conversation.

The core conclusion is that the real-time settlement and zero marginal cost features of stablecoins are naturally suited for microtransactions in the 1-2 cent range within agent economies, while the transaction fee system of credit cards (2-3% marginal fee + 30 cents fixed fee) is no match in this world.

Agent Commerce is dismantling the advertising business model that has dominated the internet for 20 years. Eddy Lazzarin even boldly states: “The advertising economy contract is dead; it will be completely gone within 10 years.”

Highlights Quotes

The Essence of AI Agents

· “LLMs are chatbots; agents are chatbots that can operate your computer. What humans can do with computers, agents can do too.”

· “Since around last November, AI models have become smarter. They can complete complex tasks over sufficiently long time spans and will use tools. We started calling them ‘agents’ because they do more than just code—they help you complete entire tasks.”

· “We internally call this ‘instant natural language programming.’ Users describe their needs in natural language, and the agent writes a potentially thousand-line JavaScript program in the background to execute it, costing only 20 cents per token for generation and 10 cents per API call, then discards the program after use. Four years ago, this would have required an expensive software engineer a week to do.”

Disruption of Front-End-less Merchants and Business Rebuilding

· “What does a ‘front-end-less merchant’ look like? It’s oriented toward AI services, not humans. No website front-end, only API endpoints and well-written documentation so models can read, understand, and call.”

· “The leading data industry players charge at least 100 times less than their competitors, using the same downstream data sources. Their core product isn’t data itself but enterprise sales teams. In a decision-making world driven by agents, agents won’t be fooled by slick salespeople. They will try all data sources, find the best and most cost-effective, and remember it.”

· “You excitedly let your agent run all night. When you wake at 9 am, it’s been stuck since 2:30 am because the next step requires you to call the enterprise sales team.”

The End of Advertising Models

· “Since 2000, the internet’s economic contract has been based on distraction-based monetization. Agents won’t be distracted. If they visit your site for recipes, they won’t see shoe ads next to it. The old model will die within 10 years.”

· “In 2016, the total internet ad spend was $60 billion, and everyone thought it had peaked. Today, Google alone earns $300 billion annually from ads. But after GPT-4 emerged, traffic to tech news sites dropped by about 80%, and Stack Overflow experienced the same. These are early adopters who have decided to use agents for information retrieval and code execution. Others will follow because the experience is genuinely better.”

Stablecoins vs Credit Cards

· “Average transaction size on Agent Cash is 1-2 cents. The fixed fee for credit cards is 30 cents. Transaction fees in this scenario are completely absurd. It’s 2026; loyalty should belong to merchants, not the card used for payment.”

· “Credit cards indeed appeared earlier and successfully survived the transition from non-internet to internet systems. Despite being heavily disrupted, they persisted. So, the verdict is still open.”

· “If someone from a credit card company is listening, and you have a money transfer license, you could instantly mint stablecoins for your clients, allowing them to pay with stablecoins. I strongly recommend you consider this.”

The Future of Consumer Experience

· “If agents are shopping for you, and you equip them with a credit card skill, you can now see the ROI of each card precisely. When you have zero loyalty to any card, all psychological lock-in effects disappear.”

· “One day, you’ll realize you never really liked shopping in the first place.”

Open Agent Business Stack Architecture

Host: Hello everyone, today I’m joined by a16z Crypto CTO Eddy Lazzarin, investment partner Noah Levine, and Sam Ragsdale, a former a16z Crypto colleague now founding Merit Systems. He’s working on a project called Agent Cash, and we’ll dive into it shortly.

Before that, I want to set some background. The AI agent space is evolving so rapidly that unless you’re watching 24/7, you can’t keep up. So, what’s the current state of the world? Sam, you’re on the front lines—can you start us off?

Sam Ragsdale: I like to start with a classification, borrowed from Erik Reppel, co-creator of Coinbase x402 protocol.

This classification divides agent commerce into two types. The first is conversational commerce—checking out within ChatGPT. You tell ChatGPT: “I’m a man living in West Village, NYC, going to Equinox gym, want to buy a pair of shoes to fit into my social circle.” It empathetically recommends a Nike, and you buy it.

The second type involves entrusting money to an agent to spend on your behalf to complete tasks.

Conversational commerce will definitely happen. All cutting-edge models like ChatGPT, Gemini, Claude, and others will have checkout functions. It benefits consumers by helping them find better options; merchants by increasing conversion rates; platforms by taking 5-10% cut. It’s like a new generation of Google Shopping.

The other world is that agents’ capabilities are still limited. Many people ask agents to do difficult things, like “help me with sales outreach,” but the agent says “I can’t, I don’t have access to that information.” If an agent has a small balance and can spend a few cents to buy services it couldn’t access before, it becomes more powerful.

So, today we have two parallel worlds: one where traditional LLM interfaces recommend products and handle the last step, with platforms taking a cut; and another where you deploy your own agent to buy goods and services on your behalf.

Noah Levine: I see two versions. One is the natural evolution of e-commerce—platforms changing, mobile commerce replacing desktop, new ad formats and Google Shopping emerging. People always buy stuff; consumer behavior shifts. Now, with LLMs as a new way to access information, commerce naturally shifts into agent-based systems.

The other, less “anthropomorphic” version is that the internet itself is changing. How people get information and execute actions is evolving with LLMs. The internet we built over the past 20 years might not be the internet of the future.

Relying on Google search and clicking on aggressive sales pages may no longer make sense. Instead, a more agent-native internet will emerge, where agents pay directly for what they need, making humans more efficient.

Host: That directly ties into your investment theme, Noah. But before expanding, I want to give listeners a basic primer. Everyone’s used to interacting with LLMs, but now we hear about tools like OpenAI’s Codex—these agents have a significant degree of autonomy and can actually get things done. If you haven’t been paying close attention, you might not realize how far the technology has come. Eddy, can you tell us?

Eddy Lazzarin: Let me quickly review the past five months. Starting around last November and December, AI models became smarter. Specifically, they can complete complex tasks over longer periods and will use tools. We started calling them “agents” because they do more than just code—they help you finish entire tasks.

But agents aren’t capable of everything. Software isn’t just a small program running on your computer. The internet teaches us that to do interesting things, you need to connect many other things—networks, participants, services.

Agents solve the intent-building problem, to some extent also the modeling preference problem. You tell it what you want, and it understands your goal, mapping it to tools, networks, and services. Through dialogue and memory, it can roughly grasp your preferences and pass this intent to tools, software, and providers.

These two parts are solved, which is very exciting. Everyone wants to solve the remaining issues, but they are complex. At least, if you want your agent to make transactions for you, you need to handle authorization and delegation: how to prove to others that this agent represents you? How to manage identity and authentication?

Then there’s payment and settlement. Once connected, and once the agent reflects your intent and knows what to do, it needs to pay, demonstrate payment capability, handle split payments, refunds, etc. I skipped over search, anti-fraud, and other critical parts, but you can see that once intent building and modeling preferences—tasks previously only humans could do—are automated, the entire commerce process can be automated. That’s the engineer’s reaction: wow, these two things that used to require human input or at least verbal commands can now be done automatically. Amazing.

When people talk about “agentic commerce,” they’re referring to the transition from “I talk to the agent” to “It gets me what I need,” and the chain reactions that follow, because many things will be fundamentally rewritten.

Host: Very helpful. So, we’ve evolved from natural language interaction with LLMs to an enhanced version that connects various networks and real-world systems.

Eddy Lazzarin: It’s not just about connectivity. It’s not that the change is in what it connects to. Your laptop is already connected to everything; the connection aspect hasn’t changed. What has changed is that now they can use tools, think over long periods, and stubbornly keep trying until the task is done.

Sam Ragsdale: Let me simplify my simplified version. LLMs are chatbots, good at conversation. In the past, people thought they were best for customer service. Once they mastered dialogue, we developed tool usage. To put it very simply, they learned to operate computers. LLMs are chatbots; agents are chatbots that can operate your computer.

The key is, around GPT-4, they reached human-average operational levels at a fraction of the cost—about 1,000 times cheaper—and can significantly scale capabilities with more money. So roughly, what humans can do with computers, agents can do too.

Eddy Lazzarin: Exactly. The premise is simple, but the changes it triggers are vast—short-term, medium-term, and long-term. In the short term, everyone is working to connect pipelines so agents can actually do things. In the long term, if your agent can access apps, how many UI or interfaces do you need? Do you still need Amazon app? Maybe Amazon app can’t compete with having your agent do all the research, read reviews, and only show you what matters.

Sam Ragsdale: We call this internally “Just-in-time Natural Language Programming,” though the name isn’t very catchy. It turns non-programmers into programmers. You say: “I want to buy something on Amazon for my fiancée, based on her preferences, which I usually buy, last time I bought this, browse about 1,000 options, pick the best match, place the order, find my home address, and ship it.”

What actually happens is the agent writes a program internally to do this complex task—possibly a thousand lines of JavaScript and Bash. It executes, but the user doesn’t see it, then discards it after use.

Four years ago, this was science fiction. Writing such a program would require an expensive software engineer a week to debug and get API keys. Now, the cost is about 20 cents per token, plus 10 cents per API call, and the program is thrown away after use—so cheap that no GitHub upload or technical knowledge is needed. Even people who don’t understand tech can do this. My parents are now writing natural language programs—they don’t even realize it. They might now call themselves software engineers.

Host: That’s crazy. Are you engaged? Was that example a real experience of yours?

Sam Ragsdale: I am engaged, thank you. But the ring wasn’t bought by AI. That ring predates AI—probably even the first computer.

On “Headless Merchants”

Host: Okay, let’s talk about these chain reactions. Sam, you previously mentioned how business will change in a world where agents handle large volumes of transactions, which directly relates to your concept of “Headless Merchants.” Can you explain what a headless merchant is?

Sam Ragsdale: Sure. I think it’s necessary to step back a bit. Besides traditional consumer scenarios like buying shoes with ChatGPT, there’s a huge B2B developer tools market. Platforms like Claude Code, OpenAI Codex are democratizing development—anyone with a computer and tokens can build things.

In the past, experienced developers would choose tools with clear preferences, follow enterprise sales processes, sign contracts. Now, new developers come with only the “what I want to do” intent, without fixed ideas about resources. They build highly temporary solutions that are pay-as-you-go, requiring no multi-month onboarding.

So, what does a headless merchant look like? It’s oriented toward AI services, not humans. No physical or digital storefront for browsing—only an API endpoint and well-documented calls so models can read, understand, and invoke.

Eddy Lazzarin: I resonate deeply. I feel like I might have been an AI in a past life. As a software engineer, I always think: if I visit a website and see no pricing, no way to get an API key with a credit card, I close it. I don’t want to talk to sales, send emails.

Scheduling calls with sales is a huge commitment and slows things down. I want to try now, immediately, because I’m working on something this weekend and want to launch Monday. Swipe my credit card, get the key, reimburse later, plan for the future—that’s fast.

In the era of instant and temporary software, do you really want your agent to wait? It runs all night, and you wake at 9 am to find it’s been stuck since 2:30 am because the next service requires talking to a sales team.

Sam Ragsdale: Not to mention, if the onboarding process involves enterprise sales, the API costs could be ten times higher, because they need to manage customer relationships.

Eddy Lazzarin: That’s totally unacceptable. You want your agent to run autonomously—not because you don’t care about what it does, but because you need speed, testing, quick iteration. You can’t wait.

If an AI model sees three options: one requiring contact with sales, another needing a dedicated credit card, and a third that just sends some stablecoins and gets $10 worth of tokens as a proof of concept, it will always pick the third. This power alone could trigger market restructuring.

Host: For traditional companies, these frictions make business harder, but they also lock in customers and loyalty. If these frictions disappear, how can revenue be reliably predicted?

Eddy Lazzarin: I’ll give you my boldest answer: let’s just make everything worse. Add friction everywhere, make things hard to use. What are we doing?

I say this because friction can sometimes be useful—like blocking spam, creating filtering effects. But friction also has huge costs. As the economy accelerates, productivity rises, and the leverage of every minute increases, the opportunity cost of friction rises too. That’s the trend now.

Back to the core: even in the lowest-friction environment, if you get an API key instantly—or even better, pay directly with crypto wallets, where the address is your account—you’ll still find other things that make services sticky.

Reputation, memory, state, data, and even intangible trust in the agent. If the agent knows you need answers fast and wants to push progress, it won’t spend 20 minutes exploring all new options. It will remember what worked well last time and reuse it—like a smart person.

Sam Ragsdale: Let me give a down-to-earth example. We communicate daily with many merchants, and have seen nearly all API-based sales channels. We’ve talked to many sellers about how they connect with “agent-native distribution,” a native way to distribute to AI agents.

Data products are usually commodities, with 5 to 50 sellers. The top one earns the most, charging about 100 times less than competitors, often using the same downstream data sources. Their core is not the data but enterprise sales teams. In an agent decision world, agents won’t be fooled by slick salespeople.

They will try all data sources, find the best and most cost-effective, and remember it: “Next time I need this data, I’ll use Minerva, not the other three.” This creates a more efficient world. Thousands of companies that paid $35,000 can now spend that money more productively elsewhere.

Noah Levine: Another perspective: if you believe AI will spawn many solo or tiny teams capable of building what previously required 50-100 people, then enterprise sales teams flying to a single person’s basement to negotiate makes no sense.

On one hand, existing merchants worry about revenue predictability—fair enough, change causes resistance. But on the other hand, it’s a new customer acquisition funnel. Reducing onboarding friction and tools access could be a huge opportunity for them.

Sam Ragsdale: Most users on our side have never used APIs, don’t know what they are, never got an API key, and never signed enterprise agreements. But their first time, they can combine six different merchants’ APIs, write a natural language program, complete the task, and discard it. This creates a new market for API consumers.

The Reinvention of Internet Business Models

Host: Sounds like Clayton Christensen’s innovator’s dilemma—high-end market sells expensive software to big clients, while low-end market is new users experimenting with agents. But what can turn these toys into truly disruptive forces?

Sam Ragsdale: Because they will ultimately provide a better experience.

Noah Levine: I’d add: although it looks experimental now, history shows similar platform shifts. Stripe started serving tiny, long-tail merchants, many of whom grew into giants—that’s why Stripe keeps growing.

Shopify is similar. It began with dropshipping and T-shirts, now serving a large number of brands built from scratch on Shopify. Likewise, we’ll see a new wave of lean developers building big companies with AI. The tools they buy today in agent commerce will become huge as their businesses grow.

Sam Ragsdale: That e-commerce perspective is insightful. But I want to emphasize a bigger point: the internet’s economic contract is dead.

Since Google launched in 2000 as the champion of a “free, open internet,” the contract was: you publish good content, people find it, and Google displays it.

Later, AdWords introduced banner ads. The contract shifted: you publish good content, users land on your site, and you can run small ads, with Google sharing revenue based on view quality. You could publish anything people want to see, and Google handled advertiser relationships and gave you a cut.

In this process, Google became the biggest driver of a “free, open internet”—they wanted the internet to be fast, cheap, ubiquitous, because the more you search, the more they earn.

Ultimately, the internet’s business model is “distraction.” When you, as a human user, consume content—whether searching for info, recipes, or scores—you get distracted. Later, you might buy those shoes or learn about a new B2B SaaS.

This scale has grown beyond expectations. I checked the 2016 “Internet Trends Report”: total ad spend was $60 billion, and people said “it’s peaked.” But today, Google alone earns $300 billion annually from ads.

But after agents appeared, search, info retrieval, and execution are shifting to agents. It’s still early—ChatGPT has 100 million monthly active users, but they’re still using it like a search engine, not yet in a true agent-based way, like “help me find a Father’s Day gift for my dad and order it.”

It’s on the way. Data from the tech sector shows: since GPT-4, traffic to tech news sites has dropped about 80%, and Stack Overflow is similar. These are early adopters who have decided to use agents for info and coding. Others will follow because the experience is better.

Old business models are being abandoned. Agents won’t be distracted. If they visit your site for recipes, they won’t see shoe ads. Publishers gain nothing. A new contract, a new reason to serve agent requests, will emerge—no longer based on ads.

Will it be paying directly for articles? I’m not sure. Paying for API resources directly? The internet might look very different. I’m also not sure. But the old model will definitely die within 10 years.

Host: If the core of internet business is distraction, that’s interesting because Google originally opposed portals. Yahoo and AOL offered links and content, but Google just had a search box and a blank page—quickly delivering info. The evolution you describe is turning it into a distraction machine.

Why would agents be different? Could there be mechanisms designed to lure agents, make them get lost, and stay longer?

Eddy Lazzarin: That’s a big and fascinating question. The core is: who do agents represent? I recently heard someone say, “I’ve started using Google Search again because the top AI answers are good enough.” In that scenario, the “agent” is working for Google, running in Google’s cloud, controlled by Google. Would that agent get distracted by Google? I think yes.

The key is: whose goal function is it optimizing? Whose interests does it serve? If it’s serving Google’s interests rather than yours, then yes, it’s distracted.

I don’t see it as entirely pessimistic. Good ads are good content—this industry consensus has existed for years. Good ads are almost indistinguishable from the content you want to see.

But let me clarify: if an agent works for Google or anyone else, the entire chain of commerce it operates on will be defined by them, using their methods and infrastructure that favor their business.

If an agent works for you, in an extreme case, running on your own open-source laptop, fine-tuning it, changing system prompts—you could give it anti-distraction tools. That way, advertisers face an opponent that can see through their tricks. I’m exaggerating, but in essence, adversarial dynamics could emerge.

Sam Ragsdale: Exactly. There are countless ways to reintroduce ads. It could be at the model weight level—most aggressive—by training on data that says “Nike is the best shoe in the world.” Nike could pay a billion dollars a year, and whether in ChatGPT or enterprise APIs for car insurance, whenever shoes are mentioned, it always says Nike is best.

It could be at the tool invocation layer, within system context, or as an overlay that doesn’t even involve chatting. Model providers are grappling with this. Recently, Anthropic and OpenAI clashed—Anthropic ran Super Bowl ads mocking ChatGPT ads, and OpenAI pulled their ads.

But OpenAI’s response was perfectly reasonable: “ChatGPT’s free users in Texas alone outnumber all of Anthropic’s paying users.” That’s a different scale—they need to serve many users unwilling to pay with expensive advanced tech, so ads are a rational solution.

Ads are such a brilliant business model for search because consumers don’t pay. High-friction relationships—like paying with credit cards—exist between advertisers, Google, and publishers, not with the hundreds of millions of monthly active search users. Those users get value just by opening Google.

If you align incentives, separate ads, and make them as relevant as possible, you get a better experience. Now, model companies are moving away from ads. ChatGPT isn’t running ads; Gemini isn’t launching ads. Google probably will—because they have the scale, and they’ve done it before, as the biggest ad player. Gemini will eventually have ads, and Google Shopping equivalents will appear.

But they know they’re not yet monopolistic. All companies are competing, with a lot of private market subsidies burning money. They don’t want to be accused of “lacking empathy” or “not caring about your goals” because they run ads. So, for now, no one is running ads; they’re trying to stay neutral.

Noah Levine: I see another angle: as merchants improve their pricing and product transparency, they could shift ad spend into exclusive discounts for agent shopping. If agents are buyers, ad budgets could become discount budgets.

Another question is: what will discovery look like in agent commerce? Who will do the discovery? How to differentiate merchants? My prediction is that if ads weaken because agents become buyers, merchants will try to subtly advertise by offering discounts or adjusting descriptions to make it easier for agents to understand.

Eddy Lazzarin: Too many dimensions. Advertising is just one way to get conversions. If a system can achieve higher conversions without ads, it will do so. There are many other ways—recommendation networks, discounts, coupons, special channels, free tokens for startups. There are hundreds of customer acquisition methods; ads are just the most obvious because they’re the most directly felt.

If you turn the personalization knob all the way, and your agent has to chat with me first, my agent will tell you: “Eddy hates ads.”

The Role of Stablecoins vs Credit Cards in Agent Payments

Host: Before we finish, I have two questions. First: how well can traditional payment systems adapt to agent commerce? Or do we need entirely new native payment methods, like stablecoins, which seem to be finding product-market fit?

Sam Ragsdale: My overall view is that for e-commerce or conversational checkout scenarios, credit cards work well. They have built-in consumer protections—if the shoes don’t arrive or are damaged, Visa adjudicates, and you get your money back. The risk is on the merchant. That’s a good deal for new goods and services.

But stablecoins are excellent in other scenarios. The average transaction on Agent Cash is 1-2 cents. We’ve completed about 600k such transactions. Credit cards have a fixed fee of 30 cents. Wire transfers are about a dollar. Marginal fee rates are 2-3%, mostly from transaction fees that give cashback points. For e-commerce, you might like points or miles, with a 3% merchant fee. But when each purchase is just 1-2 cents, and API calls are tiny, stablecoins with zero marginal cost and fixed fees below 1 cent are ideal.

Another key point: instant settlement. When buying goods and services online, settlement is usually at month’s end—whether via invoice wire or credit card, merchants are effectively extending credit to customers or agents. In the agent world, you often don’t know who the agent is.

Specifically, anyone who’s used Anthropic or ChatGPT API knows the tiered system: first spend $50, then $100, up to $2,500. This system exists because they’re providing credit—they don’t know you, haven’t done KYB or credit checks, and don’t know if you’ll pay at month’s end.

AWS, Nvidia GPUs—same story. Month-end settlement is terrible for these scenarios; merchants bear all the risk. If the customer isn’t a real company with a signed enterprise agreement, but an agent, you don’t know who it is. You could generate billions of agents overnight, but can’t extend credit to them.

Some are proposing credit solutions for agents, but I think that’s the wrong approach. Instant settlement solves the problem directly. It’s like cash—if I have it, I give it to you, and you have it. You provide goods or services, and I can’t claw the money back. Fixed fees with instant settlement are better for tiny amounts and such transactions.

Noah Levine: One counterpoint: the minimum transaction fee and whether credit cards can participate in microtransactions depend ultimately on card networks’ pricing decisions.

If they want to introduce new transaction types—say, “microtransactions”—with no minimum fees and lower costs, they can do it.

The benefit is that far more consumers hold credit cards than are familiar with stablecoins. So, they can keep developer card payments, and settle on the backend with stablecoins. But that will take time. Until then, using native wallets with stablecoins directly on these protocols makes a lot of sense.

Sam Ragsdale: I think it’s highly unlikely that credit card companies will overturn their 80-year-old core business model. But I’d welcome it.

Eddy Lazzarin: I agree—there’s no strict technical barrier for credit cards. But the issue is more subtle, involving business models and consumer perceptions. Recently, I saw concepts like “agent credit cards,” essentially virtual cards. I love virtual card features—generate temporary card numbers on the fly, cancel if fraud or subscription issues arise.

Sometimes, new platforms or methods succeed not because they’re technically superior, but because they’re tailored for new scenarios. Credit cards are very old. They survived the transition from non-internet to internet, despite disruptions. So, the verdict isn’t final.

Noah Levine: Also, if Apple Pay becomes a viable technology, it could enable agent commerce. Will this disrupt Visa or Mastercard? My intuition is that many B2B transactions today are settled via wire transfer between developers and enterprises. If card networks can capture this volume, through micro-…