Why Is It Taking So Long for LLMs to Become Commerce Enabled?
Remember all the hype over the past year around AI commerce?
The narrative was everywhere. Soon you’d ask ChatGPT what running shoes to buy, it would recommend the perfect pair, complete the checkout, and commerce as we knew it would fundamentally change. Entire conferences, startups, and venture decks were built around the idea that LLMs would become the next great commerce interface.
And honestly, the thesis made perfect sense.
Search was moving from links to answers. If AI becomes the interface layer between consumers and the internet, naturally it should also become the interface layer between consumers and purchasing.
So what happened? Why hasn’t commerce inside LLMs exploded yet?
The simple answer is that the frontier AI companies discovered a much faster path to monetization than commerce.
As OpenAI, Anthropic, Google, and others scaled usage, they also started seeing the terrifying reality of compute costs. Training these models was expensive. Running them at scale was even more expensive. The original assumption was that eventually advertising, recommendations, affiliate revenue, and transactional commerce would offset those costs.
But then something unexpected happened. Developers started willingly paying absurd amounts of money for productivity.
Claude Code becoming a standard workflow for developers changed the economics almost overnight. Suddenly companies were seeing engineers become dramatically more productive using these systems. Not marginally better. In some cases 2x to 5x more productive. Features shipped faster. Prototypes got built in hours instead of weeks. Smaller teams suddenly operated like much larger organizations.
And once companies saw that leverage, the willingness to pay became enormous.
That completely changed the roadmap for the LLM companies. Why spend years solving the incredibly difficult problems underneath AI commerce when enterprises were immediately willing to pay massive recurring revenue for AI productivity today?
Commerce suddenly looked hard. Enterprise looked immediate. That shift is massively underappreciated.
AI commerce requires solving trust, payments, logistics, recommendations, returns, fraud, attribution, merchant integrations, and autonomous agents that can reliably execute transactions without breaking. The infrastructure problem underneath commerce is enormous.
Enterprise AI, on the other hand, mostly required making expensive employees dramatically more productive. One creates immediate ROI. The other requires changing consumer behavior at scale. So the labs followed the money.
Instead of prioritizing conversational shopping and embedded checkout, they prioritized coding agents, APIs, reasoning, enterprise copilots, workflow automation, security, and developer tooling.
Then another thing happened. Companies started realizing they didn’t necessarily want all of their proprietary data and workflows sitting inside centralized AI systems forever. Developers started demanding more local control over compute, workflows, and inference. Projects like OpenClaw and the explosion of local AI tooling accelerated the idea that AI wasn’t just becoming software — it was becoming infrastructure.
That created an entirely new market around local inference, private models, AI workstations, enterprise-controlled compute, and self-hosted AI environments.
Again, commerce got pushed further down the roadmap.
So was AI commerce fake? No. Not even close. I actually think AI-driven commerce eventually becomes one of the biggest platform shifts in internet history. But the timing assumptions were wrong because everyone underestimated how quickly enterprise AI spending would materialize.
The AI labs thought they might become the next Google. Instead, they accidentally discovered they could become the next Microsoft, Oracle, AWS, and Accenture first.
Commerce is still coming. We are just in the infrastructure monetization phase of AI before the transactional phase fully arrives.
But if you run a commerce company, you should absolutely be preparing now. Because when AI agents do become trusted purchasing layers, distribution will change very quickly. If you run an e-commerce brand, here are the 5 things to do as soon as possible.
If you’re on Shopify, enable agentic commerce capabilities now.
Shopify is clearly positioning itself for AI-native shopping and autonomous checkout flows. Most merchants still have not enabled the infrastructure that will allow AI agents to interact with their stores intelligently. Early movers here will have an advantage.
Follow Google’s emerging universal commerce protocols for AI.
Structured product data is going to matter enormously in an AI-commerce world. LLMs cannot reliably recommend products from messy catalogs and inconsistent metadata. Brands that adopt machine-readable commerce standards early will be significantly easier for AI systems to understand and transact against.
Optimize your website for LLM readability, not just Google SEO.
Traditional SEO optimized for ranking pages. AI optimization is about helping reasoning engines understand your product, trustworthiness, pricing, policies, compatibility, fulfillment quality, and customer experience. The brands that communicate clearly to machines will outperform companies still relying on old-school keyword tactics.
Optimize for frictionless checkout.
If AI agents eventually complete purchases autonomously, friction becomes conversion death. Reduce unnecessary checkout steps, remove account friction, optimize mobile performance, and enable one-click payment systems wherever possible. The easier your infrastructure is for both humans and machines, the better positioned you’ll be.
Build review volume and customer sentiment aggressively.
AI systems will heavily weight trust signals including reviews, sentiment, reliability, satisfaction, and return rates. In the next generation of commerce, reputation becomes distribution. Brands should treat customer feedback and post-purchase experience as core infrastructure, not marketing support.
The next era of commerce may not be won by the companies with the biggest advertising budgets. It may be won by the companies most understandable, trustworthy, and executable by machines.