Rethinking Agentic Commerce // BRXND Dispatch vol 105
On where AI drives true value in shopping, Anthropic "destroying demand" and a busy week at OpenAI. Plus, a call for guest submissions!
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New guy alert! Mike Mallazzo here, coming to you live from day 5 on the job. I’ll be writing and editing this newsletter alongside Noah and Claire going forward, and I’d love to meet you all. I’ve been working and writing at the intersection of AI, brands, and commerce my entire career, and am excited to now do that here.
To that end, drop me a note and say hey at mike@brxnd.ai, or find me on LinkedIn or X. I’ve also included a call below to pitch your most audacious and contrarian takes on marketing + AI. Looking forward to what comes back!
This issue contains an essay on the agentic commerce zeitgeist, a busy week at OpenAI, and a wide-ranging roundup of AI news.
Agentic Commerce at the Crossroads
“Amazon solved buying, but it killed shopping in the process”- Emily Weiss, Glossier (2019)
“The internet has thus far meant you can buy anything that you can in New York City, but not shop the way you can in New York City,”- Benedict Evans (2018)
This week, OpenAI shared its revamped vision for shopping after walking away from native checkout. In this new model, humans buy things, and merchants maintain control of the purchase flow, throwing the concept of agentic commerce into some amount of uncertainty.
So what is agentic commerce, really? Best I can parse it, “agentic commerce” is an umbrella term for three related but distinct ways that AI could transform shopping:
An agent completing a transaction on a user’s behalf, soup to nuts…and eventually, proactively anticipating their needs
Agents replacing conventional software to orchestrate a set of backend processes that result in more relevant products and offers being presented to shoppers, based on the context of their session and known affinities
Large language models enabling an answer engine to provide bang-on responses to complex semantic search queries for a shopper.
In their 2025 annual letter, Stripe uses a slightly different framework to describe the five levels of agentic commerce, positing that we are currently between levels 1 and 2.
Level 1 — Eliminating web forms: You research and decide what to buy. The agent fills out your payment and shipping details and comes back with the confirmation. The system isn’t making any decisions; it’s just typing and clicking “buy” on your behalf.
Level 2 — Descriptive search: You stop searching for products and start describing situations — “back-to-school supplies for a third grader who likes tennis, nothing itchy.” The system reasons across weather, materials, sizes, reviews, and delivery timelines. Keyword search is no longer a thing.
Level 3 — Persistence: You stop reintroducing yourself. The system already knows your preferences and budget from previous conversations and purchases. You’re still deciding what to buy, but choosing from options that already reflect your taste.
Level 4 — Delegation: You stop choosing altogether. “Get the back-to-school shopping done. Keep it under $400.” The system handles search, evaluation, and purchase. You only set the budget. This is what most people mean today when they talk about agentic commerce.
Level 5 — Anticipation: There is no prompt. The system already knows the school calendar, your preferences, and your typical budget. You simply receive a notification: here’s what’s been purchased. Things you need show up before you have to ask.
Stripe envisions a world where shoppers will ultimately delegate more and more of their purchasing decisions to agents. This is technically possible—the models are getting better in ways that we can’t conceive of yet, and the protocols are all there to share the requisite data. But is it chasing the right utopia? While there’s certainly room for innovation at the margins in transactions, for the most part, buying is a pretty well-solved problem on the internet thanks to Amazon’s Buy Now and ShopPay. In Stripe’s anticipation example above, all agentic commerce is ultimately doing is harvesting existing demand.
The best version of agentic commerce is about creating demand rather than harvesting it.
The real magic (and real money) won’t be made in further accelerating convenience but in surfacing brands and products people wanted before they knew they wanted them. In other words, shopping. Call it discovery or demand generation or serendipity, but the bottom line is the thing that worked about going to the store is looking at the shelves, inspired purchases you didn’t know you wanted, and this is still the weakest part of ecommerce today. Jeff Bezos’s oft-cited 1997 letter to shareholders lays this out perfectly with its money quip:
“Today online commerce saves customers money and precious time. Tomorrow, through personalization, it will accelerate the very process of discovery.”
It’s still the top of the first inning for building online commerce experiences that accelerate discovery. And it is in making shopping better for humans where agentic commerce becomes a far more beautiful and lucrative problem.
The models can now parse complex semantic queries. The protocols can now connect consumer surfaces to business backends in a safe and secure way. What’s been missing to date is a heavy dose of panache, audacity, and first-principles thinking in designing new front-end interfaces for AI. Nobody with a semblance of imagination believes that a rectangle search bar and SKUs in tidy squares is the best we can do. The model makers themselves are behind in this regard as well: just see how many shopping queries come back with walls of text or simple static images with nothing but price and retailer.
That might be fine in highly thought-through categories, but what about clothes, furniture, or even CPG, where we know consumers make decisions based on what they see and feel? In a previous era of commerce, we defaulted to simple heuristics like keywords, user reviews and price because that’s all the technology could support. But LLMs provide the capability to effectively connect a SKU to the entire social zeitgeist around it: every expert review, angry Reddit post or trending creator unboxing. The best shopping experiences built with AI will look more and more like media.
Far too much of the agentic commerce zeitgeist today seems utterly mystified by the notion that we actually like the act of shopping. Maybe the parking lot sucks, and they don’t have your size, but sometimes you just want to be inspired. (As an aside, this is the fundamental bull case for Meta, which has cracked this with an ad product better than any company in history.) There are downsides to having hyper-rationalist accelerationists build technology for something so viscerally emotional and irrational. What you buy is ultimately an expression of self.
Recall Stripe’s “level 5 agentic commerce” back-to-school example presented above. Among my earliest memories is walking through the aisles of Staples as a five-year-old with my dad, diligently working through my kindergarten shopping list, insisting that I NEEDED a Cerulean Jansport that was twice my size.
I have zero desire for AI to rob me of that upcoming seminal moment with my daughter. The better question is, how can it augment it?
A busy week of OpenAI News
OpenAI acquired Astral — the company behind uv and Ruff, the Python tooling that’s become essential infrastructure for a lot of developers. The Astral team folds into Codex. Anthropic is going all-in on JavaScript; OpenAI is going all-in on Python. The irony is that all of Astral’s tools are written in Rust.
They also hired Dave Dugan, former VP of Global Clients at Meta, as VP of Global Ad Solutions. Sam Altman once said ads were “a last resort” and “sort of uniquely unsettling to me.” The ad team is now almost entirely Meta alumni, executing roughly the playbook Ben Thompson laid out: ads informed by the underlying prompt, optimizing for serendipitous discovery rather than high-intent keyword matching. Bizarrely, OpenAI’s initial ads pilot isn’t really brand building or product discovery at all; it’s a poor attempt at intent-based direct response.
Finally, the NYT coined “tokenmaxxing“ — the status game inside tech companies where employees compete on leaderboards that track token consumption. One OpenAI engineer processed 210 billion tokens in a single week. One Anthropic customer ran up a $150,000 Claude Code bill in a month. Meta and Shopify now factor AI usage volume into performance reviews. Goodhart’s Law says all measures will eventually be gamed, but token maximalism is definitely the only option.
Destroying demand
James Gross wrote my favorite piece of the week, an opus (pun mildly intended) on everything from why Claude seems to suddenly have a cold to why Microsoft lost $357B in market cap.
Gross argues that AI has the traditional bubble narrative exactly backwards: rather than the traditional bubble where supply races ahead of speculative demand, AI is an “anti-bubble” where foundation models don’t have enough compute to meet demand. As a result, foundation models throttle customers in the hope of destroying the insatiable demand we have for intelligence.
Read the piece to learn why you may want to upgrade that iPhone now.
Quick hits
Claude’s Dispatch + computer use feature launched as a research preview for Pro and Max subscribers on macOS — Claude can now control your mouse, keyboard, and browser to complete tasks. I asked it to buy outdoor furniture, but it declined to add items to my cart.
Anthropic launched the Claude Partner Network — a formal program for organizations helping enterprises adopt Claude.
The holding company era may be ending, per Marketing Brew. No one wants to be WPP anymore.
The NYT Magazine ran a long piece on Silicon Valley programmers who are “now barely programming. Instead, what they’re doing is deeply, deeply weird.” Worth the time.
Travel stocks have been hammered on fears that AI agents will disintermediate booking. Rafat Ali’s breakdown: Hilton is returning 150% of free cash flow through buybacks while spending ~2% on tech. Airbnb is the only travel company investing like a tech company.
Gartner is telling CMOs to double their PR budgets by 2027 since 94% of AI citations come from non-paid sources. One commenter noted the irony: much of the “earned media” driving AEO success is paid placement that LLMs just don’t know about yet.
The Great Turnover: 9 in 10 companies plan to hire in 2026; 6 in 10 also plan layoffs. Hire and fire at the same time.
Meta delayed their Avocado model until at least May after it underperformed on internal evals, and the company is reportedly considering licensing Gemini as a stopgap.
Meta also announced new product discovery and creator affiliate tools, in yet another attempt at facilitating native commerce. On the surface, it’s a bit odd for Meta—arguably the most successful ads business in history—to pursue far lower yield affiliate deals. But I suspect this is a shrewd move to ultimately capture more retail media dollars. Walled garden retail media players are existentially dependent on creators for growth, and if those creators come to Meta, guess who has all the leverage.
Pitch me!
As I come on board here at BRXND, one of my major goals is to seek perspective from a diverse and bold group of thinkers on the frontier of brand building or AI. To that end, I’d love to start running more guest columns here in the newsletter.
Feel free to send me polished pieces or half-baked notions at mike@brxnd.ai that are living rent-free in your head. If there’s a kernel of a bold take there, we’ll turn it into something great together :)
If you have any questions, please be in touch. As always, thanks for reading.
— Mike