Ser_

Doing some product ai engineering @ Qurable.

[Ser_ @mxldyn]

Taco Bell +8% comp. Wendy's -7.8%. Same quarter, same category.

Q1 2026 was the quarter the digital + loyalty gap stopped being invisible on the P&L.

Weather got the blame. The blame is structural.

[Ser_ @mxldyn]

Not CRM-with-AI. Post-CRM.

Bret Taylor just raised $950M at $15B for Sierra. 40% of the Fortune 50. $150M ARR in seven quarters. One in three of the world's largest banks.

The category arrived while the procurement docs were still being written.

[Ser_ @mxldyn]

@Vivek4real_ Salesforce's moat isn't decades of bug fixes. It's a UI humans learned to live with. When the calling user is an agent, the UI stops being the product. The "mission-critical platform" phrase is doing a lot of work in this take.

[Ser_ @mxldyn]

@levie AI didn't replace the engineers. It deleted the "is this worth building?" filter. Things that died in roadmap review now ship. The bottleneck is taste, not throughput.

[Ser_ @mxldyn]

@btaylor The reasoning across brands is the easy part. The hard part is that each brand's loyalty rules fire on the same transaction stream — the agent has to pick the right ruleset at the moment of swipe. Branded cards aren't UI skin. The brand IS the rule set.

[Ser_ @mxldyn]

The bottega lineage answers it. Ive wasn't Pippen to Jobs's Jordan — they were a workshop, and a workshop's output looks the same whether the master or the senior apprentice ships it. The system was the talent. Apple post-Jobs got Ive-led product. Apple post-Ive lost the workshop.

[Ser_ @mxldyn]

I spent three years as a Senior PM at Mercado Libre. The Q1 2026 number that matters isn't the +49% revenue. It's the 85% of customer service running on agents.

85% AI handling at MELI's volume isn't a productivity proof point. It's the category-arrived signal for LATAM. The cohort that was still asking "will AI customer service work at scale" got an answer the size of Latin America.

And the pairing is the part most readers will miss. Same quarter as the 85%, revenue grew 49% — fastest since Q2 2022. The framing isn't AI-as-cost-saver. It's AI-as-growth-engine. The agentic layer didn't replace the operators. It freed the operators to compound the rest of the business.

For every CMO and CPO looking at this from the outside: the question isn't whether to ship an AI-native CX layer. It's whether your data is shaped to make 85% structurally possible.

Most companies' isn't. That's the architecture problem this quarter just made visible.

[Ser_ @mxldyn]

For four years, Starbucks loyalty members were the only cohort transacting more.

Last quarter, the rest of the brand caught up.

The "loyalty as discount channel" frame doesn't survive that data. The program wasn't the cost. It was the floor.

[Ser_ @mxldyn]

If your loyalty members keep transacting and your non-members don't, that's not a problem with the loyalty program.

That's the program holding the brand up.

Starbucks just published four years of data on this. Q1 FY26 — for the first time since Q2 fiscal 2022, transaction growth came from members AND non-members. For nearly four straight years, the loyalty cohort was the only group transacting more. Through pandemic recovery. Through inflation. Through staffing crises.

The "discount channel" frame for loyalty programs was always backwards. The discount is the surface artifact. The cohort is the asset. The asset compounds; the artifact decays.

Most retail and QSR operators look at loyalty as a margin cost. Starbucks' last four years say it's the floor that kept the brand transacting through the softness — and the cohort whose return-to-growth signals the brand has cleared it.

The program led the recovery. The brand followed.

[Ser_ @mxldyn]

In B2B, costly signals aren't brand — they're integrations. Anyone can claim "AI-native." Not everyone runs millions of transactions a month through a single client's payment rail. The rail is the signal; the brand layer translates it. A startup before its first scaled tenant is Bell in the station.

[Ser_ @mxldyn]

@rauchg Every order of magnitude on inference cost moves a different category over the "callable per business event" line. 10× cheaper at top-of-eval moves the long tail — products that need an AI call on every transaction, not every session.

[Ser_ @mxldyn]

Token billing is rough for one class of product: pure assistants — where every inference burns to look helpful with no business event behind the call. The apps that survive bind every inference to a transaction, a ticket deflection, a fidelity event. Something with its own revenue.

[Ser_ @mxldyn]

Two classes of hard in software. One scales with code volume — compilers, deploys, refactors. AI moves the line here. The other scales with judgment under load — what the system should do this second, for which user, with what bounded authority. AI multiplies the surface there. GitHub's hard is mostly the second class.

[Ser_ @mxldyn]

@martinvars Holds at the deployment layer too. The moat in shipping AI to production isn't the model — it's the operator who knows where the agent goes, where the human stays, and which 1% precision-loss kills user trust. Klarna's walk-back wasn't a model failure. It was a placement failure.

[Ser_ @mxldyn]

The Italian word bottega doesn't mean workshop. It means a place where mastery transfers through proximity. The bottega never scaled. Most things worth doing don't.

[Ser_ @mxldyn]

The hardest engineering problem in loyalty isn't loyalty. It's getting the loyalty engine to fire inside the 200 milliseconds between authorization and settlement, every single time, without becoming the reason the transaction failed.

[Ser_ @mxldyn]

AI replaces the easy half of a job. What's left exposes how much of the rest was judgment nobody priced in.

[Ser_ @mxldyn]

The agentic commerce stack just locked in.

Four open protocols. Each ratified by serious infrastructure in the same window:

- MCP (Anthropic) — agents calling tools
- A2A (Google) — agents talking to agents
- UCP (Google + Shopify) — agents transacting with merchants
- AP2 (Google) — agents paying under bounded authority

The endorsers tell the whole story. On the payments side: Visa, Mastercard, Amex, Stripe, Adyen. On the merchant side: Walmart, Target, Home Depot, Macy's, Best Buy, Etsy, Wayfair. The two sides of the commerce balance sheet ratified the same standard inside a single news cycle.

Two things follow.

First, protocols stop being a differentiator. They become infrastructure, the way REST stopped being a differentiator around 2010. The retailer who ships UCP and AP2 cleanly gets parity with the rest of the market. Nothing more.

Second, the competitive rent moves up the stack. The retailer who knows what to do with the agentic transaction once it lands — which customer to recognize across surfaces, which reward to fire at the moment of payment, which signal to capture about an agent-driven purchase that a human never sees — wins the next decade of margin per customer.

Every agentic purchase is a customer telling you exactly what they want, what they decided to spend, and what their bounded authority is. That's the cleanest behavioral signal in the history of commerce.

Most retailers are about to receive it and throw it away.

That gap is the next category split.

[Ser_ @mxldyn]

Three exits from the 2019 neobank cohort. One pattern.

Brex sold to Capital One for $5.15B.
Ramp pivoted to AI spend management.
Mercury hit $5.2B valuation — four years profitable, conditional bank charter approved.

Different exits. Same pre-condition: profitable + AI-pivoted + engineering-first.

The campaign-CRM cohort isn't on the list. Not because marketing failed them — because their architecture was downstream of the marketing function instead of the other way around. When growth costs rose and AI subsidies receded, the gap between the two architectures showed up as the difference between an exit and a wind-down.

This is the LATAM corollary I keep watching for. Nubank, Ualá, Macro, Santander LatAm — each at a different point on the same axis. The ones that built their data layer to be callable by an agent will compound. The ones running campaigns on top of a CDP-stitched-onto-a-data-warehouse will keep paying the integration tax.

When a class graduates, you find out what was load-bearing. For the 2019 neobanks, it was the architecture. The marketing was the consequence, not the cause.

[Ser_ @mxldyn]

@edzitron The Uber finding isn't about AI cost. It's about AI architecture. Bolting AI onto a stack designed for humans clicking dashboards accelerates analysis but doesn't move unit cost. The companies showing ROI rebuilt the surface, not the model.