This week, OpenAI’s Codex unveiled some helpful new features, including one called “Record & Replay” which uses computer use models to record a task and save it as a re-usable skill. We’ll take a closer look at some of the ways you might want to experiment with it.

Plus, Square reveals how its new coding agent shipped an entire backlog in just a few days and how LinkedIn’s new connectors feature can help you demonstrate the work you actually do day to day to potential future employers.

Happy Friday and have a great weekend!

Rich

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Key reads and resources for product teams

What developers can build on Shopify’s Catalog API and UCP
As part of the Spring ‘26 Edition, Shopify designers and developers built five shopping apps in a few days. Each running on Catalog API for product discovery and Universal Commerce Protocol for the full commerce journey, they show what’s possible for developers, designers, and anyone with an idea. See everything that launched. (Shopify Editions*)

How to use Agent Skills - an in-depth new guide from Google’s AI teams with practical examples
Agent Skills turn any general-purpose AI agent into a specialist on demand. Written by a specialist AI team at Google including Tanvi Singhal and Gabriela Hernandez Larios, this comprehensive guide explains why a simple folder with a markdown file has become the standard for equipping AI agents with procedural memory - and why 19% of poorly-designed skills actually degrade performance. Learn how to build, evaluate, and deploy skills in production, from structuring your first one to managing libraries of hundreds. (Google)

New on the Department of Product Substack this week:

How Coinbase built an AI Agent that converts Figma designs into production ready code
Learn more about how Coinbase cut feature development time from 16 days to 4 by building an AI Agent that converts Figma designs directly into production-ready code - slashing 80% of wasted handoff time between design and engineering.

Why Semantic Data Matters to Product Teams
Semantic data layers - a translation tool between raw data and business questions - are having a renaissance in 2026. Find out why - and how you can use them at work. (Department of Product)

How to make AI agents follow your design system
AI agents are great at learning from your codebase - which is a problem if your codebase is a mess. Alice Moore discovered this when an AI agent opened a pull request that looked perfect in screenshots but used deprecated components, hardcoded colors instead of design tokens, and failed accessibility checks. Learn how to constrain agent behavior through linting, strict types, and reference implementations so your code review focuses on what actually matters. (Builder IO)

What Atlassian learned building its own DESIGNmd file in production
Atlassian’s design team tested DESIGN.md, and found it works great for quick prototyping but can cost more tokens and produce less accurate results than their custom tools in production. (Atlassian)

Is Meta destroying its engineering organization?
Gergely Orosz investigates how leadership’s obsession with AI led to forced data-labeling assignments, keystroke tracking, mass departures, and a security breach that exposed high-profile Instagram accounts. (Pragmatic Engineer)

Google Deepmind leader on Agent Harnesses and the future of personal software
Logan Kilpatrick, who runs Google AI Studio and the Gemini API, explains why the agent harness - not the model API - is now the connective tissue across every Google product, why he thinks custom scaffolding has roughly 12 months of relevance before models absorb it, and what 350,000 Android apps built in a single week tells you about where personal software is heading. (Sequoia Capital)

*sponsored by Shopify Editions

New product features and innovation this week

Codex has launched a new feature called Record & Replay which lets users show Codex a workflow once and then reuse it later as a skill.

When you perform a task while Codex watches, it records your actions, identifies the pattern, and converts it into a reusable skill. The next time you invoke that skill with new inputs, Codex executes the entire workflow automatically without requiring detailed instructions.

The system works across multiple tools and interfaces. So, for example, Codex can interact with spreadsheets, web applications, browser tabs, and connected plugins - using whatever combination you demonstrated. It remembers not just the mechanical steps but your preferences and conventions.

For product teams, this new feature could be used for all sorts of workflows. Here’s some ideas on how you could put this to use:

• North star metric “morning brief” - record your daily routine: open GA4/Amplitude, select the product area segment, export key charts, grab experiment stats from your experiment platform, and update a Notion “metrics wall”. Then you can say “Generate this week’s metrics brief for Consumer app” and have it re‑run the exact workflow with a new date range.

• Cross‑platform consistency check - demonstrate how you compare the same flow across mobile, web, and desktop: open them side‑by‑side, perform key tasks, and make a structured list of deltas you accept vs. deltas you consider bugs. The replayable skill becomes your “consistency inspector” whenever platform teams diverge.

• Weekly “state of product” briefing - record how you: open your analytics dashboards, latest user research notes, experiment pipeline, and roadmap board; then compile a short status doc and send it via email/Slack. Turn this into “Prepare weekly product brief for [Team]” so the ritual survives even if a specific PM is out.

Codex has also launched a dedicated iOS plugin that makes it easier to build iOS apps end to end. It lets teams automate the repetitive parts of iOS development (boilerplate, build loops, validation) while keeping the process fast and auditable. You can run your app in a built-in browser without leaving Codex and it comes with a bunch of skills, including a “Liquid Glass expert”. OpenAI’s co-founder calls it a “much better way to build iOS apps” but we’ll let you be the judge of that one.

Square unveils “builderbot” - its latest AI coding orchestration layer; engineers shipped an entire backlog in days

Block has released notes on how it built Builderbot, an AI agent orchestration system designed to manage code changes across massive, multi-service codebases. It functions as a Slack-integrated development assistant that coordinates multiple AI agents to handle tasks ranging from bug fixes to cross-service migrations.

While typical AI coding tools work within single repositories, Builderbot maintains context across Block’s entire codebase - hundreds of millions of lines of code spanning hundreds of services. Engineers tag “@builderbot” in Slack with a request, and the system researches the relevant code, creates branches, writes implementations, opens pull requests, and iterates based on CI feedback.

Block says that the new coding bot “picks up tickets directly from Linear and Jira, creates the branch, writes the code, opens the pull request, watches CI, and iterates based on feedback”. On a practical level, the product teams at Square say that builderbot “took a list of features sellers had been waiting on for months” and “shipped them in days”.

Jack Dorsey calls it “the beginning of the beginning” of the company’s intelligence layer:

They also make the point that it was engineers, not product managers, who made the decisions that shaped the product in another example of how AI is eating into the traditional boundaries that used to separate roles in product development.

Claude Design’s new bidirectional syncing

Claude Design shipped some major updates worth knowing about this week. The first is a change to the components it creates to help bridge the gap between design and development. Product teams can now upload their actual component libraries - buttons, typography, color tokens, spacing rules - from GitHub, design files, or raw uploads. Claude then generates designs using only those components and auto-corrects output before showing it to users.

This is part of a new bidirectional syncing with Claude Code, which means that designers can sync their codebase’s actual design system into Claude Design, ensuring prototypes use real components.

The second is a redesigned editor which comes with some new layout controls. These let you drag, resize and align elements directly on the canvas, without having to rely on the conversational interface to make the changes.

You can also now ship from Claude Design into third parties like Replit.

Other updates worth knowing about

Pinterest has teased the launch of “Ask Pinterest,” an experimental standalone app that lets users discover products through natural language conversation instead of traditional visual search.

Pinterest built it as a separate application to test conversational shopping without disrupting the main app. For product teams, this experimental approach to launching new conversational features as separate standalone apps could be something we start to see more of.

Cursor says Bugbot is now over 3x faster, 22% cheaper, and finds 10% more bugs.

Their product roadmap leaked this week but here’s one feature for your resume: LinkedIn is launching “connected apps” - a feature that automatically generates verified descriptions of how you actually use supported software tools and displays them on your profile.

When you link apps like HubSpot, Adobe Express, or Github Copilot to your LinkedIn account, the platform analyzes your real usage patterns and creates specific statements like “creates and sends segmented email campaigns in HubSpot’s Marketing Hub” instead of generic skill tags.

This feels like a neat idea that’s helpful for proving real world skills to potential employers, but I wonder what the unintended consequences of this might be.

📈 Product data and trends

Amazon’s shopping assistant Rufus converts at double the rate of traditional shoppers with the average Rufus user spending 40 minutes before converting vs 15 minutes for non-Rufus users. This suggests that AI shopping assistants not only drive engagement but ultimately conversion, too.

The Rufus data on Assistants comes from the latest Sensor Tower report which includes a bunch of nuggets that might be of interest to product teams, including:

• ChatGPT’s market share has dropped below 50% for the first time - driven by

• Men are the predominant users of all leading AI apps except for character based apps like Character AI where women make up almost 70% of users

• Shopping and SaaS are the biggest categories for ChatGPT ad spend so far

Meta’s head of AI product for AI for Work Emily Dalton Smith is leaving the company. Their app is still quietly growing, though with downloads up 38% month on month.

49% of US adults now report using AI chatbots, including one in four who uses them on a daily basis according to new Pew Research.

Headcount growth at the most AI-exposed companies is outpacing that at the least AI-exposed companies according to new analysis by PWC:

Retool interviewed over 300 different CIOs, CTOs and CSOs and found that half of respondents discovered company data sitting on free-tier cloud services that nobody had ever approved:

93% of CTOs, CIO and CSOs are alarmed about vibe coded apps in their organization - and they’re more worried about the ones they don’t know about. New data from Retool who are relaunching with security as their differentiator.

Stanford launched a new series of reports designed to monitor the economic impact of AI that examines how AI is impacting both companies and individuals.

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Anthropic’s data team recently revealed that they built a self-service reporting system where 95% of its data requests are now automated with Claude.

The teams who worked on it say the biggest challenge when building this system was this:

In other words, the hard part wasn’t writing the SQL - it was figuring out exactly what data the user actually meant when they asked a question. And semantic data layers play a huge role in this.

A semantic data layer lets you formalize core product concepts (user, workspace, subscription, session, feature, experiment, etc.) and their relationships as a knowledge graph or semantic model. This model then sits as a “semantic layer” between raw data and tools, so everyone queries with the same business terms and metrics.

AI and AI agent tools with natural language searches have brought semantic data back to the top of the agenda and according to recent reports1, enterprise companies like Microsoft, Databricks, SAP and other AI software providers are all fighting over control and access to this semantic layer as one of the next major battlegrounds in the AI race.

In this Knowledge Series, we’ll explore what semantic data is, why it matters to product teams and how exactly you can put it to use for practical use cases like:

• Standardising how your company talks about important definitions

• Speeding up product discovery and concept exploration

• Building better in-product search features

• Making data analytics truly self-serve so that you don’t have to ask your data teams for custom reports (using Anthropic’s set up as inspiration)

Plus, we’ll look at a real world case study and experiment with using tools like Claude Code to create visual representations of your data that you can use to present your data model back to non-technical users.

What exactly is a Semantic Data Layer?

A semantic data layer is a translation layer that sits between your raw data warehouse and whoever (or whatever) is querying it. It takes raw table columns and turns them into named, governed business concepts.

Without one, “monthly active users” might mean five different things depending on which analyst wrote the SQL. With one, “monthly active users” is a single defined metric that always returns the same number no matter where you query it from. Anyone who has been tasked with producing a standardized report at any company can attest to just how difficult this is in companies with no pre-existing definitions set up.

A semantic data layer could store things like:

• “Activation” - the specific milestone that marks a user as activated, per your product’s definition

• “Feature adoption” - which specific events count, whether a single use counts or a threshold

• “Customer PII” - identified semantically, so that everyone has a shared understanding of personally identifiable information that can be redacted for privacy reasons

• “New user” - first 7 days, first 30, or first session only

(You can read more about SQL in previous Knowledge Series editions if you’re interested in that).

Why it matters now specifically

Semantic layers are having a bit of a renaissance in 2026 because precise definitions of data are essential for AI Agents and LLMs. Once definitions are set up, it allows non-technical parts of a business to self-serve analytics reports on demand through conversational interfaces - and it lets AI agents perform data and reporting tasks autonomously.

Not investing in a solid foundation including a semantic data layer can make it virtually impossible to build a self-serve analytics model. In Anthropic’s case, before investing in a semantic layer and complementary agent skills, analytics questions were stuck at around 21% accuracy on their evals. Investing in the infrastructure got these numbers consistently above 95% in aggregate and regularly around 99% in certain domains, meaning that their data science team can focus on more strategic work.

How a semantic layer actually works - and how to practically use them in product

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The hand off process between design and engineering has historically been one of the most time consuming; a designer finesses a bunch of screens in tools like Figma and hands it over to an engineer who takes the design and rebuilds it in production code.

According to Coinbase, this step was eating up a massive 80% of the delivery time for its product teams. And so they decided to do something about it by building an AI Agent that converts Figma designs into production-ready code. The result of this was that the development of a new feature that would typically take around 16 days was reduced to just 4:

AI agents are transforming the product design to development handoff process and in this Deep Dive, we’re going to take a closer look at what exactly Coinbase built with a step by step breakdown of how it works in more detail. Then, we’ll extract some of the core principles from this set up and explore how you can build your own version of the same thing.

Coinbase isn’t the only company doing this, of course, and so we’ll finish with some other examples of similar design to coding internal Agents and features from other leading companies that you can use as inspiration for your own processes.

Coming up:

• A deeper look at what Coinbase built with their Figma to Code Agent

• How to build your own version of this with prompts and templates you can use

• Examples of similar agents from other companies including Monday, Uber and more

What Coinbase built and why

Coinbase built an AI agent that converts Figma designs into production-ready code. According to their Senior PM Marvin Mathew, this process of translating a design file into working software was eating roughly 80% of delivery time on their internal tooling platform. A typical backend domain with 8-12 endpoints took one to two weeks. The frontend was another one to two weeks on top of that.

The agent they built cut that down dramatically. And their first pilot shipped a feature in four days that would normally take two to four weeks. Here’s a snapshot of what it is and how it works:

The Coinbase PM and engineer who worked on it were thoughtful in their selection of this use case as a potential opportunity for AI agents vs other internal use cases.

Translating a Figma design into code is largely mechanical: you map components, match layouts, wire up data fetching, write tests, and check everything against the team’s bar before it can ship. This takes time and attention, but it doesn’t really need an engineer’s judgment, making it a solid use case for offloading onto an AI Agent.

How it works - and how you can build your own version of this Agent

The system has a foundation with three core parts: a reference implementation, a rules document, and prompt templates - and they work together in a specific sequence.

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This week, Fable 5 arrived as a safety-tuned version of the powerful Claude Mythos model. We’ll take a look at what people are building with it so far.

Plus, while Apple’s WWDC was a pretty low key affair this year, the company’s talks on product design are always worth a look. And are self-improving products really the future of product development? Amplitude is giving the idea a shot with its latest release.

Have a great weekend!

Rich

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Key reads and resources for product teams

New Agentic payment and features explored for product teams
AI agents are becoming an economic force on the internet, but the payment infrastructure they need looks nothing like the checkout pages built for humans. This deep dive breaks down the 5 layers of agentic payments emerging in 2026: from protocols like x402 and ACP that let agents discover and pay for products, to new credential systems, identity verification, usage-based billing, and settlement rails. Here’s what product teams need to know about these shifts right now - and why consumers aren’t quite ready yet. (Department of Product)

Unexpected lessons from an AI-assisted prototype experiment
Adobe’s Veronica Peitong Chen shares surprising lessons from an eight-day experiment using AI-assisted prototyping on the Firefly team. (Adobe)

Are Lean Startup principles still relevant? An Ask me Anything with Lean Startup author Eric Ries
Eric Ries, author of The Lean Startup, is back with Incorruptible, exploring why good companies drift from their missions, not through evil intent, but through “financial gravity”. He hosted an AMA on Hackernews to discuss AI and whether lean startup principles are still relevant.

Why you need a point of view in the Age of AI
AI has flooded the market with information, but buyers want something simpler: your honest take on the future. April Dunford, a positioning strategist, examines how companies like OpenAI, Microsoft, and ServiceNow are winning by staking clear claims about what comes next. (Substack)

Design systems are over. Product context is now the work.
Design systems as we know them are ending - and that’s actually good news. Robin Cannon argues the real work isn’t building component libraries anymore; it’s codifying product context that AI needs to maintain coherence at scale. (Substack)

How Anthropic build self-service data analytics with Claude
Anthropic’s data team automates 95% of analytics queries with Claude - but, pointing an AI at your data warehouse without proper safeguards often produces confidently wrong answers. This post reveals how they structured their system to avoid three critical failure modes, plus the exact playbook they use to maintain 95% accuracy as their data model evolves. (Anthropic)

The PMs playbook for shipping AI features that actually work in production
Gaurav Savla, an experienced PM, shares hard-won lessons on shipping AI features that actually survive production. (O’Reilly)

Updates from Apple’s WWDC

This week, Apple held its annual WWDC and the big announcement was of course, the revamped version of Siri which now comes with a dedicated mobile app and the ability to swap models. There were no major updates to iOS, with Apple taking the opportunity to ship some refinements including the ability to configure the transparency settings on Liquid Glass. These smaller quality of life improvements are, in all honesty, quite refreshing to see at a time when most companies seem to be chasing the next big thing.

Aside from the product announcements, each year, Apple also publishes a series of development and design workshops and here’s one that might be of interest. In this talk, Apple’s design evangelists Linda and Doug reveal what great design actually means. They break down seven core principles for building products people truly love, from respecting privacy and giving users control to crafting experiences with meticulous attention to detail:

How to apply Apple’s design principles to your own product

In the AI library this week, I’ve taken these principles and created a series of tools you can use including:

• A mini app that runs assessments live - describe your feature, pick your platform and audience, and it scores each principle, renders a radar chart, and tracks your progress across iterations so you can see whether your design is actually improving

• A Claude Code Skill that turns Claude into a structured design reviewer based on Apple’s principles - drop it into your skills folder and Claude will know when to invoke it, what context to gather, and how to score across all seven principles

• A system prompt you can use anywhere: a Claude.ai project, a direct API call, or any custom integration you’re building

New product features and innovation this week

After months of hype (and fears) around Claude’s new Mythos model, this week Anthropic released a safer version of it called Fable. Fable 5 uses an AI-based classifier system that detects risky requests in three areas - cybersecurity, biology/chemistry, and model distillation - and automatically routes those queries to Claude Opus 4.8 instead.

This fallback approach keeps the model safe while still delivering capable responses on restricted topics. The safeguards trigger in less than 5% of sessions. Mythos 5 is the same underlying model but with these guardrails removed for specific use cases: cybersecurity professionals get unrestricted access through Project Glasswing, while biomedical researchers can access biology capabilities without those guardrails specifically.

For product teams, this matters because the fallback architecture lets you deploy frontier capabilities safely since the classifier acts as a safety valve rather than a hard block, improving user experience on benign requests while preventing misuse.

How people are using Claude’s new Fable model

Tech debt and code migration - software engineering teams can use Fable 5 for large-scale codebase refactoring and migrations that would otherwise take months. Anthropic says that Fable 5 compressed months of engineering work into days for Stripe’s codebase migration. In a 50-million-line Ruby codebase, the model performed a codebase-wide migration in a single day. There are scant details about exactly how this worked but if this is replicable, it could transform how product teams approach tech debt management and code migrations.

Technical API architectural diagrams - a single shot technical architectural diagram that visualises API architecture using the OpenAPI spec.

Building games - this example shows a minecraft roller coaster game that was built in a single shot and here’s a table tennis game. If making games becomes easier, could we see more consumer products include them as additional features or in content marketing as we’ve seen across products like Netflix and LinkedIn?

Video editing - Anthropic’s own tech lead used Fable 5 to edit his own video. Here are the results, packaged into a presentation deck. To create the video, he says used “a lot of code and tool calls, transcription services, Figma and others.

Landing pages - a hyper-polished landing page for a SaaS company with liquid glass style components.

If you want to get hands on, Anthropic has published a handy guide on how to prompt with Fable 5 effectively, which could be useful for product teams.

NotebookLM gets its own coding abilities, Analytics can track AI Assistants

Google has announced some powerful new capabilities to NotebookLM which give it a set of coding abilities. Powered by Antigravity, NotebookLM can spin up a sandbox environment, write scripts, and execute them to support deeper analysis (e.g., data cleaning, statistical comparisons, simulations) without you leaving the UI.

Google says NotebookLM will come with a set of more than 100 software skills that can help you build workflows with your notebooks that previously would have required you to jump between apps. For product teams, this could be useful for things like processing datasets, creating visualizations, or performing calculations directly rather than exporting data elsewhere.

Google Analytics is also getting some important new updates. Starting this week, GA4 will get its own AI Assistant traffic measurements which lets you track and trend human traffic from top AI assistants including Gemini, ChatGPT and AI Overviews directly in reports.

OpenAI, Visa and Mastercard unveil more agentic payment abilities

Visa and OpenAI have announced a partnership which sees Visa’s payment network directly into OpenAI’s AI agents, allowing these agents to initiate secure transactions on behalf of users. When an AI agent needs to complete a purchase, it uses tokenized Visa credentials (encrypted payment information) rather than raw card data.

Mastercard also launched new agentic capabilities this week. Agent Pay for Machines (AP4M) is Mastercard’s infrastructure for high-speed, automated payments between AI agents and services. Instead of traditional person-initiated transactions, AP4M enables agents to execute continuous, programmatic payments - including tiny fractions of a cent - at machine speed.

And Coinbase announced the AI agent lets users automate cryptocurrency trades and research payments without manual intervention. Users connect the agent to their account - or isolate it in a sandbox for safety - and give it natural language instructions like “rebalance my portfolio” or “execute this trade.”

Other updates worth knowing about

The idea of “self-improving products” is starting to gain traction - thanks to increasingly powerful context and semantic data labelling. This week, Amplitude released Wave, an AI agent that automates the full product improvement cycle - from identifying what to build to measuring whether it worked.

Wave continuously ingests signals from your product: user clicks, session replays, customer feedback, error logs, and AI agent traces. It analyzes this data to surface high-confidence opportunities for improvement, then orchestrates the work (coordinating with human teams and coding agents) to ship those changes. After shipping, Wave monitors outcomes through experiments and metrics, feeds those learnings back into the system, and uses them to sharpen the next cycle.

DoorDash has released a new AI Assistant that lets users order through voice, chat, recipe link or image. Their CEO says that in early testing, nearly half of all restaurant orders made with Ask DoorDash were from a place the customer had never ordered from before, and grocery baskets built with Ask DoorDash were over 35% larger than those without. But did those grocery baskets actually convert?

OpenIAI’s Codex now has full access to developer tools to help you debug your product.

📈 Product data and trends

Lovable has published its first ever Build Economy report which shares who is building software and what they’re building. 80% of Lovable builders are non-technical, as you might expect, with product managers sat in third place - above designers who come in at number 8:

Other noteworthy nuggets from the report:

• Men account for 82% of all users

• 60.5% of builders haven’t made any money from their products built in Lovable

• The US accounts for 25% of activity, followed by Brazil, the UK, the Netherlands, and Germany

Google’s tech staffer Logan Kilpatrick says the company is generating more than 1.2 million apps per week - with more than 18 million created since late February.

The annual study from Harvard Business Review of 12,637 AI use cases shows people are increasingly turning to generative AI for therapy, companionship, and relationship advice rather than work optimization. Marc Zao-Sanders and Sara Biuk’s third annual “AI in the Wild” report analyzed nearly 50,000 records collected from Reddit, LinkedIn, TikTok, YouTube, and other sources between March 2025 and February 2026.

The findings show an increase in emotion-related activities - therapy and companionship climbed to 11% of all use cases, up from 5% the prior year.

The AI dinner prompt is here. 22% of consumers have used an AI tool like ChatGPT or Google Gemini to help choose a restaurant, according to new DoorDash data. Three-quarters of consumers say they’re comfortable using AI for reservations, but only 28% of operators are using AI to manage calls and customer service

New research from Atlassian shows 94% of knowledge workers use AI regularly, but disclosing that fact costs them professionally. In a controlled experiment with 961 workers, colleagues rated peers who admitted to using AI as 10 times lazier - even when evaluating identical work. Those who disclosed AI use were also 24 percentage points less likely to be recommended for high-visibility projects.

In just two years, Google searches ending without a click have jumped from 60% to 68%. Paid clicks are growing but organic clicks to the open web collapsed by 9.5 points, which is bad news for product teams reliant on organic search.

Curiously, the biggest change in user behaviour is what they do after performing a search. The percentage of people who performed another search after their initial search has grown from 29% in 2024 to 36% in 2026; perhaps driven by conversational UX making it more common for users to start a chat rather than perform a one-off search.

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She says that over the last six months, agents have become a new kind of actor on the internet; while ecommerce was built for humans who click pricing pages, enter cards and navigate checkout flows, agents don’t do any of that. Instead, they need a programmatic way to understand a product, including what it costs and how to pay - without manually clicking through a product’s UI in the way a human would.

This rapid evolution of payments in recent months has led to new technologies, protocols and features that could transform our understanding of payments on the web.

And in this DoP deep dive, we’ll take a closer look at some of these new advances in payment abilities and features from a product perspective so that you’re fully up to speed with how these technologies might impact the future of in-product payments.

Coming up:

• The 5 layers of the agentic payment stack explored - new payment technologies, features and protocols explained

  • Protocols - how agents discover your product and understand how to pay for it

  • Credentials and wallets - how agents pay without holding raw card credentials

  • Identity and trust - how agents prove they are legitimate and that a human authorised the transaction

  • Billing and monetisation - how businesses charge for consumption rather than seats

  • Settlement rails - how money moves at the speed and scale agents actually require

• Considerations for product teams - what product teams need to know about these new agentic payment technologies and features

• Do users care? Consumer and business attitudes towards agentic payments with deep dive reports for further reading

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The 5 layers of Agentic Payments: making sense of new technologies, features and protocols

Here’s a snapshot of the 5 layers of the agentic payments stack which have seen the most disruption during the past year:

In this Deep Dive, we’ll use these layers to contextualize each of the new technologies and features that have been released in recent months in more detail, explaining what impact they potentially have on product teams.

A timeline of the most impactful Agentic Payment launches from the past 12 months

But before we dig into the details on how some of these new technologies work, here’s a summary of some of the most important agentic payment related releases from the past 12 months.

June 2025 - Adyen launches its MCP server in alpha, becoming the first major European payment processor to let AI agents call payment APIs directly using plain language instructions.

September 2025 - Google announces AP2. The first mechanism for proving a real human authorised a purchase - the human signs a spending mandate in advance, the agent presents it at checkout, and it can’t be disputed after the fact. Separately, OpenAI and Stripe ship ACP inside ChatGPT - the first production protocol for completing purchases inside a conversation.

October 2025 - Stripe launches Shared Payment Tokens: scoped credentials that give agents payment access without exposing the underlying card, capped by merchant and amount, and revocable at any time. Mastercard Agent Pay goes live. The CEO confirms on the earnings call: “our first agentic transaction took place on our network this quarter.” Visa launches Intelligent Commerce in pilot.

You can read more about the Agent Commerce Protocol and how Stripe’s Shared Payment tokens work here.

December 2025 - Stripe launches its Agentic Commerce Suite. x402 V2 releases after processing over 100 million payments in six months, adding multi-chain support and auto-discovery.

January 2026 - Google and Shopify launch UCP with 60+ backers - agents and merchants negotiate transaction terms in real time. Google deploys it in AI Mode on Search and Gemini. Mastercard announces the Agent Suite.

February 2026 - OpenAI launches “Buy it in ChatGPT” for all US users. Etsy is live at launch, with over one million Shopify merchants in the pipeline. OpenAI charges a 4% fee on autonomous agent-led conversions.

March 2026 - Stripe and Tempo co-launch Machine Payments Protocol (MPP) - a standard for how agents pay over HTTP: server returns a price, agent pays, resource is delivered. OpenAI, Anthropic, Google Gemini, and Dune Analytics adopt it at launch.

April 2026 - x402 moves to the Linux Foundation, backed by Stripe, Google, Microsoft, Mastercard, Visa, AWS, Cloudflare, Shopify, Circle, and the Solana Foundation. Stripe Sessions ships 288 products including Link agent wallet and streaming payments in Preview. Visa launches Intelligent Commerce Connect and expands its Agentic Ready program to Asia Pacific and Latin America.

May 2026 - Three major agent payment announcements in 30 days. On May 5th, Google Cloud and Solana launched Pay.sh. May 7th Amazon launched AgentCore Payments in Preview and on May 11th, Circle launched Agent Stack including Nanopayments. All three independently chose x402 and USDC - we’ll talk more about the role of both x402 and stablecoins later.

The 5 layers of Agentic Payment Abilities and Features Explored

Now let’s take a look at each of the 5 layers in more detail to put some of these new technologies into context for product teams.

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This week, OpenAI unveiled its strategy for Codex as it seeks to expand beyond coding and into other use cases like design and data analytics. We’ll take a closer look at what exactly they announced, examples of how you can use it in practice, and what it means for OpenAI’s strategy.

Plus, new data reveals how Spotify’s divisive disco ball App icon caused a huge backlash with users and we’ll cover some of the essential highlights from Microsoft’s Build event including a product that uses the latest trend in product design of “just in time UI”.

Happy Friday!

Rich

Watch on YouTube | Follow on Substack Notes

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Key reads and resources for product teams

New from the Department of Product Substack this week:

How to use Claude Routines - a practical guide for product teams
Claude Routines lets you put AI on autopilot to handle repetitive product work - without managing infrastructure yourself. Learn five practical routines product teams can set up today: weekly backlog triage, API deprecation tracking, competitor monitoring, release notes drafting, and user feedback aggregation.

Transform your docs into HTML-based presentations with animations you can play in the browser
Despite markdown being the go-to format for most AI related docs, Anthropic’s tech lead says he has a new favorite format: HTML. This guide takes Anthropic’s tech lead’s HTML presentation and reverse engineers it into a prompt you can use to build your own single page presentation with animations you can play in the browser. (Department of Product)

How to improve your executive presence as a product leader
On r/ProductManagement, dozens of experienced leaders share what actually moved the needle for them: leading with decisions and trade-offs, tailoring for your audience, and practicing ruthless clarity. (Reddit)

How to get Claude Code to check its own work before handing it back to you
How do you get Claude Code to check its own work before handing it back? Watch how you can encode your manual checks so Claude closes its own feedback loop. (Anthropic)

How Atlassian cut up to 80% of Engineering “Chores” using AI Agents
Atlassian’s Jira engineering team cut up to 80% of time spent on repetitive maintenance tasks by using AI agents to handle the “chores” nobody wants to do - from fixing flaky tests to cleaning up stale feature flags. Written by Arnaud Moret and WaiYee Loo, Principal and Senior Engineers at Jira. (Atlassian)

How to make your Design System AI-Ready
Vitaly Friedman shares a practical guide on structuring spec files, auditing with FigmaLint, and layering tokens so AI actually has the guidance it needs to generate quality work. (Smashing Magazine)

Google’s VP of Search on AI Mode and the future of links
Robby Stein is VP of Product at Google Search, where he leads AI Overviews, AI Mode, search ranking, and Google Lens. Before Google, he built Stories, Reels, and Close Friends at Instagram – products now used by billions. He also co-founded Stamped (acquired by Yahoo) and led product at Artifact. (YouTube)

Is “voice in, visuals out” the ideal pattern for product design?
OpenAI’s cofounder thinks so, but is he right? Allen Pike explains why talking to AI while it shows you things feels natural, but why most voice assistants feel sluggish and dumb.

New product features and innovation this week

This week, OpenAI announced some major new updates to its AI coding agent, Codex, positioning it as a tool designed to be used by everyone, not just engineers. And the data seems to back this up (more on that later).

The announcement centers around 3 new features: role-specific plugins, Sites and Annotations.

Role-specific plugins can be installed from the Codex plugin directory and are designed to help with niche use cases. For product teams, the most relevant plugins are probably the design plugin and data analytics plugin. The design plugin is built for turning early ideas into prototypes that teams can review in third party tools like Figma. It also incorporates some new self-testing features; before handing over the prototype, the model tests it itself - checking different screen dimensions and comparing the output against the reference image to validate fidelity.

The analytics plugin lets you query your data and then turn it into shareable assets like Google Slides. In this example, you can see how the data analytics plugin can be used to query a data warehouse in natural language and create a deck from it.

Sites are essentially a formal name for the types of mini apps that many people were already building with coding agents at work anyway. The difference here is that they now come shipped with a secure, workspace-specific URL out of the box, making it easier for teams to share their mini apps with each other.

Here’s an example OpenAI shared of a product launcher mini app built in Sites:

UI-wise, it does look a little hectic with the visual hierarchy all over the place but I’m sure this can be resolved in a few prompts. OpenAI also says that the Sites you create are not static and can be hooked up to external sources and kept up to date automatically in Codex.

All of this, of course, is OpenAI’s latest attempt to position itself as a serious enterprise player - and it’s worth noting that the entire launch materials don’t mention ChatGPT once, with OpenAI seemingly attempting to carve out Codex as its core enterprise tool and clearly distinguish it from its consumer offering. It’s a smart branding decision to separate the two and is something that the product teams at the doomed Facebook Workplace (remember that?) probably wish they’d done sooner.

But this might not be the strategy for long; there were plenty of reports earlier this year that OpenAI is planning to build a “super app” where it merges both Codex and ChatGPT into one single app - but this doesn’t seem to make much sense if Codex adoption is really starting to take off.

Microsoft reveals an always-on sidekick called Scout, reportedly designed by product teams to be addictive

Microsoft held its annual Build conference this week, too. You can watch the Keynote presentation here but some of the announcements worth knowing about for product teams include the arrival of a new Assistant it calls Scout, as well as some new hardware devices.

Scout is Microsoft’s first of Microsoft’s new suite of “Autopilot” agents. It’s an always‑on, autonomous work assistant that lives across Teams, Outlook, your desktop, browser and OneDrive/SharePoint, and is designed to continuously take coordination work off your plate without waiting for prompts. Because Scout is powered by Work IQ and operates continuously, it gradually captures patterns about your work - who you collaborate with on which topics, how far in advance you prepare, which projects are high priority.

Shifting Copilots into Autopilots is a smart way of demonstrating how Microsoft’s agentic offering is evolving but the feature and the product teams who built it have found itself at the centre of some controversy, with leaked strategy documents reportedly revealing that one of its core goals is to make people “addicted”. According to a new piece from 404, the internal Microsoft document, called “ClawPilot: Overview and Plan with Project Lobster,” seen by 404 Media has a subheading called “ClawPilot Overall Plan,” which notes “three phases” to its launch plan. And the first phase is “Make people addicted.” This might be controversial to folks who don’t work in product management but ever since Hooked was published over 10 years ago, many product teams have continuously used addictive loops and UX patterns to drive engagement.

Project Solara and “just in time UI”

There were other agent-related announcements made at the conference including a new series of intelligence layer APIs but one announcement that stood out was the launch of new hardware as part of Project Solara.

Project Solara is Microsoft’s name for a new platform for AI Agents that can extend its capabilities across a collection of new devices. In this demo, Microsoft shows how this new platform works in the context of a corporate badge that can be used in settings like medicine:

The device has an “agent driven interaction model with just in time UI that adapts to the form factor” . For product teams, this is the latest example of this new concept of UI on demand where an agent surfaces a lightweight context-specific UI that’s personalized to the user.

Hinge wants its users to behave themselves - and it’s offering a badge to users who do; could this influence other product’s activation tactics?

Dating app Hinge has launched a new feature which gives a badge to users who exhibit what it deems to be good behaviour. Users can get the badge by meeting what Hinge calls “baseline requirements” - which includes things like a completed profile and selfie, as well as “thoughtful participation”signals that include sending comments, messaging matches and looking before liking a profile.

In a sense, this is Hinge gamifying their product activation by only giving the badge to users who meet these criteria. And the net effect for everyone is that the overall experience of using the product improves as users potentially change their behaviors to get a badge.

Traditional product activation is seen as one and done; a user checks off a bunch of onboarding items and is considered activated. But this creates a new type of ambient, ongoing activation where users are continuously nudged into engaging with the product in a positive way. Other products could experiment with similar tactics where behavior is rewarded with social badges (think Airbnb badges for good hosts, YouTube badges for users who engage in civil discussions) but there’s always a risk some users will game the system and behave inauthentically, so it’ll be interesting to see what impact this new feature actually has.

New generative AI in search UX risks confusing users

Amazon has updated its search UX with some new capabilities again. This time, it is introducing a new feature that creates AI generated images of products that don’t necessarily exist. So, for example, if you want a “shirt with a draped collar”, the results set will show you an AI generated image of that as you type. Users can then use that image to perform a visual search to find similar items that look like the visual.

On the face of it, this feels like it has the potential to be rather confusing to users who might (understandably) expect the visualised product to exist. But, if this works even modestly, it could turn stalled searches into purchase paths. The visual-to-product pipeline also generates training signals on what generated images correlate with actual purchases - compounding advantages over time. Amazon may even make this data available to third party sellers to incentivize them to produce products that people are searching for - and an image will do an excellent job of expressing that.

Tools you can use

Presentify - take your presentation skills to the next level. Annotate your screen, highlight your cursor, and spotlight or zoom in on key areas.

Tokenwise - find out where you’re over spending on LLMs in your company.

Dreambeans - an experimental new product from Google that delivers collections of personalized stories, surfacing things you might otherwise miss, alongside topics that are relevant to you.

📈 Product data and trends to stay informed

AI tools now account for up to a quarter of total referral traffic for retailers and 25% say they trust the retailer’s own AI Assistant over third parties. But 51% say they still don’t trust an AI Assistant at all. Full report from Bain on Agentic Commerce.

OpenAI says that non-developers now make up about 20% of Codex users - and are growing more than 3x as fast as developers:

Spotify’s controversial disco ball logo change had a measurable negative impact on App Store UI/UX review sentiment. The percentage of negative reviews jumped from 30.9% before the change to 44.1% afterwards. Icon design matters (to people who leave App Store reviews anyway).

Pinterest’s CTO says the company’s open-source AI stack costs 90% less than frontier models - and their custom-trained recommender outperforms off-the-shelf alternatives by 30% in accuracy.

Here’s the framework they use for deciding whether to build in house with open source / proprietary models or stick to off the shelf.

• Core = the thing that makes your product worth using. For Pinterest, that’s recommendations, visual search, personalisation. If it degrades, users leave. This is where you invest heavily - build in-house or fine-tune open source on proprietary data - because quality and cost at scale both matter too much to outsource to an API.

• Context = everything else that makes work happen. Internal tools, coding assistants, sales productivity, writing aids. Users don’t see them so an off-the-shelf API is fine because the cost of a suboptimal result is low and the switching cost is negligible.

GitLab says the number of code pushes on the platform is up 49% YoY - driven by AI agent activity. Revenue is up but it plans to cut 14% of its workforce, “with up to 3 layers of management removed”.

The era of “tokenmaxxing” could be over already. Uber has capped usage of AI tools to $1,500 per employee according to Bloomberg. The idea of tokenmaxxing was always a bit idiotic which incentivised all sorts of bad behavior and so this is largely being welcomed by folks in the industry. Simon Willison says that the $1,500 monthly limit per tool is a a rational policy response to over-spending, and much more sensible than those tokenmaxxing leaderboards encouraging employees to compete for as much AI usage as possible. That means each employee’s AI spending cap is ~11% of that median compensation package.

A new study has found growing evidence of dark patterns in chatbots. The study by The Center for Democracy & Technology found that 37% of interactions where users tried to end a conversation with companion chatbots like Replika and Character.AI, the chatbot attempted to continue engagement by invoking guilt or fear-of-missing-out. 21% of responses implied the user was emotionally neglecting the chatbot. These tactics increased post-goodbye engagement by up to 14 times.

Anthropic says 95% of its business analytics queries are automated via Claude, with ~95% accuracy. And in a new piece of research, it confirms that In the second quarter of 2026, the typical engineer was merging 8× as much code per day as they were in 2024 - because most of the code is written by Claude.

Upgrade to unlock the full newsletter. Paid subscribers get the full DoP Substack including: The Knowledge Series, the AI Prompt and Mini App library and DoP Deep dive reports for in-depth analysis and case studies to learn lessons from the world’s top tech companies.

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At Anthropic’s recent Claude Code events in London and San Francisco, alongside the talks announcing new features and capabilities, many of the speakers took their time to re-engage with features that already exist.

Given the sheer velocity of new features shipped by Anthropic and other AI companies over the past few months, these refresher talks were a welcome addition to the lineup where Anthropic's employees revisited some of the most powerful tools available across the Anthropic product suite.

And one of the products that was mentioned and demoed by multiple speakers was Claude Routines.

Claude Routines was originally released earlier this year as part of a full redesign of the Claude Desktop app but since then it has often found itself drowned out by other flashier new releases. But, getting it set up as an always-on assistant to work on regular routines on your behalf can be a powerful addition to your everyday workflow.

In this Knowledge Series, we’ll step through the essentials of Claude Routines and how it works, together with 5 practical routines you can get started with, including:

1. Weekly backlog triage

2. Changelog to release notes pipeline

3. API deprecation watchdog

4. Competitor changelog tracker

5. User feedback aggregator

Claude Routines explained; what they are and why they’re valuable

Routines is a feature inside Claude Code that lets you run automated, proactive Claude Code sessions without building any infrastructure yourself. In a sense, they let you put Claude Code on autopilot - with some caveats.

Instead of waiting for you to type a prompt and press enter, Claude can kick off work on its own - triggered by a schedule or an external event.

Traditionally, running Claude Code in an automated way with cron jobs or a webhooks would mean you’d have to manage everything yourself, and if your laptop closes, the session dies. Routines moves all of that onto Claude Code’s managed infrastructure so none of it depends on your local machine - unless you want it to run locally, instead.

How to set up a routine

There are two ways to create a routine. From the terminal, you can type /schedule inside Claude Code and describe what you want done in plain language. Claude asks a few follow-up questions - when should this run, what tools does it need, where should it send notifications - and then creates the routine for you.

Alternatively, in the Claude Code desktop app, navigate to Routines in the left sidebar and open the “New routine” form.

You fill in a name, write the instructions directly into a text box, pick a trigger from a dropdown, select which repositories to connect, and attach any connectors the routine needs - such as Google Drive, Notion, or Slack. The form gives you a bit more visibility upfront, since you can see and edit all the configuration in one place before hitting Create.

For this tutorial, we’ll stick to the Claude Code desktop app to keep things simple.

A routine needs to have the following options set:

• Name - a label for the routine

• Instructions - the prompt Claude follows during each run

• Repository - this is one or more codebases Claude has access to. For Routines to work remotely they need to have access to the relevant code base/s. More on that below.

• Trigger - the event or schedule that kicks off the routine

• Connectors - external integrations available to Claude during each run

• Behavior - a tab suggesting there are settings around how Claude operates (not fully visible)

• Permissions - a tab suggesting there are controls around what Claude is allowed to do (not fully visible)

Local vs Remote: How repositories work in the context of Routines

When setting up your routine, you’ll be given the choice between running it locally or remotely. Since we don’t want these routines to dependent on our machine running, we’re going to choose remote.

Claude Routines is designed to work with code repositories and so to get the most out of it, you’d need to be able to give Claude access to one or more repositories.

In our examples, we’ll assume that you have access to your a code repository and a place where you can save skills to be used in Routines.

How triggers work

There are two types of triggers when working with Routines: time based and event based triggers.

Time-based triggers run on a schedule. So, for example, every Monday at 10am, once a week, or whatever cadence makes sense for the task.

Event-based triggers fire in response to something happening. Within this category there are two options:

• Native GitHub events, such as a pull request being opened or closed, an issue being created, or a release being cut.

• Custom events from your product to an API, where you post to a endpoint that routines expose. You can include whatever context you want in the API request, and Claude will have access to it during the session.

How connectors work in the context of Routines

Connectors also play a critical role in Routines. Connectors are integrations you attach to a routine that give Claude access to external services during each run.

When you set up a routine, you choose which connectors to include - things like GitHub, Slack, Google Drive, or Notion. These define what Claude can actually reach out to while the session is running. For example, if you want Claude to open a PR when it finds a documentation gap, it needs the GitHub connector. If you want it to ping you when it’s done, it needs Slack.

One thing that’s worth noting is that Claude can use all tools from attached connectors - including write operations - without asking for permission during each run. That’s by design, since these are meant to be autonomous sessions that don’t wait for human approval. But it means you should be deliberate about what you attach. If you give Claude a connector, assume it may use it.

This is different from a regular Claude Code session where you’re present and can approve or reject actions as they happen. In a routine, Claude is operating on its own, so the connector list acts as the boundary of what it’s allowed to touch.

5 Practical ways to use Claude Routines at work

Now that we’ve covered some of the essentials worth knowing, let’s take a closer look at some of the practical ways you can actually use Claude Routines at work.

For this, we’ll explore 5 different use cases including a weekly backlog triage, changelog to release notes pipeline, API deprecation watchdog, competitor changelog tracker and user feedback aggregator.

1. Weekly backlog triage

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This week, Figma’s CEO declared that the design vs. code debate is a false dichotomy - and released a product to prove it. We’ll take a closer look at what it does.

Plus, as the dust settles on last week’s Google’s I/O event, there was one announcement that didn’t get a lot of attention that could transform how the web looks. And Claude Code gets a new Dynamic Workflow feature to tackle complex, multi-stage coding tasks end-to-end.

Happy Friday!

Rich

Watch on YouTube | Follow on Substack Notes

Subscribe now

Key reads and resources for product teams

New from the Department of Product Substack this week:

Knowledge Series - How to build a Strategy Thought Partner grounded in Product Frameworks
Adobe’s former CPO warns that outsourcing thinking to AI erodes our strategic abilities - but what if you used AI as a thought partner instead? Learn how to build a product strategy tool grounded in 25+ frameworks (Kano Model, Jobs to be Done, Porter’s Five Forces, and more) that helps you stress-test ideas, evaluate features, and sharpen your thinking without replacing it.

AI Library - Identify opportunities for a Slack AI Agent in your company
Using DuoLingo as inspiration, use this to describe your organisation - team size, tool stack, pain points - and it returns a ranked list of Slack AI agent opportunities, each broken down by what triggers it, which systems it needs to pull from, what it posts back, whether it requires write access, and what the failure risk is. (Department of Product)

Insights - How Uber manages what its AI Agents can see and do
When an AI agent on behalf of an engineer opens a pull request to fix a system bug, who gets credit - and accountability? Uber engineers Matt Mathew, Prasad Borole, and Meng Huang tackle a critical gap in AI identity systems: how to trace actions back through chains of delegated AI agents to the original human user. (Uber Engineering)

Interview - Claude Code creator on why his product manager now codes and the future of software development
Anthropic’s Claude Code creator Boris Cherny believes software engineering as we know it is ending (he hasn’t written a line of code in six months). But unlike some doomsayers, Cherny thinks job destruction will be met with massive job creation. He notes that roles are blending together: his product manager now codes, his designer codes, his manager codes and predicts there will be 100x more of these “builders” in the future. (Platformer)

Design - Why your opening prompt is the most important when using Claude Design
Your opening prompt in Claude Design sets the ceiling for everything that follows. A team at Contra Labs analyzed five designers’ sessions on the same luxury resort brief and found the first prompt mattered more than the next fifteen combined. (Contra Research)

Case study - How Dropbox built its internal coding agent, Nova
Dropbox built Nova, an internal platform that lets coding agents handle not just code writing, but the messy operational work that slows engineers down. In a post by Mike White and Kevin Altschuler, they explain why a shared platform beats one-off AI tools, how they keep agents grounded in real validation systems, and what they’ve learned from scaling agents across their entire development lifecycle. (Dropbox Engineering)

Resource - 50+ Claude Skills for designers and product managers
Tommaso Nervegna has curated 50 Claude Code skills you can install in 90 seconds and use on real work today. Examples include a diagnostic that audits project bottlenecks, a conceptual model builder to align teams on what the product actually is, and a design system audit to catch consistency issues before migration.

Strategy - How to build a self-improving company with AI
This talk from YC partner Tom Blomefield reveals how to extract domain knowledge, build recursive systems that learn from their own failures, and what humans are actually for in this world. Read the full breakdown of what’s coming next. (Y Combinator)

Tech - The 4 new types of AI-driven tech debt - and how to prevent them
Vikram Venkat, principal at Cota Capital, breaks down four new forms of AI debt silently sabotaging enterprise deployments and reveals how leading organizations can prevent catastrophic failure before it’s too late. (Venture Beat)

Summer reading list

New product features and innovation this week

Anthropic has released its new model, Opus 4.8, together with a new feature it calls Dynamic Workflows. This lets Claude Code orchestrate dozens to hundreds of parallel subagents to tackle complex, multi-stage coding tasks end-to-end. Instead of Claude processing work linearly in a single pass, it breaks large problems into subtasks, runs them in parallel across independent agents, and verifies results before presenting them to you.

When you trigger a workflow (either by asking Claude directly with the word “workflow” or enabling “ultracode” mode), Claude plans the work dynamically, distributes tasks across subagents, and uses verification agents to check findings from independent angles. Progress saves continuously, so interrupted jobs resume where they left off. This approach handles work that would normally take weeks or quarters in days.

Here’s some practical use cases for product teams to consider:

• Codebase-wide bug hunts - run parallel searches across a service, then have independent agents verify each finding before surfacing real issues in a report

• Large migrations - port codebases across frameworks, deprecate APIs, or change languages across thousands of files with coordinated parallel execution

• Framework rewrites and tech debt - systematically modernize legacy code by mapping patterns across the old codebase, generating replacements in parallel, and iterating until test suites pass

• Managing A/B tests - Claude’s head of product Cat Wu says she recently used this new feature to catalogue 100s of A/B test flags and deprecate stale ones. Instead of waiting for Claude Code to investigate each one sequentially, dynamic workflows allowed her to do them all in parallel.

Because it’s all a script under the hood, workflows are also reusable: you can save them, commit them to your repo, share them with your team and/or turn them into a skill. But, be warned: workflows use significantly more tokens than standard sessions, so budget planning is essential.

Figma’s CEO: Design vs Code is a false dichotomy; new update lets non-engineers make code changes for engineers to review

Figma’s CEO says that the “design vs code” debate is a false dichotomy and that the company is working towards a future that includes both freeform design and prototyping with code and shipping to production - in one connected platform.

The data backs up the idea that more designers than ever are writing code and this week, the company unveiled an update to Figma Make that lets it work using local code bases - much like Claude Code or Cursor. The new update allows non-technical members of product teams like designers and product managers to make visual changes to a product’s live code base and open up pull requests for engineers to review.

Here’s a deeper preview of the new abilities from Figma’s product manager and product marketing manager:

The convergence between coding and design tools is very real so instinctively this makes sense. The problem for Figma, though, is it’s getting squeezed from competitors as the traditional boundaries between product roles continue to blur. This week, Replit joined the fight by shipping new updates to its own design tool, Replit Canvas. Cursor released some lightweight design features in December last year and it wouldn’t be surprising if they, too, added further design capabilities.

Google’s new HTML in canvas, shareable Gemini Workspace assets

Now that the dust has settled on last week’s I/O event, there was one announcement that seemed to get lost among the noise. This is the official announcement of an experimental new API called HTML in Canvas.

Traditionally, web developers faced a choice: use the DOM for semantic richness and accessibility, or use canvas for performance and graphics control. This new API aims to bridge that gap. When you nest HTML elements inside a canvas, the browser can render them into graphics textures while keeping the underlying DOM element interactive and connected to browser features.

For product teams, this could lead to innovative new web interfaces and also make it easier for AI Agents to read the contents of page designs that would otherwise be off limits.

As part of the release, Google’s product manager Thomas Nattestad shared a selection of different demos showing the technology in action.

The demos include a 3D book that uses HTML to render the pages, a dynamic billboard in a 3D environment and new typography designs with refractive overlays. They’re fairly primitive demos right now but we’re likely to see some further, more interesting examples emerge as the API trial continues.

This week, Gemini also got some new updates which make it easier to share chats and collaborate. Projects is coming to Business users of Workspace which will make it easier for groups of people to manage and collaborate on distinct projects and generated media will now also be easier to share.

YouTube will auto-detect AI generated videos and label them more prominently

YouTube’s product teams have confirmed that it will now auto-detect and prominently label AI generated content. On Shorts, the label will appear as an overlay and on longform video it will appear directly below the video player, just above the description.

For users who hate AI generated slop, the updates will be welcome news but the CEO of AI video platform RunwayML isn’t a fan - and advocates for the complete opposite. In his view, we’re headed towards a future where all video is AI-generated and in that scenario, labeling videos as AI generated “would be like trying to determine whether water is wet”. Of course, being the co-founder of a video generation platform, he would say that, but does he have a point?

Tools you can use

Limora - Generate any design asset you actually need in minutes.

Supaboard - transform data into strategy. Run queries like “Give me my acquisition, conversion and activation funnels, and tell me where it’s leaking and I’m losing users.”

Taste MCP - lets your taste follow you across different AI tools.

📈 Product data and trends to stay informed

GPT 5.5 has jumped to the top of the leaderboard of a new software engineering benchmark, DeepSWE:

DeepSWE is a new benchmark that measures how well coding agents handle realistic software engineering tasks by using original prompts written from scratch (not recycled from existing commits), spanning 91 repositories across 5 languages with solutions requiring substantially more code changes (668 lines on average) while keeping prompts short and natural.

Accelerating coding benchmarks continue to have a negative effect on junior software engineering hiring but AWS’s CEO says replacing junior engineers with AI is “the dumbest thing I’ve ever heard”.

Claude Code’s creator Boris Cherny seems to agree. He predicts a 100x increase in people writing code or using AI agents to write code, as productivity gains enable companies to expand rather than contract engineering talent:

Early data on ChatGPT’s new ads product is pretty positive. ChatGPT ads currently generate an average click-through rate of roughly 0.68%, according to Similarweb data, placing them between display ads and traditional Search ads.

DuckDuckGo installs are up 30% after Google’s I/O event last week, with some users expressing worries that Google is “force feeding” them AI features they don’t want.

Claude Mythos Preview has scanned >1,000 open‑source projects It has identified an estimated: 23,019 total vulnerabilities, of which 6,202 are estimated high‑ or critical‑severity. Read the latest status update report from Anthropic.

Anthropic’s growth revenue multiple in the first five months of 2026 dwarfs OpenAI’s, but how long can this realistically last? If a decent chunk of these revenues are a result of inefficient token spend, companies will be incentivised to cut spend or find ways to make it more efficient.

Uber’s CTO famously said he’d spent the entire 2026 budget already last month and this week, their COO said AI spend must show ROI for it to be justified.

It’ll be fascinating to see how revenue growth looks in ~12 months and what companies do to manage spend under greater scrutiny.

Mobile Apps launched before 2020 still generate 69% of all subscription revenue and new subscription app launches went from ~2,000/month in January 2022 to 14,700+ by January 2026, largely driven by AI-assisted development according to the latest State of Subscription Mobile apps report by Revenue Cat.

Other stats from the report worth knowing about for product teams:

• AI-powered apps generate 41% more revenue per payer than non-AI apps.

• ChatGPT normalised $20/month pricing. AI apps now monetize at roughly 2x pre-AI ARPU. Before ChatGPT, most consumer subscriptions topped out around $60/year.

• Health & Fitness leads Day 14 revenue per install at $0.48 median - nearly 6x Gaming’s $0.08.

• Business apps lead trial-to-paid conversion at 9.1% median - more than 2x Gaming’s 4.4%.

Visa says 1,000 of its employees are now using Replit for product prototyping and development - and it is investing an undisclosed amount in the company.

Paid subscribers get the full DoP Substack including: The Knowledge Series for sharpening your tech / AI skills, the AI Prompt and Skills library and DoP Deep dive reports for in-depth analysis to learn lessons from the world’s top tech companies.

Subscribe now

In this Knowledge Series, we’re going to take that principle and build a product strategy thought partner you can use at work. This tool lets you run ideas past structured frameworks, stress-test decisions, and evaluate new features in a conversation – so you’re working with AI to think more clearly, not asking it to think for you.

The product strategy thought partner is grounded in over 25 practical, real world frameworks like the Kano Model, Jobs to be done, Pre-mortems, Porter’s Five Forces and more.

Once you’ve stepped through the different parts of the framework and your strategic analysis is complete, you can generate a visual asset and use it in presentations or elsewhere.

Subscribe now

Here’s what we’ll build together:

The Strategy Thought Partner builds upon the tool we created in a previous Knowledge Series which allows you to stress-test features against a panel of user personas.

Now, you can import feature ideas and run them through multiple different frameworks at once to analyze them across different lenses.

The frameworks library has over 25 different product frameworks and tools you can use split across 6 distinct categories including:

• Discovery

• Strategy

• Prioritization

• Delivery

• Decision making

These include things like Opportunity Scoring, MosCow, North Star Metrics, Impact / Effort matrices and more.

If you’re not sure what framework to use, it also includes a framework recommendation feature which lets you describe your situation or decision you’re trying to make and picks the top 3 most practically applicable frameworks:

The strategy thought partner uses your selected framework to ground your conversation and walk through its core components. Once the analysis is complete, you can generate visual assets from the session that you can reuse elsewhere.

In this Knowledge Series, you’ll get:

• a pre-built version of this app

• step by step instructions and prompts you can use to create your own version in Claude Code.

Getting set up

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This week, Google held its annual I/O event and Anthropic continued its world tour with its Code with Claude event in London. We’ll take a look at some highlights for product teams.

Plus, how DuoLingo built an internal Slack app that is now used by 30% of the company and new data from the state of AI in design reveals how the role of design is fundamentally changing in 2026.

Happy Friday!

Rich

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Key reads and resources for product teams

New from the Department of Substack this week:

Deep Dive - How Uber’s product teams built a PRD Reviewer - and how you can build your own
Uber built an AI tool that reviews PRDs before they reach leadership - catching gaps in assumptions, blind spots, and second-order effects that individual PMs miss. This Deep Dive walks through exactly what they built, how to create your own lightweight version using Claude Projects, together with examples of similar tools from DoorDash and Atlassian that are reshaping product development processes.

New in the AI library - Strategy Tension Tool
This tool takes two inputs - your stated strategy (north-star metric + priority areas) and your actual shipped work (tickets, features, quarters) - and does the maths to show how much engineering effort actually went toward each strategic area. It then compares the two distributions, flags any area where reality diverges from strategy by more than 20%, and produces a one-page report with enough specifics to walk into a leadership conversation and say “we said X mattered, but we shipped Y” with evidence behind it. (Department of Product)

Resource - Claude Cookbooks - a selection of free practical guides on how to use Claude effectively
Claude Cookbook offers 100+ practical guides for building with Claude - from basic workflows to advanced agent patterns. (Anthropic)

UX - DesignMD tool - paste a URL and get the product’s design DNA
Paste a URL — get a production-grade DESIGN.md with colour tokens, typography, spacing scale, and component patterns. ~12 seconds. (DesignMD)

AI Org Redesign - How Coinbase built a multi-agent compliance system that puts human judgement at its core
At Coinbase, Dor von Levi and team spent six months rebuilding fraud detection from scratch, discovering that the real work isn’t checking sanctions lists - it’s the interpretive judgment in the other 95%. They built a multi-agent AI system that handles 55% of US fraud cases while adding more human review, not less, and feeds every correction back into the model. (Coinbase on X)

Interview - Notion’s CEO on the future of SaaS and how the company is reinventing itself again
Ivan explains why most SaaS playbooks are broken, why refounding might be exactly what your stuck company needs, and how organizations will actually look in three years. (Sequoia Capital)

Case study - How Spotify runs better experiments with LLMs
At Spotify, only 12% of A/B tests ship positive results - but that understates their real value. Now LLM evals are changing the game by assessing quality dimensions at scale before experiments run. Matilda Ankargren and Mårten Schultzberg explain why the relationship between evals and experiments should be a funnel, not a fork, and how this feedback loop makes both smarter over time. (Spotify Engineering)

Skills - How to get buy in for your next big idea
In this conversation, Sue Ashford, professor at the University of Michigan’s Ross School of Business, and Ellen Bailey, former vice president of business and culture transformation at Harvard Business Publishing, give suggestions for framing those ideas so that executives buy into them, including the research findings they keep in mind, questions they ask themselves and others when vetting an idea, and what they learned from the times they fell short. (Harvard Business Review)

Process - How DuoLingo built an internal Slack app that is now used by 30% of the company
Duolingo built an AI Slack app that debugs incidents, triages issues, and ships code. What started as local experiments grew into a tool used weekly by 30% of the company. Aaron Wang walks through how they evolved from painful manual setup to zero-friction automation, plus the lessons learned scaling AI agents across thousands of weekly queries. (DuoLingo Engineering)

New product features and innovation this week

Google’s annual I/O event took place this week and included a bunch of announcements that are worth product teams knowing about. First, Google unveiled what it is describing as its “biggest upgrade in over 25 years”. And to be fair, on first glance, it looks like a pretty accurate claim.

The “intelligent Search box”, which is Google’s first major redesign in 25 years, now uses AI to help users formulate questions with smart suggestions beyond basic autocomplete. You can input text, images, files, videos, or Chrome tabs with the UI changing depending on the user’s request.

New Search agents will monitor the web 24/7 for updates matching your criteria, automatically notifying you when relevant information appears. The new Search mode also comes with coding abilities powered by Antigravity and Google’s new model, Gemini Flash 3.5. This mode generates custom UI components on the fly - things like interactive visualizations, dashboards, and mini-apps tailored to your task, while Personal Intelligence integrates your Gmail, Photos, and Calendar data so Search understands your context.

Here’s an example of a mini application built in Search about black holes on the fly.

For product teams, this evolution of Search may not instinctively feel particularly relevant. But, if these new patterns and modes do become popular, they could set a new benchmark for a user’s basic expectations of what in-product search is capable of. More and more companies like Reddit, Amazon and others are currently experimenting with new ways to do in-product search with new trends like merging search with AI Assistants and you can check out a deep dive on that here.

Gemini Spark and new voice modes in Google Docs

Google also officially unveiled Gemini Spark - a new personal AI agent that runs on Google Cloud infrastructure rather than your device.

Spark operates as an orchestration platform within the redesigned Gemini app. You can toggle it on and assign it tasks - either through text or voice commands - and it autonomously completes them by accessing your Google workspace (Gmail, Docs, Sheets, Drive, Calendar). The agent pulls information from multiple sources, performs actions across applications, and updates your files in real-time based on incoming changes.

For product teams, you might use Spark for things like:

• Daily Brief as stand‑up prep - pair Spark’s Daily Brief–style summary with your sprint board so you get “what changed in Jira/Linear + what’s on my calendar + which docs moved” every morning.

• Prep and debrief for meetings - have Spark prep context packs (latest spec, open risks, relevant feedback) before key meetings and auto‑draft follow‑up docs and emails afterward.

• Long‑running “product questions” - start an agent task like “track all evidence related to pricing pain for Teams customers,” and let Spark collect and organize signals over weeks instead of one‑off queries.

Google also rolled out new voice capabilities, including a feature called “Docs Live” that acts as a thought partner and co-writer to help users create a first draft of a document.

But, as impressive as all of these announcements are, some folks have complained that the overall product strategy feels a little overwhelming and disjointed, with too many Gemini products, names and processes to keep track of. Despite that, the data shows that in Search at least, Google’s strategy is working - more on that later.

Highlights from the Anthropic Code with Claude event in London

Google wasn’t the only company to host an event this week, though, as Anthropic came to London to host its Code with Claude Event. I was delighted to be able to attend the event and one of the most interesting talks for me came from Spotify’s VP of engineering Niklas Gustavsson who outlined how the company is adapting to incorporate AI into its workflows.

During the talk, he shared that 99% of the company’s developers are now using AI and 73% of PRs are now AI assisted.

But, rapid adoption led to a bloated code base that was growing 7x vs the number of developers, leading to a “maintenance nightmare”. As a result, a custom built tool called Honk now handles maintenance, saving 90% of time on complex migrations.

Coding is no longer the bottle neck at Spotify; instead, figuring out what to build is. According to Gustavsson, validating ideas through prototyping has transformed how Spotify validates and prioritises ideas and prototyping that previously took days or weeks now takes minutes. He confirmed that in 6 months time, they will have a very different way of building products at the company but admitted that they’re still figuring things out as they go.

You can watch the full talk from Spotify’s Niklas Gustavsson here along with the rest of the talks from the event which are now on Claude’s YouTube channel (this talk on everything that’s new in Claude Code was also helpful).

Figma fights back with a new Agent

Reports of its death appear to be greatly exaggerated. This week, Figma released a new agent that works directly on the design canvas. The agent understands your design system, components, tokens, and team context - giving it access to information third-party tools can’t reach. It lives in the left rail alongside your layers, eliminating context switching.

The agent handles three core workflows: exploring design directions (generate multiple stylistic approaches simultaneously), automating busywork (bulk edits, component swaps, content population), and processing team feedback (summarizing comments, identifying themes, pressure-testing designs from different perspectives).

Unlike the separate MCP server that moves work between code and canvas, this agent operates within the same file where your team already collaborates. Investors seem pretty happy with Figma’s new announcements and the company’s stock is up 20% this month so far.

Tools you can use

Magic Path 2.0 - A canvas for product design. This second version is now a multiplayer canvas for humans and agents like Codex or Claude Code to design and build with AI.

A browser CLI - gives Claude and other agents 200+ pre-built integrations from weather.gov to LinkedIn to recreation.gov, so they can search, scrape, extract, and automate tasks across the open web without writing code.

Prelude - improve your product’s onboarding KPIs with world class human verification and authentication. Raised $20m this week.

📈 Product data and trends to stay informed

The latest State of AI in product design report found that more designers than ever are using AI to generate code. 50% of respondents said they are using AI for code generation - up from just 19% last year.

Here’s a snapshot of the core AI use cases for design teams from the report with a YoY comparison:

Other nuggets of data from the report worth knowing for product teams include:

• The average AI toolstack has gone from 3 tools to 7 in a year

• 78% use Claude as their primary AI tool - overtaking ChatGPT (65%) and 65% use Claude Code - a tool that didn’t exist when the 2025 survey ran

• A massive 74% of designers at 2,000+ employee companies use internally built AI tools

Google’s AI Overviews are now used by more than 2.5 billion monthly users, while AI Mode (the conversational search launched last year) now tops 1 billion monthly users. For comparison, ChatGPT has 900 million weekly active users:

OpenAI has released a B2B version of its Signals report which looks at how companies are using its set of products. It includes some interesting data points for product teams, including one nugget which explains that the gap between high AI adoption companies (frontier companies) and the rest is big - and is growing.

The report confirms that the frontier firm demands 3.5x as much intelligence per worker as the typical firm. This gap has increased from 2x in April 2025, suggesting that firms using AI most deeply are widening their lead and are better positioned to translate new AI capabilities into deeper, more complex work.

More tech firms are using their own employees as AI training data sources. Microsoft collects code written by its 100,000 developers through VSCode and Xbox game source code, then tracks which AI-generated suggestions engineers approve. Meta takes a more invasive approach - its Model Capability Initiative tracks employee mouse movements and browser activity to teach AI agents how people actually use computers. xAI offered employees $420 to donate their tax returns for Grok training and BCG is building an AI agent based upon the behavior of their sales teams.

Anthropic and OpenAI now generate 89% of top AI Startup revenue with almost $80 billion in annualized revenue - up 112% from six months ago. Salesforce alone is on track to spend $300 million on Anthropic’s models, representing almost 5% of the $6.7 billion Salesforce total cost on revenues last year.

Claude’s head of product, Cat Wu, says that in the past year, the median user of Claude Code went from using it 20 minutes a day to 20 hours a week:

Benedict Evans publishes a biannual report on the state of AI. The latest AI Eats the World report gives us an update on the current state of play.

Paid subscribers get the full DoP Substack including: The Knowledge Series for sharpening your tech / AI skills, the AI Prompt and Skills library and DoP Deep dive reports for in-depth analysis to learn lessons from the world’s top tech companies.

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One company that is still firmly on board with PRDs is Uber. And despite their CTO recently admitting that they’ve already spent their Claude Code budget for 2026, AI is transforming how they approach PRDs. This past week, Uber’s product teams revealed a powerful new tool that they use to write PRDs that involves the help of an AI reviewer that analyses its contents, runs contextual checks for similar ideas that failed in the past and assesses what impact, if any, the proposed feature might have.⁠

The PRD review doesn’t offload the critical part of deciding whether or not a feature should ship; instead, it works to gather the necessary contextual information to help product and leadership teams apply their human judgement instead.

In this Deep Dive, we’re going to take a closer look at what exactly Uber’s product teams built, plus, how you can build your own lightweight version of a PRD Evaluator in Claude - with relevant prompts and frameworks.

As well as this, we’ll explore other recent examples from companies including Atlassian and Doordash that show how they’re transforming their own development and design processes with AI, which you can use as inspiration for your company’s product development processes.⁠

Coming up:

• A deeper look at what Uber’s product teams built with their PRD reviewer

• How to build your own version of this tool with Claude quickly

• Other examples of how the product development process is being transformed from Atlassian, Doordash and others

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Uber’s PRD Reviewer Explained

Uber’s PRD reviewer was built to solve a very specific problem at the company: PRDs would reach the review stage with unsupported assumptions, blind spots around adjacent systems, unexamined second-order effects, or policy-sensitive changes without proper guardrails.

In a large company like Uber with thousands of employees, it’s difficult for product teams to truly get a 360 understanding of things like prior experiments that were run for similar features, hidden dependencies or adjacent impacts on other products in the organization.

Here’s a snapshot of what they built:

The PRD Evaluator is an AI-powered reviewer that starts with a PRD and assembles a broader knowledge base around it. This includes things like linked documents, related decks and meeting notes, prior experiments, cross-functional artifacts, and preloaded Uber-specific context like core principles, metric definitions, and key jobs to be done.

Crucially, it’s designed to augment - but not replace - senior judgement by ensuring PRDs are given the proper context they need before they reach a wider review.

A PM may not know a similar hypothesis was tested earlier by another team, may not realize a metric is missing an obvious guardrail, or may not see a downstream operational dependency because it sits outside their immediate product surface. Product managers can upload a draft PRD and get all of the necessary context they need to catch any gaps beforehand.

How it works - and how to build your own version with Claude

The PRD reviewer is a custom built internal tool that works across 4 key steps.

Step 1 - Build context beyond the PRD

The evaluator uses the PRD as an entry point, then searches across relevant company artifacts and linked material to assemble context needed to assess the decision well: related documents, prior experiments, cross-functional inputs, and preloaded Uber-specific context.

The point here is that many gaps in a PRD only become visible when you compare it against what already exists - prior experiments the current team may not know about, adjacent efforts that create dependencies, institutional context that never made it into the document itself.

Step 2 - Classify the PRD to calibrate review depth

Not every PRD needs the same level of scrutiny and so the evaluator handles this by classifying each PRD into one of four tiers:

Read more

This week, Notion unveiled a series of major new announcements as part of its Developer Platform that could have a significant impact on the product development process. We’ll take a look at how its new Workers and “Feature Creatures” can build internal tools and connect with third parties like Stripe.

Plus, Google announces the launch of agentic workflows and vibe coded widgets - could Apple follow suit? And Anthropic’s head of product reveals her vision for the future of product user experiences, but is a Claude backlash brewing?

Enjoy the rest of your weekend!

Rich

Watch on YouTube | Follow on Substack Notes

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Key reads and resources for product teams

New from the Department of Product Substack this week:

Knowledge Series - What is the Command Line Interface and why do they matter to product teams?
OpenAI’s co-founder called them “super exciting,” and Spotify’s CPO uses one every morning - but command line interfaces aren’t what you’d expect. Once the exclusive domain of engineers, CLIs are experiencing a renaissance thanks to AI agents, which now prefer text-based interfaces to clicking buttons. Find out why product teams should care about this old technology in 2026, with practical examples from Google, Stripe, and Spotify.

New in the AI Library - Build an early warning system that flags emotional churn signals
Build a churn prediction dashboard that scores customer accounts by engagement health, using behavioural signals like feature usage, survey responses, and beta participation. Outputs a weekly ranked report of at-risk accounts with warning signals and suggested actions, filterable by segment, tier, and geography. (Department of Product)

Org design - How to run an AI-native engineering org
Fiona Fung, Director of Engineering for Claude Code, walks through what broke at Anthropic (review, ownership, hiring) and the norms we had to rewrite to keep shipping. (YouTube)

Design systems - Microsoft’s new “AI-forward” design system explored
Microsoft is redesigning how AI thinks alongside you. Jon Friedman, writing for Microsoft Design, explains the Copilot Design System - a new framework that treats AI as a true thought partner rather than a fragmented tool. (Microsoft Design)

Case study - How Slack rebuilt its notifications system
Slack’s notification system was broken which meant users couldn’t understand their settings, mobile and desktop behaved differently, and confusion drove it into the top three customer complaints. Senior engineers Frances Coronel and Shilpa Kannan reveal how they rebuilt the entire system from scratch, unifying four conflicting models into one, and cut support tickets while increasing user control by 5x. (Slack Engineering)

Interview - Anthropic’s Head of Product on how future products will anticipate your needs
Anthropic’s product head Cat Wu predicts AI will soon anticipate your needs before you even realize them - a shift from today’s chatbots to proactive agents that handle work automatically. Wu discusses how Anthropic plans to stay ahead of competitors and what managing “fleets of AI agents” really means for the future of work. (Anthropic)

Strategy - Is Software losing its head?
As software gets stripped down to its core, the rules for competitive advantage are being rewritten. Some will stay durable. Others will vanish. Read Andreessen Horowitz’s Seema Amble’s full analysis on what changes when humans leave the building. (Andreessen Horowitz)

UX - The death of the empty state
Adi Leviim, a founder and engineer, argues AI products abandoned 40 years of HCI principles like signifiers and affordances for what amounts to no design at all. Read how Leviim envisions better first-run experiences using worked examples, starting verbs, and exposed model limits - design patterns that exist today but remain unused across the industry/ (UX Collective)

Analysis - Claude just overtook OpenAI - but is a backlash brewing?
Claude just overtook OpenAI as the most-used AI model in business - but the data comes with some serious caveats as the company risks a major backlash from the developer community.

New product features and innovation this week

Google held its Android event this week, where it released multi-step task automation capabilities for Gemini AI on Android devices. The feature lets users describe complex tasks through natural language, and Gemini executes them across multiple apps with user confirmation.

When you press your phone’s power button and describe a task, Gemini reads your screen’s current content as context. It then performs sequential actions - like copying a grocery list from Notes, opening your shopping app, adding items to cart, and pausing before checkout. The AI also auto-browses the web to complete tasks like booking appointments, fills out forms using your saved Personal Intelligence data.

Another feature worth knowing about is is “vibe-coding” for widgets. Users simply describe what they want in natural language (”Suggest three high-protein meal prep recipes weekly”) and Gemini automatically generates a custom widget. This could be the first step towards fully vibe coded apps that work natively in Android and the agent automations could change the way users interact with apps, but is iOS also set to adopt similar agentic features?

Apple has historically been openly hostile towards the idea of agents but it is reported to be considering rolling out similar features in upcoming versions of iOS. WWDC will reveal more about what Apple thinks - with potentially significant implications for product teams.

Notion builds a new Developer Platform and CLIs; Stripe live agentic dashboard impresses

Notion held its annual developer conference - with some powerful new features for product teams.

They’re branding this set of announcements the “Notion Developer Platform” and the core concept at the heart of this is something they call Workers. These are small programs that Notion hosts and runs in its own environment with the pitch that developers and coding agents can write the logic and Notion takes care of everything else.

Workers can connect to any external system with an API - third party services like Stripe, Salesforce, and Snowflake, or internal databases and tools - and pull live data into Notion as a shared canvas.

In this example, a “Stripe Sync” worker was created in Notion to sync data from Stripe to Notion. The result was a live Notion database with Stripe customers, subscriptions, and invoices syncing in real time, which a personal agent then turned into a team dashboard:

They also showed something called “Renewal Ronnie”, an agent built on top of that synced data that runs nightly, checks every customer renewal, and prompts the account team to reach out proactively.

Another demo showed a support team use case where a "Feature Creature" agent could check whether a feature flag was enabled for a customer, then attempt to toggle it - but only after validating the customer was on the right plan. When Gummy Bear Aeronautics turned out to be on Pro rather than Enterprise, the Worker blocked the toggle and logged the entire interaction to a shared customer database in Notion. Once built, the support team could run it themselves without involving engineering.

For product teams, this could be super-helpful for scenarios where you want to build your own internal tools or dashboards that proactively notify you of specific, relevant changes.

Microsoft, TikTok and Amazon’s AI feature cleanup

Feature bloat is a real risk in 2026, as AI makes it easier than ever for product teams to ship new features just because they can. This week, some companies started to slow down and assess and remove some AI features following user feedback.

TikTok is scaling back its AI summaries feature after complaints from users that it created bizarre and inaccurate captions and Microsoft is reported to be cutting back on the rollout of Copilot after Customers described sine Copilots as ranging from unnecessary to “functionally useless.” The Windows team has already removed Copilot entry points from Photos, Widgets, and Notepad. Gaming Copilot on Xbox is being wound down entirely.

Amazon is ditching its Rufus brand in favour of Alexa. Amazon says Alexa for Shopping brings together the Alexa+ and Rufus capabilities, signalling another step towards the simplification of pre-existing AI features.

Tools you can use

A1 MCP - Connect your AI coding tool to a curated library of real websites. Search by style, font, technology, and colour - without leaving your AI coding tool.

Dessn - a new design tool that runs directly in your codebase. Raised $6m this week.

Mark II - the bookmark reinvented. One of the few new “AI-powered” pieces of hardware that actually seems useful. Mark is a physical bookmark that tracks everything you read. The new version of Mark comes with a highlighter that lets you highlight passages and save them to apps like Apple Notes or Notion.

📈 Product data and trends to stay informed

Microsoft has released its annual Work Trend Index report. The research analyzes how workers actually use AI agents and copilots in Microsoft 365, based on telemetry from over 100,000 conversations and a global survey of 20,000 AI users.

The analysis reveals that nearly half (49%) of Copilot conversations support cognitive work - analyzing information, solving problems, and thinking creatively. The remainder splits between working with people (19%), producing work (17%), and gathering information (15%). Within cognitive work, decision-making and problem-solving dominate at 28% of all usage.

“Podslop” is the new term for AI generated posts. According to new analysis, 39% of new podcasts were likely AI generated.

LinkedIn is planning more layoffs along with operational changes. A new leaked memo from their CPO says “Our fastest moving teams are focused, have fewer layers, and leverage AI to move quickly… the changes we’re making across our Product orgs are based on creating more agile teams in this model.”

When tracking AI’s impact on jobs, don’t just watch for mass layoffs. Pay attention to companies quietly cutting hiring plans. SaaS company Monday.com is an example - it went from planning 20% workforce growth to expecting headcount to stay “flat” through 2026, reflecting the “productivity gains AI is already delivering” across their organization.

This week, for the first time, Anthropic passed OpenAI in business adoption, according to the latest release of Ramp AI Index. Adoption of Anthropic rose 3.8% in April to 34.4% of businesses. OpenAI adoption fell 2.9% to 32.3%. Overall AI adoption rose 0.2 percentage points to 50.6%.

Website visits to Claude were up a massive 34% in April, while ChatGPT was down -3.84% and Grok down-14.4%.

Airbnb’s CEO Brian Chesky revealed that AI now writes 60% of Airbnb’s new code, with AI resolving 40% of customer support issues without human intervention. He also said managers who won’t get hands-on with tools like Claude Code “have no place” at the company. Anthropic’s CEO says AI writes 90% of their code.

New analysis shows that over reliance on AI in software engineering is rotting engineers’ brains. And the former Adobe CPO argues that cognitive enhancement is better than cognitive offloading.

44% of DoP survey respondents said they prefer the idea of working with a team of AI agents over human colleagues. But for now at least, 56% say working with a team of AI Agents “sounds awful”:

Paid subscribers get the full DoP Substack including: The Knowledge Series for sharpening your tech / AI skills, the AI Prompt and Skills library and DoP Deep dive reports for in-depth analysis to learn lessons from the world’s top tech companies.

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Command line interfaces have been in the headlines for all sorts of reasons over the past few weeks.

OpenAI’s co-founder called them “super exciting”, Ramp’s CPO says they’ve seen usage of their CLI accelerating aggressively over the past few months and Spotify’s CPO said that their experimental new CLI product is something he now uses “every morning”.

Command line interfaces are an old technology, but they’re now experiencing a renaissance, in part thanks to AI agents, which Vercel’s CEO neatly captured in this meme:

And while CLIs have been traditionally used exclusively by engineers, they are now being rapidly adopted by non-engineers and product teams as AI makes them easier to use.

But what exactly is a CLI, and if they’re such an old fashioned concept why should product teams really care about them in 2026?

In this Knowledge Series, we’ll unpack everything you need to know - with some practical guides on how to get some hands on experience with it.

Coming up:

• What is the Command Line Interface and why does it matter?

• Recent CLI product releases from Google, Stripe and Spotify

• Hands-on: how to use the Google Workspace CLI - practical use cases for product teams explored

• The future - are CLIs likely to become a major new platform for new product feature releases? What is the strategic benefit of launching a CLI?

What’s a Command Line Interface?

A Command Line Interface, or CLI, is one of the oldest and most fundamental ways humans interact with computers. Instead of clicking buttons or tapping icons on a screen, you type text commands into a terminal.

CLIs predate pretty much everything we think of as “modern” product design. For decades, they were the only way to use a computer before graphical interfaces arrived in the 1980s. Which makes it all the more ironic that in 2026, we’re not reverting back to this headless way of interacting with products.

What CLIs can do

The Command Line Interface is designed to allow a user to do anything you can do with a traditional mouse and keyboard - and more. This includes actions like:

• Read and write files - A CLI can create, copy, move, rename, delete, and search files.

• Run programs and scripts - any script you write gets executed from the command line. This is how you kick off data processing jobs, run tests, start servers, and schedule tasks.

• Talk to the internet - you can hit any API, pull data from a URL, or post data somewhere - without writing a single line of application code. This is how CLIs talk to web services.

• Automate repetitive work - anything you do more than once can become a script. So for example, you use could rename 500 files, send a weekly report, back up a folder, process incoming data.

• Interact with external tools and services - this is what the recent CLI wave is about. Tools like the GitHub CLI, Stripe CLI, and Google Workspace CLI turn services you’d normally click through into something scriptable and composable. More on that later.

As you’d expect, you interact with Command Line Interfaces by using commands. We won’t get too bogged down in the specific commands you can use but here’s a snapshot of some of the most practically useful commands.

Traditionally, you’d have to learn these commands off by heart to truly be able to use a CLI, which made them off-limits to non-engineers. But AI is starting to change that.

Why Command Line Interfaces are popular again

AI coding assistants like Claude Code, OpenAI’s Codex, and others are increasingly capable of operating autonomously and these agents live in the terminal. They speak the language of CLIs natively.

Graphical interfaces optimize for visual cognition. Humans can scan a toolbar, recognize an icon, and click it in under a second but AI agents like text. Large language models (LLMs) are text-native in that they read text, reason about text, and generate text. A CLI is a text-in, text-out interface - which is why they’re now experiencing a resurgence.

Andrej Karpathy, OpenAI’s co-founder says that CLIs are super exciting precisely because they are a legacy technology - and that product teams should prepare to build for Agents:

This creates new demand for CLIs that simply didn’t exist a few years ago. When your users are increasingly software agents rather than humans, the design requirements of your interface start to change. Agents don’t need or care about friendly onboarding flows. Instead, they need deterministic, scriptable, composable interfaces. And that’s exactly what CLIs can do.

CLIS as products: Recent examples from Google Workspace, Stripe and Spotify

Read more

This week, Claude released a new feature called Dreams. We’ll dig into that along with new releases from OpenAI and Google Docs.

Plus, after announcing 14% job cuts, Coinbase’s CEO says that the future is “one product teams,” where the roles of product, design, and engineering all merge together under an “AI pod” that manages a fleet of agents. But is this a realistic vision of the future? We’ll take a look at his plans in more detail to try to find out.

Have a great Friday and enjoy the weekend!

Rich

Watch on YouTube | Follow on Substack Notes

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Key reads and resources for product teams

New from the Department of Product Substack this week:

Deep Dive - Spotify’s new Natural Language Ad API and other Natural Language Interfaces Explored
Spotify just let advertisers create campaigns through the API with natural language. Now 30+ companies - from Canva to Uber to Shopify - are rethinking entire workflows around natural language interfaces. This latest Deep Dive Product explores how NLI is transforming product design, which parts of your product might be ready for it, and why the future of UI might just be letting AI handle the clicks while humans focus on judgment calls.

New in the AI Library

• Identify opportunities for ChatGPT Apps for your product - This prompt builds a structured idea generator that takes your brand, customer types, and business model as inputs, then produces 8-10 specific ChatGPT app concepts tailored to your business

• Evaluate and remove AI feature bloat - use this prompt to design a customer feedback synthesis that identifies and removes feature bloat.

Interview - Uber’s CEO on Agentic Coding and why product people are making changes to Uber’s codebase
“Now, they’re just going in and they are vibe coding, and an engineer is going to review the code, but essentially the product person is going directly into the code base, so to speak, or going directly with an agent into the code base.” Uber’s CEO on replacing policies with outcomes, why agentic coding is changing who writes the code, and the bet that there won’t be one physical AI model to rule them all. (Decoder Podcast)

Resource - A library of sounds you can use on your product’s components to bring it to life
Designed by Raphael Salaja, this is a rich library of component sounds that simplifies sound management in web applications to help bring your product to life. (WebKit Audio)

Design - Linear’s Design Engineer on How to build agents with Taste
Emil Kowalski shares how he taught Claude to understand his design taste by packaging animation rules into a “skill file” - easing curves, scale values, timing guidelines, and all. (Emil Kowalski)

Strategy - Emotional churn explained
Emotional churn, when users mentally check out before officially leaving, can be the silent killer of your B2B business. It happens weeks or months before actual cancellation, hiding in apathy and radio silence, argues Jake Stein. (Substack)

Skills - A guide to Agent-Native Product Management
AI is now doing the drudgework of product management - turning three-hour analytics investigations into minutes of conversation with Claude. In a practical guide for Every, Marcus Moretti shares the agent-native techniques he uses to run product management for Spiral, including two installable skills (strategy and product-pulse) that let PMs focus on the actually interesting parts of the job. (Every)

Resource - WebUI Comparison Benchmark
A comparison of how different models handle the same UI prompts, side by side. Useful if you’re wondering which model to use for different parts of your product design process. (WebUI)

Analysis - Coinbase’s CEO says the future is “one person teams”
Coinbase is laying off 14% of its workforce, but CEO Brian Armstrong frames it as necessary preparation for an AI-powered future. The company is restructuring around smaller “AI-native pods,” eliminating pure management roles, and flattening hierarchy to move faster through market cycles. The future, he says, is “one person teams”. But is he right?

New product features and innovation this week

Anthropic released three new capabilities for Claude Managed Agents this week that help autonomous systems work more effectively without constant human oversight.

“Dreaming” is a scheduled review process that analyzes past agent sessions and memory to identify patterns (things like recurring mistakes, converged workflows, team preferences). It restructures memory to keep it high-signal as agents accumulate experience, enabling self-improvement across sessions. You control whether dreaming updates memory automatically or requires your approval first.

You can think of it as the agent’s being awake during the day (sessions) where it tries to write memories as it goes. And at night, as it sleeps, it compresses, deduplicates, removes stale or contradictory info, and surfaces cross-session insights that weren’t obvious from a single interaction. Just as a human might do when they dream.

Anthropic says companies including the likes of Harvey and Netflix are already using this. Harvey uses dreaming so agents carry knowledge between sessions, things like file format workarounds and tool quirks. The result was roughly a 6x improvement in agent completion rates.

Outcomes define success criteria as a rubric. A separate grader evaluates the agent’s output against your standards in an isolated context window, then surfaces specific gaps if corrections are needed. The agent iterates until the work meets your bar. This works for both objective requirements (file formats, completeness) and subjective criteria (brand voice, design guidelines). Internal testing showed 10-point improvements in task success, with +8.4% gains on Word documents and +10.1% on PowerPoint files.

Multiagent orchestration lets a lead agent break complex work into parallel tasks and delegate each to specialist agents with custom models, prompts, and tools. Specialists share a filesystem and context, enabling the lead agent to check progress mid-workflow. Full tracing in the Claude Console shows which agent did what and why.

ChatGPT gets a new plugin for Sheets and new Voice models, pitching voice as the “interface between people and products”

OpenAI released ChatGPT for Google Sheets, an add-on that embeds natural language AI directly into spreadsheets via a sidebar panel.Instead of manually building formulas or structures, you describe what you need in plain English - “create a budget tracker” or “analyze spending by category” - and ChatGPT builds, edits, and updates the sheet accordingly. It already has over 100,000 downloads with a 5 star rating so far, but it’s still early days.

This week, OpenAI also released three new audio models designed for developers building voice-enabled applications: GPT-Realtime-2, GPT-Realtime-Translate, and GPT-Realtime-Whisper.

OpenAI is explicitly pitching the new Realtime-2 model to product teams, describing it as an “interface between people and products”. They say that as voice becomes a more natural way to use software, three distinct patterns are emerging on how developers are using voice APIs:

1. Voice to action - where people describe what they need and the assistant uses tools on behalf of the user to complete the task

2. Systems-to-voice - where software turns context into live guidance e.g. “Your inbound flight is delayed, but you can still make your connection. I found the new gate, mapped the fastest route through the terminal, and your bag is still expected to transfer”

3. Voice-to-voice, where AI can help live conversations continue across languages, tasks, or changing context

Some product teams, they say, are combining all three approaches to build an end-to-end voice powered UX. You can watch some of those use cases in action here.

Other updates

Google Docs now lets users set persistent custom instructions for Gemini that automatically apply to all future interactions. Instead of repeating preferences like “use bullet points” or “keep it professional” in every prompt, users input these rules once in the Gemini side panel. Gemini then remembers and follows these instructions across all conversations within that document.

Spotify has released a Command Line Interface tool called “Save to Spotify”. It lets you connect tools like Claude Code generate podcasts from any text source and then store it to your library. Spotify’s CPO Gustav Söderström said:

The use cases feel obvious once you have it: for example a morning briefing built from your calendar and inbox. A deep dive on your class notes before an exam. A travel itinerary narrated for your flight.

Is this the end of the swipe?

Bumble may be ditching the swipe as a user interface for dating. They posted a cryptic “Dear swiping, it’s over” message on Instagram, sparking speculation that the dating app might ditch the swipe. Bumble reported a 21% drop in paying users this week and the company has hinted at a major overhaul called “Bumble 2.0,” including AI-powered features and in-person events. But will ditching swipes actually fix the app’s problems?

If it works, we could see other product teams in other categories follow suit and actively add friction to their core UX to encourage a more mindful experience. Which, given the state of the average person’s doom-scrolling induced, dopamine-riddled brain, may be a good thing.⁠

Tools you can use

Cognitive Bias Index - A tool for identifying and mitigating the systematic errors in thinking that affect the decisions and judgments that humans make.

Marcus - AI prompt library and thinking system for leaders. Apply concepts from over 100 business books in your prompts and stress-test important leadership decisions.

Kanwas - a centralized hub where product teams accumulate, connect, and build on product context.

📈 Product data and trends to stay informed

The SaaSpocalyse narrative was violated on a couple of fronts this week. Atlassian revealed that revenues grew 32% to $1.8 billion in its latest quarter, accelerating from 23% growth the previous quarter. Their CEO Mike Cannon-Brookes also revealed that customers using their AI Assistant Rovo generate twice the annual revenue compared to non-users.

And despite fears over the disruptive effects of AI, Figma was named as one of the fastest growing companies in Ramp’s latest AI tools report. This only uses data from Ramp customers but assuming Ramp’s customer profile is reasonably AI native, it indicates that those companies are not ditching Figma in favor of other AI tools.

Pinterest now has 631 million MAUs globally, up 11% year-on-year - tenth consecutive quarter of double-digit user growth.

AI now writes most of the new code at several major new tech companies and new data suggests that the hype surrounding Claude Mythos was justified. Anthropic says With the help of Claude Mythos Preview, the Firefox team fixed more security bugs in April than in the past 15 months combined.

Amazon says almost 60% of users now use autocomplete when searching - and the company’s product teams are working on a new hybrid search which will push users to Rufus if it detects questions more suited to a conversation.

Paid subscribers get the full DoP Substack including: The Knowledge Series for sharpening your tech / AI skills, the AI Prompt and Skills library and DoP Deep dive reports for in-depth analysis to learn lessons from the world’s top tech companies.

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The ability to speak to products in natural language is transforming pretty much every part of the product experience.

We’ve come to expect natural language interfaces (NLIs) in user-facing features like AI Assistants, but now we’re also starting to see it roll out to other parts of the product stack, too.

For example, just this past week, Spotify revealed that it has built a new plugin that lets its advertisers create and and manage ad campaigns in the Spotify API using plain language instead of having to write manual API calls. As well as this, Uber’s CPO revealed new conversational features in their annual conference and one CEO of AI company LlamaIndex put it nicely this week when he commented on his own current experience with engineering teams:

“Engineers are not actually writing real code…They’re all typing in natural language”

In this Deep Dive, we’ll explore some real world examples of new natural language interfaces and how NLI is transforming the features we build, as well as the underlying technical infrastructure that powers them.

Coming up:

• Spotify’s Natural Language ads builder and 30+ other examples of natural language interface UX in other products

• How one company built an NLI feature with 85% adoption rates

• From “Execution UI” to “Judgment UI” - a useful frame for deciding which screens in your product to invest in, simplify, or retire

• Prompts you can use to identify which workflows in your own product might be ready for NLI

• The full 30+ examples of NLI in action, from Canva, Adobe, Spotify, Amazon, Google, Figma, Shopify, Slack, Airbnb, Uber, Yelp and more

How this analysis is structured

There are over 30 different examples of natural language interface features included in this piece from companies like Amazon, Adobe, Uber, Canva and more. Each example includes a description of how it works along with links to find out more.

Each example is also categorized to demonstrate how NLIs are now transforming every part of the product stack, including:

• User-Facing Product - NLI features built directly into consumer or business products that end users interact with. The user replaces clicking, filtering, and form-filling with plain language. Examples include Spotify’s playlist creation (”lo-fi beats for a late-night study session”), Airbnb’s accommodation search (”a cozy cabin near a lake for a family reunion”).

• Internal Tool - NLI applied to internal business workflows - things that help employees rather than customers. The user here is typically a knowledge worker, analyst, or support agent. Examples include Slack’s natural language workplace search, Databricks’ pipeline generation, and Linear Agent handling ticket triage autonomously.

• Developer Tool - NLI aimed at software developers specifically - either to accelerate coding or to abstract away technical complexity

• Platform / Framework - NLI capabilities that other products are built on top of, rather than end-user features themselves. Microsoft’s NLWeb, Google’s FunctionGemma, Vercel’s json-render, and ChatGPT’s app integrations layer all sit here. These are the infrastructure layer that makes the other three categories possible.

What Spotify built: a natural language to API ad builder

Let’s start with a recent example from Spotify. Here’s a snapshot of what Spotify’s engineering teams built recently to allow advertisers to describe their campaigns in natural language:

Read more

This week saw companies including Stripe and Uber host their annual product showcase events. We’ll take a closer look at some of the announcements that matter to product teams.

Plus, Vercel’s head of design Hannah Hearth shares what products her team uses, Spotify and Reddit double down on human verification as a differentiator to fight back against AI slop and a new study reveals which models excel at different parts of the product design process.

Have a great weekend!

Rich

Watch on YouTube | Follow on Substack Notes

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Key reads and resources for product teams

Process - The tools that Vercel’s Product Design Team Actually uses
The Vercel design team tried every shiny new AI tool and discovered something surprising: there’s no one right way to design anymore. Hannah Hearth shares what product designers actually use day-to-day, from pairing Claude and Codex for code reviews to running parallel agent threads to stay focused. (Vercel)

Knowledge Series - DESIGN.md Explained: the format reshaping how AI builds UI
Google just open-sourced DESIGN.md - a format that turns design systems into “agent-native” specifications that AI can actually use to generate UI. But the excitement masks real challenges: AI-generated designs look polished even when conceptually flawed, and consistency doesn’t guarantee coherence. I break down what DESIGN.md is, why it matters for product teams, and how it could fundamentally reshape design roles.

New in the AI library

• Analyze how AI agents could disrupt your product’s strategy - use this prompt to think through the strategic implications of the shift from traditional apps to AI agent-based interfaces and what it might mean for your product’s strategy.

• Identify opportunities for Voice Agents in your product - use this prompt to systematically scan your product for places where an AI voice agent would genuinely improve the experience - rather than just bolting voice on because it’s possible. (Department of Product)

Interview - Google Deepmind’s Demis Hassabis on agents, AGI and building products that combine AI with hard science
Demis Hassabis, the Google DeepMind founder who cracked protein folding and won a Nobel Prize, believes we’re only one or two breakthroughs away from AGI. In a wide-ranging conversation, he reveals what’s still missing (continual learning, long-term reasoning, better memory systems), why agents are just getting started, and how to build products that combine AI with hard science. (Y Combinator)

Strategy - The five principles that made Japanese products like the Walkman, Gameboy and Pokemon unexpected hits
Sony’s Walkman shouldn’t have succeeded and colleagues called it ridiculous. Yet it became a global sensation. In this Harvard Business Review piece, Northwestern’s David Schonthal and Tokyo-based writer Matt Alt reveal the five principles behind Japan’s surprising hits - from Game Boy to Pokemon to emoji - showing why convenience beats specs, constraints spark creativity, and intuition matters more than data when predicting the future. (Harvard Business Review)

UX - 10 UI patterns that won’t survive the AI shift
Designer Taras Bakusevych identifies 10 UI patterns under pressure, from setup wizards to notification feeds, and shows what’s replacing them with real product examples. (Substack)

Pricing - Why Headless software is the future and what this means for pricing models
As AI agents become primary software users, traditional business models are breaking. Aaron Levie from Box explores how software pricing must evolve. (X)

Technical showcase - Uber’s Chief Product Officer reveals new capabilities at their annual product event
Uber’s CPO, Sachin Kansal, hosted an event alongside the company’s CEO where they revealed new capabilities including AI voice, the ability to book hotels and a new way to search that the company is calling One Search, which transforms the search bar into a universal finder across all Uber service. (YouTube)

Resource - 2,000 DESIGN.md free files you can use
Search curated references by brand, mood, color, typography, or URL. Open any style for colors, type, spacing, components, and a DESIGN.md your agent can use. (Refero)

New product features and innovation this week

Google has released a file generation feature in Gemini that lets users create downloadable documents directly within the chat interface. Instead of drafting content in Gemini, copying it elsewhere, and reformatting, you can now prompt Gemini to generate files in multiple formats - PDFs, Word docs, Excel spreadsheets, Google Workspace files, and others - to download or save to Google Drive immediately.

It’s a little surprising that it took Google so long to do this, but still, it’s a welcome addition to the Workspace Gemini line up.

Stripe shows off its new payment capabilities at Sessions 2026

This week, Stripe held its annual Sessions conference this week - and the event was packed with a bunch of new announcements that are relevant for product teams.

One of the most practically relevant products is Checkout Studio, a visual builder that lets you design, test, and optimize payment checkout experiences without writing code. It combines a drag-and-drop configuration interface with an AI assistant, live transaction replay, A/B testing, and performance analytics.

The tool works by letting you customize checkout fields, payment method selection, and form layout through the Dashboard. The AI assistant suggests optimizations based on your transaction data - things like reordering payment methods to match customer preferences or adjusting required fields. Live transaction replay shows you exactly how real customers experience your checkout, highlighting where they drop off and built-in A/B testing lets you compare different layouts or field configurations against your baseline performance.

Other new features announced include a Link Agent wallet that lets agents spend on your behalf and programmatic transactions using the Machine Payments Protocol. Link functions as a traditional digital wallet - you connect payment methods (cards, bank accounts, crypto wallets, BNPL services) and store checkout details like billing and shipping addresses. When you grant an AI agent access to Link via OAuth authentication, the agent can request permission to spend money. It’s still early days, and data suggests users are still weary about letting agents spend on their behalf, but as trusted companies like Stripe roll out agents with spend limits, that might change pretty quickly.

You can see a thread of examples of what people are building with that here and Stripe has also published a public roadmap of upcoming features here. As well as this, Stripe’s product and engineering teams built their own API assessment tool that lets you check your product’s API docs and flag any potential design issues.

At $2 per check, it’s slightly bizarre but if nothing else, is a neat way for Stripe’s product and marketing teams to flex their technical documentation design skills.

“Authentically human” users is now a product differentiator

Spotify has launched a “Verified by Spotify” badge system to help listeners identify authentic human artists amid rising AI-generated music. The badge appears on artist profiles and search results as a green checkmark with “Verified by Spotify” text. Spotify hasn’t published any data on what percentage of new music is AI generated but a recent report from Deezer revealed that a massive 44% of all music uploaded is now AI generated.

Human verification features are being used as signs of product strength in other products, too. In its latest earnings report this week, Reddit declared its platform as “authentically human”.

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Other updates worth knowing

Notion is rumoured to be adding a “Computer” capability to its AI agents, enabling them to control browser-based and desktop tasks within sandboxed environments provided by Anthropic’s infrastructure.

Linear has launched a new feature called “Linear Releases” which connects your CI/CD pipeline to Linear, automatically tracking where each issue stands in deployment. When code changes reach production, issue statuses update automatically - so you see what’s actually live to customers, not just merged code. It does start to feel as though all of the major product development SaaS products are starting to converge in an attempt to own the complete context of how work moves from idea to production.

Tools you can use

Adoptly - turn product updates into feature adoption.

Exa for Claude - Give Claude access to billions of websites, docs, papers, people, companies, and more.

Velo - turns your raw screen recordings into watch-worthy videos. Could be useful for things like product demos or new feature explainers.

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📈 Product data and trends to stay informed

A new study developed by Contra Labs entitled the Human Creativity Benchmark found that no single model excels at all stages of the product design phase. One part of the study looked at landing page results and found that Claude Opus 4.6 leads ideation at 68.9%, winning the open-ended “what should this be?” problem. Gemini 3.1 Pro takes the lead on mockups, hitting 68.9% and Claude reclaims refinement at 60%, edging out competitors on polish and iteration.

New analysis found that enterprise software companies are abandoning flat-fee AI pricing in favor of charging based on actual usage. By the end of 2025, 79 of the 500 largest software firms - including HubSpot, Adobe, and Salesforce - had switched to usage-based models. This number is likely to grow in 2026.

But, outside of the 500 companies tracked in that analysis, seat-based pricing still leads the way. Just 3.8% of traditional SaaS products currently charge on a consumption based model, vs 74% of AI Labs according to Ramp.

Nearly half (46%) of respondents in a new Adobe survey say that they don’t care if a brand uses AI so long as their needs are met.

Meta still burnt through over $4 billion on Reality Labs this quarter, down slightly from $4.2 billion in 2025 according to its latest earnings report. Revenues were also down at $402 million from $412 million in the year prior.

OpenAI is betting on a structural shift away from premium subscriptions toward an ad-supported model that will initially shrink its paying user base but eventually generate far more revenue through advertising.

ChatGPT Go ($8/month) is projected to grow from 7% to 92% of all paying subscribers and ChatGPT Plus ($20/month) would drop from 92% down to just 7%, with Pro making up the rest.

OpenAI expects ads to become its single largest revenue driver, generating $102 billion (36% of total revenue) by 2030. The strategy mirrors Netflix’s 2022 ad-tier launch, which successfully drove both subscriber and total revenue growth, but can they pull it off?

Venture capital is flooding into voice AI startups with over $7 billion invested in Q1 2026 alone. The voice recognition market, currently valued at $22 billion, is expected to nearly triple over the next five years.

Paid subscribers get the full DoP Substack including: The Knowledge Series for sharpening your tech / AI skills, the AI Prompt and Skills library and DoP Deep dive reports for in-depth analysis to learn lessons from the world’s top tech companies.

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When Square’s Head of Product recently announced1 the company’s impressive new agentic features which proactively monitor every user’s store and deliver insights on demand, he said that the AI Agent harness powering the feature was its “secret sauce”:

Actually, managing the context and managing the way that we progressively disclose tools, and some of the other innovation that we have at the harness layer, is I think some of the secret sauce.”

Block’s CEO Jack Dorsey has also echoed this sentiment, saying that his vision is that the harness becomes infrastructure for organizational intelligence rather than just developer tooling.

Agent harnesses are helping some of the world’s leading product teams ship powerful new features and Anthropic and OpenAI have also both announced major updates to their AI Agent SDKs to offer enhanced AI Agent harness capabilities for product teams.

Google Trends data certainly shows a clear signal of an increased interest in the term in 2026, too:

But what exactly is an agent harness and what do non-engineering members of product teams need to know about them?

In this Knowledge Series, we’re going to explore what an AI Agent harness is, together with some of the most common harness architectural patterns to understand how they work. Plus, we’ll take a look at how real world companies like Stripe, Microsoft, Square and others are actually using these harness patterns and AI agents to build innovative new internal and external features and capabilities.

Coming up:

• What is an AI Agent harness and why do they matter to product teams?

• Examples of emerging Agent Harness patterns and principles worth knowing

• Agents and harnesses in practice: 20+ real world examples of patterns and features built with AI agents and harnesses from Stripe, Microsoft, OpenAI, Square and others

What is an Agent Harness and why do they matter to product teams?

An agent harness is the scaffolding or framework that surrounds an AI agent to help it work more effectively on complex tasks.

The raw AI model alone often isn’t enough for complex real-world tasks and so a well-designed harness can dramatically improve what the model can achieve. In practice, a harness can typically handle things like:

Read more

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Google has announced that it is officially open-sourcing its DESIGN.md format - a standardized specification originally developed for its own design tool, Stitch.

The spec turns design systems into something “agent-native” and interoperable, with some arguing that it will form the basis of the future of design.

And following the announcement, some have even predicted that it is inevitable that the design role will change fundamentally. One prominent Silicon Valley venture capitalist and former product leader at Google and Meta made a particularly controversial prediction and argued that design is “the first AI casualty”:

In Gokul’s view, larger companies will likely not backfill design roles as product teams use centralized design systems that feed into AI tools to generate UI and prototypes on demand.

As you might imagine, there has been plenty of pushback against this from several folks in the tech community who argue that this is a poor take on how the role of design might evolve. But whether he’s ultimately proven right or not, it’s now clear that design systems and markdown files are rapidly reshaping some of the traditional tools processes that product teams took for granted. And companies like Google and Anthropic are increasingly pushing the format as the de facto way to get the most of out design systems in their own tools.

In this Knowledge Series, we’ll unpack the DESIGN.md format in more detail to bring you up to speed with what it is, how it works and how you can develop your own DESIGN.md system. We’ll also get hands-on with how to use DESIGN.md files in tools like Claude Design and explore some prompts you can use to build your own migration tools and systems from scratch.

Coming up:

• What is DESIGN.md? The potential impact of DESIGN.md on product teams and the software development process

• Hands-on examples of DESIGN.md in action: how you can use it with tools like Claude Design, Cursor and Google Stitch

• Prompts you can use to build your own interactive tools including a DESIGN.md side interactive editor to see the spec come to life in real time

What is DESIGN.md and how does it work?

Here’s a snapshot of what the DESIGN.md is and how it fits into the wider design process:

Put simply, DESIGN.md is a plain-text file that gives your AI agents a structured, persistent understanding of an entire design system. In many ways, it’s almost like a design counterpart to other well established files that are routinely used in software development like README.md files.

And they’re not entirely new. After they were initially released as part of Stitch almost a year ago, the format started to pop-up outside of Google’s own tools. Developers began to write their own versions of DESIGN.md and companies even started to share their own markdown files that designers could use if they wanted to adopt similar design language.

But, Google’s new open-source specification standardizes the format for the first time, outlining a structured approach that product teams can use in their own development process.

We’ll get some hands on experience with a real world DESIGN.md file later but for now, let’s focus on some of the structural decisions the format introduces.

The two layer structure

Every DESIGN.md has two parts living in the same file:

1. A section at the top containing machine-readable design tokens - exact hex codes, font sizes, spacing values, corner radii, and component styles. Written in YAML (a data format that’s similar to JSON).

2. A markdown body below that, containing human-readable rationale explaining why those values exist and how to apply them.

In other words, the file format is designed to allow product teams separate out the “what” from the “why”.

Here’s an example of the top half of the spec which includes values for things like colors and typography:

And here’s an example of the markdown beneath that to provide extra context and detail about how the system is designed:

Why this is a powerful, pragmatic approach to building agentic design systems

The “what” (tokens) is for machines. A coding agent doesn’t need to know that primary is “Golden Retriever orange” evoking joyful energy. It needs a hex code like #855300. A hex code is precise, unambiguous, executable so when an agent generates a button, it pulls the hex, applies it and moves on.

The “why” (prose) is for judgment calls. When a situation arises that the tokens don’t cover which might include things like an edge case, a new component type, an unexpected layout constraint, an agent (or a human) needs context to make a decision that stays on-brand:

or

are instructions that can’t be tokenised but can guide a decision. So in these cases, they fill the gaps and help guide the agent to make design decisions that fit with the wider system.

The potential impact of this on product teams

For product teams, the adoption of DESIGN.md has some potentially significant, practical implications.

Before we look at some hands on examples together, let’s first consider what the potential impact of widespread adoption of AI design systems like this might be.

Read more

This week, OpenAI and Google revealed new workplace Agents - with some use cases that could be particularly helpful for product teams.

Plus, a beautiful new email app from former Pinterest employees reimagines how email might work in the future, Shopify’s CEO teases what he calls the “start of something really big” and new rumors and data paints a muddled picture of what’s really going on inside Google’s AI coding adoption efforts.

Happy Friday and have a great weekend!

Rich

Watch on YouTube | Follow on Substack Notes

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Key reads and resources for product teams

Process - How Anthropic uses Claude in Product Engineering
Software engineer Chuma Kabaghe shows how she uses Claude Code to onboard unfamiliar codebases in minutes, run autonomous testing loops, and manage parallel coding sessions, reducing context switching and shipping faster. (Anthropic)

Tool you can use - A beautiful new Email App from former Pinterest employees
Extra is a new Gmail-only app from ex-Pinterest leaders that replaces traditional inboxes with a “Today” view and smart tabs. it surfaces action items, events, shopping, and news, auto-sorts everything else, and makes bulk unsubscribing and cleanup fast. (Extra)

Knowledge Series - Claude Design Explained
How it works, 5 practical examples and a gallery of 25+ creations from others you can use as inspiration. We’ll test its core workflows hands-on, using Claude’s native design system capabilities to create things like mobile onboarding journeys, landing page prototypes, stakeholder decks and more.

New in the AI Library

• Transition to outcome-based pricing - Use this prompt to design a financial model that forecasts the impact of transitioning to outcome-based pricing

• Identify product feature gaps using your API logs - discover which of your APIs are most popular and translate this into feature gaps you can use to inform your product roadmap

(Department of Product)

Case study - How Microsoft built an AI Assistant that cut sprint planning from 1 hour to 3 minutes
Microsoft’s new AI agents in Azure DevOps are cutting engineering work down to minutes instead of hours. Instead of spending a full sprint planning session manually breaking down work items and writing queries, teams now use AI assistants that handle these repetitive tasks in seconds. (Microsoft)

Resource - Compare every design tool in 2026
Browse and compare over 82 different design tools, all neatly categorized to make it easy to navigate. Includes new entrants like Lovale, v0, Antigravity and Paper as well as traditional classic tools. Handy if you’re looking to augment your product design process with new tools but aren’t sure where to start. (Design Tools fyi)

Interview - Stripe’s head of design on how they reimagined their homepage after 6 years - Stripe’s head of design Katie Dill reveals how they completely reimagined their homepage after six years. She walks through the painstaking design decisions - from wave iterations to bento layouts - and explains how AI is changing her team’s process. And she’s adamant that AI doesn’t replace craft or taste. (YouTube / Y Combinator)

New technologies - Design.MD explained by Google’s David East
Stitch’s DESIGN.md format just went open-source - meaning your design rules can now travel between platforms and projects without starting from scratch. AI agents can understand your brand’s visual language at a deeper level, making smarter choices about color, layout, and accessibility. See how it works in this video, then start building your own DESIGN.md files. (Google)

Strategy - The end of one size fits all software
The software bargain that governed enterprise IT for 30 years is breaking. Generative AI has made custom-built systems fast and cheap to create, meaning companies no longer need to reshape their workflows around standardized tools. Deep Nishar and Nitin Nohria explore how this shifts the real question: not which software to buy, but which workflows you should actually own. (Harvard Business Review)

Opinion - Linear’s CEO on why “Output isn’t design”
New AI tools promise to generate interfaces instantly, but they’re solving the wrong problem, argues Karrr Saarinen. (LinkedIn)

New product features and innovation this week

This week, Google held its annual Cloud Next ‘26 event in Las Vegas. During the event, Google unveiled Workspace Intelligence, an AI system that turns scattered Workspace data into unified context for agentic work. Rather than manually jumping between apps to gather information, the new system automatically understands semantic relationships across your Docs, Slides, Gmail, Chat, and other Workspace apps - plus external tools like Asana, Jira, and Salesforce.

Some of the specific features announced that might be of interest to product teams include:

• A new Sheets canvas that lets you create and share custom interactive visualizations (dashboards, heat maps, kanban‑style boards, mini‑apps) directly on top of your data, rather than static charts

• New infographic capabilities that let you embed data visualizations inside Google Docs

• The ability to create automations with custom-built skills - a packaged workflow that can pull from your Workspace data (Gmail, Drive, Docs, etc.) and execute multi-step tasks on a recurring basis. You build them once, and they run whenever triggered.

In theory, Google now has a structural advantage in work-context retrieval that external assistants can only partially replicate through connectors and integrations. Every user who asks Gemini a question that requires knowing what’s in their inbox is a user who can’t ask Claude the same question with the same quality of answer. But this is only the case if it actually works reliably as intended.

You can read more about all 10 Workspace announcements made at Cloud Next ‘26 here.

OpenAI releases its own workspace agent

OpenAI has released workspace agents - AI assistants that handle complex, ongoing tasks for teams. Powered by Codex, these agents run in the cloud and can execute multi-step workflows across connected tools and systems.

Here’s how they work: You describe a recurring team workflow (like lead qualification or weekly reporting), and ChatGPT guides you through building an agent step-by-step. The agent learns what tools to access, what approvals to request, and how to remember context between tasks. It can operate on schedules, respond to requests in Slack, and integrate with dozens of business tools.

Agents take actual actions (writing code, updating CRMs, sending emails) rather than just providing information. For example, OpenAI’s sales team uses an agent that pulls call notes and account data, qualifies leads, and drafts emails - work that previously took reps 5-6 hours weekly.

Here’s an example of a Product Feedback Agent that reads product feedback from the web, groups it into recurring issues, posts a daily summary to Slack, and creates or updates Linear tickets automatically:

The release coincides with the launch of OpenAI’s newest model, GPT 5.5 which scores 58.6% on software engineering benchmark SWE-Bench Pro - significantly below Claude Opus 4.7’s 64.3% but above Gemini’s 54.2%.

Meanwhile…

Perplexity has released Personal Computer - an expansion of its super successful product Perplexity Computer. Unlike that product which runs in the cloud, Personal Computer runs locally on your machine where it can work across local files, native apps, connectors, and the web in one system. You can also speak to it directly and have it carry out actions on your Mac.

Figma has released a new way to view Figma Make creations in the mobile app. For mobile product teams, this makes testing new prototypes on mobile - with swipes, clicks and other mobile native gestures - easier.

Claude has expanded the number of Connectors you can use to over 200+. New connectors include AllTrails, Audible, Booking.com, Instacart, Intuit Credit Karma, Intuit TurboTax, Resy, Spotify, StubHub, Taskrabbit, Thumbtack, TripAdvisor, Uber, Uber Eats, and Viator, with more on the way. Anthropic says they’ve noticed that users connect multiple apps and use them together in a single conversation with Claude.

For example, a product manager will pull a query from Amplitude, turn it into a Canva deck, and drop the link into Asana for the team, all without leaving the conversation.

Shopify CEO shares a vision of the future web; UI generated on demand

Shopify’s CEO shared this as a glimpse of what the future of the web experience might look like, describing it as “the beginning of something really big”:

Flipbook is an infinite visual browser created by Zain Shah, Eddie Jiao, and Drew Carr that generates pages entirely as images on demand. When you click anything in an image, the system generates a new image exploring that topic in deeper detail. There’s no underlying HTML, code, or pre-built links - just AI-generated pixels that represent information retrieved from web searches and the model’s training knowledge.

For product teams, this could lead to entirely new experiences:

• Tutorial systems that visualize concepts differently based on user interactions

• Data exploration tools that generate custom visualizations on-the-fly

• Learning platforms adapting visual complexity based on topic requirements

It’s a bit slow / clunky at the moment but still a fascinating experimental look at what the future might feel like.

📈 Product data and trends to stay informed

Former Google engineer Steve Yegge claims Google’s internal AI adoption looks like a tractor company’s, not a frontier lab’s. He says the internal usage distribution looks like this: 20% power users, 60% on basic chat tools, 20% refusing entirely.

According to Yegge and his sources, DeepMind uses Claude daily but the rest of the company gets pushed onto internal Gemini products. Demis Hassabis wasn’t impressed and called the numbers “absolute nonsense” but Yegge has responded this week saying internal Googlers corroborated his original claims. Sundar Pichai also revealed that 75% of the company’s new code is now AI generated and so the true picture of what’s actually happening in Google is muddled.

44% of online buyers surveyed by Bain & Company mostly start their journey in an LLM or split their search between AI tools and traditional search engines. The study also found 50% of online shoppers in the survey trust generative AI for initial research and product comparisons.

Google’s latest ad safety report shows a surge in attempts at placing scam ads using AI.

The trend towards outcome-based product pricing continues. This week, Adobe confirmed that it is planning to introduce new pricing for its CX Enterprise AI suite that charges customers not just for usage, but for results - for example, tying fees to the number of ad campaigns or end-to-end “jobs” its AI agents successfully complete, rather than simply how many tokens they consume.

GitHub has paused new Copilot sign-ups and tightened token limits, saying agentic, long‑running AI coding workflows now cost more in compute than fixed monthly fees cover.

OpenAI’s Codex hit four million weekly active users this week, up from three million two weeks prior. A 33% jump in two weeks. OpenAI had two million weekly users just a month ago.

Music streaming service app Deezer says that AI-generated tracks now represent 44% of all daily uploads.

Occupations with more exposure to AI have grown faster than least-exposed ones in the UK.

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Many of the world’s leading product teams are shipping new features faster than ever. So far in April alone, Google has shipped 55 new releases, Notion has shipped 11, and Anthropic has shipped 21.1

Anthropic’s lead engineer, Felix Rieseberg, recently said that the speed of development means their roadmap is now just one month long.

But just because you can ship something doesn’t mean that your users want you to. Personas are a powerful way for product teams to remind themselves of their core user groups.

In this Knowledge Series, we’re going to take that principle and build something that will help you remember who you’re building a product for and why. The Persona Feature Tester will bring your user personas to life, let you describe a feature you’re building, or import it directly from Linear, and run it past your user personas all at once.

This is not meant to replace speaking to real users. Instead, it’s meant to be used as a tool to bring your user personas to life, test your assumptions and think about features from multiple different perspectives in one go.

Here’s what we’ll build together:

The persona feature tester is built using real world data sets and it allows you to describe a new feature or roadmap item and run it past multiple personas - all at once. If you have any further questions, you can call up a user persona and have a conversation:

For our example, we’ll imagine we’re working at a HR SaaS company called Kova but you can tweak this according to your own needs.

The app comes with a bunch of core features built into it including:

• 5 personas built using a mix of different types of real world data

• The ability to describe your feature in text or import issues directly from Linear

• A summary of feedback from each user persona, broken down by what they like and what they don’t

• The ability to call up a user persona and have a conversation with them about the feature you’re planning to build

Personas are pretty divisive with some people arguing that they’re sometimes a novelty that’s not truly reflective of real world users. There is some truth to that, and they’re not a replacement for speaking to real humans, but for this, we’ll make sure each of our personas is fully grounded in real world data sets to try to make them as realistic as possible.

Getting set up

For this tool, we’re going to use a combination of some of the latest products to bring it to life, including: Claude Design, Code and Cowork, Cursor and ElevenLabs.

You’ll also get a downloadable version of the app and all of the examples of the real world data and folder structures that you can copy directly and hook up to your own instance of Claude Code.

There are 3 core parts to building this:

1. Creating our personas grounded in real world data with Claude Cowork and Projects

2. Building the first iteration of the app in Claude Design and Code

3. Hooking it to third party APIs including Linear, ElevenLabs and Anthropic to bring it all to life

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