Daily curation of artificial intelligence, product, and technology. Each edition features an in-depth analysis of a relevant topic and a radar with the most important news of the day.
One story is worth more than the other nine combined this week: an AI supplier cutting off a third-party product overnight. Alongside that came a leadership change at Google DeepMind and another chapter in the price war between models. But the most important lesson is about supplier dependence.
Today’s radar brought a heavy package of new models — Gemini, transcription, multimodal capabilities — but what stayed with me was a launch outside the model world: an agent taking over an entire sales team’s CRM.
Today’s radar brought plenty of new model launches, but another story stayed with me: a multibillion-dollar negotiation that could reshape who controls open AI infrastructure. Let’s get straight to the point.
Today’s radar was full of model launches — GLM-5.3-Flash, Qwen3.8-Flash-Next and the mystery of Ox Alpha solved. But what stayed with me was another, quieter story that is closer to my day-to-day product work.
This week brought plenty of news about chips, pricing and new models, but what held my attention most was an essay about designing freedom for AI agents. I share below why it changed the way I think about automation in credit, followed by the rest of today’s radar.
Today’s radar revolved around money and data: Nvidia backing an open model to rival China, Hugging Face reportedly valued at US$13 billion, and Thomson Reuters showing what it costs — and what it is worth — to build a proprietary model. I picked out what matters for people who work in product.
Today’s radar brought a lesson that applies to every product: being technically best does not guarantee adoption. Many developers consider Claude the most capable model available, yet cheaper alternatives are gaining ground. The day also brought plenty of movement from major players entering new spaces, from enterprise implementation to coding agents.
This week, a study about AI and school tests made me rethink how I read adoption dashboards at work. The rest of the radar ranges from the MCP roadmap to a browser built specifically for agents.
This week I spent some time reviewing the cost of running AI in the products we build, and an OpenAI price cut pulled the thread for today’s edition. The rest of the radar ranges from sub-50ms TTS latency to a billing bug in a coding agent.
The week closes with a number that captures the moment well: AI in product has stopped being an experiment and become an investment line expected to deliver results. Today’s radar ranges from age segmentation in chat to a billing bug in a coding agent.
This week’s radar brought one of those rare moves: direct competitors agreeing on a common standard. The rest of the day followed the same pattern, with payment infrastructure consolidation and increasingly cheaper models.
This was another week full of model launches and price competition, but what stayed with me was a number in the billions changing size. I will focus on that in today’s lead story, and leave the rest of the radar right after it.
I closed out the week watching an acquisition that says more about the AI market than any model launch. And the rest of today’s radar brought an interesting counterpoint: while one company is paying billions for infrastructure, the debate on Hacker News is about whether models are actually improving or merely seeming different.
Today’s radar brought a wave of new models—GLM-5.3, Qwen, Gemini 3.7 Flash—but what stayed with me was something else: how far an AI agent can go on its own before someone has to hold the brake. I set this edition aside to talk about that boundary.
Today’s radar was packed with model launches—Qwen, Gemini, GLM—but what stayed with me was an infrastructure story, not a new model. I set this edition aside to talk about homomorphic encryption and why it matters to anyone working with sensitive data in credit.
Today’s radar brought a wave of model launches—Grok, DeepSeek, Qwen—but what stayed with me was a much simpler discussion: how do we trust AI-generated text? I set today’s edition aside to talk about watermarks and document fraud, because that is what determines whether a financial product can withstand real pressure.
One data point stuck with me this week: 43% of SaaS companies already charge for work performed, not access. The rest of the day brought launch after launch—Gemini reaching 1 billion users, Alibaba with a new model, Meta entering the coding-agent race. But I set today's edition aside to talk about money, because money is what decides what survives.
Today's thread was capital chasing compute. I saw a story that put two worlds I follow closely side by side—structured credit and AI infrastructure—and decided to pull on that thread first. The rest of what passed the filter is below.
The thread today was money moving toward proven results, not promises. I started there and left the rest of what passed the filter below.
The thread today is price: a cheaper model becoming a strategic decision, not just another line item in a spreadsheet. I started with what this changes in the roadmap and left the rest of the radar below.
Today’s radar brought a bit of everything: a cheaper model putting pressure on prices, a real incident involving an autonomous agent that went out of control, and a new way to look at the AI budget. I want to start with the last one.
Today's radar has fewer flashy model launches and more of a calculation that every AI PM will soon need to make: how much does a price increase from your favorite model API weigh on your product?
The data that held my attention today did not come from a model launch. It came from people getting AI-agent approvals wrong. That weighs more on my product work in credit than any new feature of the week.
Today was a day for opening two different drawers. One was price, with OpenAI cutting Luna's entry cost by 80%. The other was people, with Jeff Dean and three other strong names leaving Google on the same day. I started with a cost spreadsheet and ended up looking at a supplier's org chart.
Today's radar was heavy on money: a wallet for an agent, token prices down 80%, and a frontier model fitting on a single GPU. I started the day thinking about spending limits and ended it thinking about unit economics.
Today’s radar was packed with model launches, but the item that stopped me was an essay about measurement. I will start with it, then share the rest of what passed through the filter.
The day brought plenty of news, but one story has stayed on my mind since early on: a major bet being shut down in less than a year. I will start there, then cover the rest of what crossed my radar.
Sunday, ten items on the radar, and one of them stayed on my mind all day. It was not a model launch, but a small change on a settings page. I will start there.
I closed the month by looking back: July was the noisiest month of model launches I can remember. Ten items made today’s radar, but I started with the one that explains all the others.
Ten items are on today’s radar, and one clear thread runs through almost all of them: what used to be an engineering project is becoming an off-the-shelf offering. I started with the launch that changed my priority list the most.
Ten items are on today’s radar, and one challenged an assumption I had been carrying without questioning it. The rest of the list points in the same direction: fewer model announcements, more discussion about how to sustain what has already reached production.
Ten items are on today’s radar, and one changes the economics of several projects that had been stalled. The rest of the list is less about new models and more about how to measure whether the work is actually delivering results.
It is Monday and the radar is already full: ten items filtered, most of them about plumbing rather than capability. New models have become routine. What really changed this week was the layer that determines whether an agent leaves the pilot stage.
Today’s radar was dominated by one theme: what happens after AI enters the delivery pipeline. There are model launches and agent infrastructure becoming a commodity, but what made me pause was the question of why measured productivity does not turn into product delivered.
Today’s radar had one dominant theme: agents are leaving the pilot stage and running into identity, permissions and auditability. I share my take first, followed by everything else that passed the filter.
Today the radar is heavy on costs and agents. GPT-5.6 arrived in three versions in one move, with Grok and Meta launching on the same day, and the result is another drop in the price of running AI. I cover what this changes for people deciding the roadmap and then everything else that passed the filter.
Today the radar is full of model launches, but what caught my attention was not raw capability. It was the strategy behind them. I cover the main story first, then everything else that passed the filter.
I am opening today's edition with a number that matters to any team considering putting AI agents to work for real. Then I cover the rest of what crossed the radar: new models, desktop agents, and a study on chatbot manipulation that is worth reading.
Today's radar is packed with model launches — Kimi K3, LM Studio Bionic, and Step 3.7 Flash, which sparked today's main story. Alongside them, I also picked out a study on dark patterns in chatbots and an account of the fatigue felt by people reviewing agent work. The full radar is worth your time below.
Today's radar brought plenty of model launches — Sonnet 5, GPT-5.6, Kimi K3 — but what held my attention was a quieter discussion: how much can human review still hold together when the volume of agents multiplies? The rest of today's curation is right below.
There were plenty of model launches in today's radar, but something else held my attention: the moment an AI agent stops answering questions and starts planning its own work process. That is where I want to begin.
There were plenty of model launches for today's radar, but what stayed with me was a study about how AI chatbots talk to us. It is worth reading before looking at the rest.
Today's thread was pricing. Sonnet 5, GPT-5.6, and Muse Spark arrived with tiered price tables, while a new report points to where the economics are heading: fewer fixed subscriptions and more outcome-based billing.
It was a busy day on the AI-for-product radar: from an agent buying stock on its own to another inference chip entering the market. But one story tied everything together—agent autonomy making its own decisions with real money on the line.
Today brought a new model with almost every piece of news, but the one that caught my attention most was the smallest of all. A model that runs directly in your pocket without relying on the cloud. I have made it the focus of this edition, followed by the rest of what I filtered below.
This week brought a new model launch almost every day, but what caught my attention most was not a benchmark. It was a simple example of a B2B product replacing a complex screen with a conversation. I have captured it below, together with the rest of today's filtered radar.
Today's radar brought the usual: a model launch, a delayed launch, and another AI feature rolled back over privacy. But the item that held my attention most was not about parameters or benchmarks. It was about how to organize work when part of it is done by an agent.
Today brought a large wave of model launches, but what stayed with me was a quieter move: putting an agent inside the product people already use instead of creating yet another new screen. That is the thread running through today's edition.
Today, some people were cutting AI costs in half while others were being sued for stealing its secrets. It is almost a perfect summary of the phase we are in: everything is moving fast, including the disputes.
Today’s radar was packed with major model launches — GPT-5.6, ChatGPT Work, and Muse Spark 1.1. But what really caught my attention was a much quieter benchmark about accounting.
This week had more frontier model launches than you can properly keep up with — Grok 4.5, GPT-Live, GPT-5.6. But what kept rattling around in my head today wasn't any of those. It was a much simpler question about price.
Today's radar brought a bit of everything: a frontier model on the way, Meta going strong into generative media, and a security alert every agentic product PM should read. But what caught my attention most was an operations case, not a new model.
Today's radar was lighter on launches and heavier on real data about what AI is doing to the labor market. And the number I picked for today's highlight is worth reading in full.
Today's throughline was clear: in AI, the model is rarely the bottleneck. Microsoft just bet billions on this thesis, and much of the rest of today's radar confirms the same pattern, from agent costs to governance.
Today's radar came packed with contradictory signals: on one side, real data on the cost of scaling coding agents; on the other, reports of regression in the very tools we use every day. It paints a picture of a moment where technology moves fast, but not always forward.