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by Peter Yang, Behind the Craft Today, I want to share 18 hot takes on where I think the AI market is headed. AI is in a weird place right now. The government is restricting access to frontier models, enterprises are becoming conscious of token costs, and everyone’s trying to rebuild their product for agents first instead of humans. I’ve interviewed dozens of AI leaders and spent far too much time following these topics on X/Twitter. Here are 18 hot takes on where I think AI is headed next: The frontier-only AI stack is collapsing The AI super app era is here Traditional software risks becoming a dumb pipe for agents Cloud agents and collaboration are the next wave The Frontier-Only AI Stack Is Collapsing Tokenmaxxing at frontier API prices makes no sense. Uber burned through its entire 2026 AI budget in 4 months, Microsoft moved engineers off Claude Code due to cost, and companies are realizing that running everything on frontier models can get expensive fast. Tokenmaxxing makes sense when you’re on a subsidized $200/month plan but is unsustainable at API rates. Companies will rely on a portfolio of models. Coinbase recently cut its AI spend nearly in half by switching engineers to Chinese open-source models like GLM and Kimi. Airbnb and Pinterest have done the same with Alibaba’s Qwen models. I believe that this will be the default path forward — using frontier for high-stakes work and cheaper models for everything else. China’s open-source strategy is working. Chinese models are taking market share from frontier models at US companies. China is also building the full AI stack — from energy (e.g., solar, nuclear) to data centers to domestic chips. The Chinese government is planning a $295B investment in AI data centers with at least 80% of the chips built domestically. Frontier labs are in a catch-22 situation. If they release great open-source models, they might undercut their own frontier API revenue. If they gate the best models behind a trusted list, companies

Frameworks like Lean Six Sigma and business process management (BPM) first gained traction because they promised clarity in the chaos—a structured way to bring order to messy, sprawling operations. Lean Six Sigma emphasized statistical rigor and quality control; BPM created end-to-end maps of how work should flow across departments. Both offered a repeatable way to…

When the enemy is in distress, exploit the opportunity to seize advantage. — The 36 Stratagems, Loot a Burning House Who's Leo — In the last story , he was CoreStack's backend lead — the guy who built the core system alone over five years with zero P0 incidents. Then a new CTO named James showed up, spent $8M on his old employer's product, and laid off Leo's entire team. Thirteen days later, that $8M AI system collapsed — three agents fighting over context, OOM taking down six GPU servers, a 37% order duplication rate, and 2,300 customer complaints. Leo pulled the old system off his laptop, flipped one line of Nginx config, and restored service in thirty seconds. The CEO called him at 3 AM begging him to come back. He came back. Three conditions: kill the paid AI product, AI assists only — never touches the primary pipeline — and engineers decide the architecture, not the guy writing checks. The CEO agreed to all of it. So who's Leo now: CoreStack's CTO. Technically confident to the point of arrogance. Zero talent for upward management. No idea how many people he pissed off on the board with those conditions. Doesn't care. He only knows one thing — the system he built is still running. That's all the proof he needs. Then a Slack message cut him off. The Signal 12:47 AM. CoreStack's CTO gets a Slack notification. The account has no profile picture, no display name, no status. Account creation timestamp at the bottom — 00:43. Four minutes old. Seven characters: Check CodeForge's status page. Leo taps it open. CodeForge's status page is all red. Payment Routing — Major Outage. Investigating. All customers affected. Status has been active for approximately 3 hours. He pulls up CoreStack's CRM. The sales team's prospect list has ShopStream at #2 — a potential whale, with "Current Provider" reading CodeForge. E-commerce platform doing 470,000 transactions a day . An hour of downtime costs them $210,000 . If this drags on until morning? He doesn't want to do the math. Core

Early-stage firm Magnify Ventures has raised a $46.6 million Fund II from LPs including Melinda French Gates’ Pivotal Ventures

Insiders say Sam Altman is in active talks with the Trump administration.

FTC urged to reject Elon Musk’s bid to end X monitoring amid AI concerns.

Tesla just released its second-quarter delivery and production report, showing that the automaker is starting to recover after a particularly brutal sales year in 2025. The company said that it produced a total of 451,758 vehicles between April and June of this year, including 442,936 Model 3 and Model Y vehicles, as well as 8,822 […]

Deliveries outstripped production, suggesting Tesla has cleared some inventory.

Artificial intelligence may have captured the public imagination through chatbots and image generators, but some of its most consequential use cases are unfolding far from consumer-facing tools. In industries where physical infrastructure, operational continuity, and safety are paramount, AI is becoming a core operating layer. With its sprawling industrial systems and constant stream of operational…

Originally published on productize.life . Quick answer: pm-skills is a marketplace of around 68 Claude skills for product management across 9 plugins, from strategy and discovery to market research and AI shipping. It is built by Pawel Huryn, author of the Product Compass newsletter. Each skill is not a loose prompt but a named, sourced framework, and one of them audits the gap between documentation and code, a PM lens built for the era of AI-written code. Last week I was reading through a run of repos that pack product work into skills. Some pick one topic and go deep. This one does the opposite: it is the broadest of the bunch. It is called pm-skills, by Pawel Huryn, the author of the Product Compass newsletter. He packs almost the entire product management craft into around 68 skills across 9 plugins, from setting strategy, running discovery, and researching the market, to analyzing data, executing, and shipping software that AI wrote. Usually something this broad ends up shallow. But when I actually opened it, it was not, and one skill in particular made me stop and look for a while , because it covers an angle that only recently became necessary in the era where AI writes code for us. I will tell it in three parts, starting with what it is , then why it is not just a prompt box , and closing with lessons for anyone building products . Terms, gathered once, right here skill a ready-made set of instructions an AI agent (such as Claude Code) can invoke, like a shortcut that wraps one way of doing a task. framework a ready-made way of thinking from the PM world, such as SWOT, JTBD, or RICE, that you once had to read a book to use well. plugin (category) a group of skills that belong to the same topic, such as the discovery category or the go-to-market category. PRD a product spec document that says what will be built, for whom, and how success is measured. Part 1: What pm-skills is It is a marketplace of around 68 Claude skills for PM, organized into 9 plugins, eac

I am working in a research group in Germany, not that well known but in general good output. I have one colleague who does nothing in his PhD. He does not want to work, or he is not able to do any good research, his level is super bad. Plus He doesn’t even care about that. To wrap it up, he is just here for the money. Since he doesn’t want to work or he can’t really do anything good, instead what he does is “paper fishing”, he searches for people in the group doing some good research, and asks that they put his name on the paper. In this case he has something to cover up for him when the professor asks him about his progress. As long as his name is on the paper, progress is checked and funding is renewed. But he actually does nothing. I know this is very unprofessional and unethical. But people tell me it’s normal in academia. Professors all the time put names of their friends and this is how it works in academia. What are your thoughts of this behaviour? submitted by /u/impressivestatus21 [link] [留言]

You built a system to surface information so a person could decide. Somewhere it started deciding for them — the output stopped saying "here's what the documents show" and started saying "you should do X." Nobody designed that drift. An LLM, when asked a question, produces an answer-shaped thing, and an answer easily becomes a verdict. What everyone tries A prompt instruction: "Don't make recommendations." "Only state what's in the documents." People add the line and assume the boundary is enforced. Why it doesn't work A prompt instruction is a request, not a guardrail. The model follows it most of the time, then on the input that matters produces a confident recommendation anyway, because nothing structurally prevents it. "Please don't make recommendations" is to a guardrail what a sticky note saying "please don't enter" is to a locked door. And the stakes are higher than they look. When output drifts from evidence to verdict, accountability moves. As long as the system returns evidence and a human decides, the human owns the decision. The moment the system returns a verdict and the human defers, the system is deciding things it was never validated to decide — and when one is wrong, accountability is a blank. High-stakes fields separate evidence extraction from judgment on purpose; most RAG systems erase that line by default. The one shift Decide what the output is and enforce it structurally. An output should declare itself: answer, evidence, missing facts, or out-of-scope. "Return decision material, not a decision" has to live in the output contract and in gates — not in a polite request to the model. The system supplies frames; the human supplies verdicts. This is the output boundary — one of three places production RAG dies. Read the full version on my blog , where this connects to the RAG Failure Diagnosis Kit for teams debugging production RAG.

Stop Treating Databases Like Dumb Storage! A Modern Approach to Data Layer Optimization Introduction In the rapidly evolving landscape of cloud-native applications, the database often remains the last bastion of outdated architectural thinking. Too many development teams, even in 2026, treat their databases as little more than dumb storage – a simple receptacle for data. This oversight invariably leads to an insidious problem: what was once perceived as a cost-saving cloud server rapidly transforms into an expensive, resource-hungry bottleneck that devours compute cycles, memory, and, most critically, developer sanity. The knee-jerk reaction to performance woes—throwing more hardware at an unoptimized SQL database or poorly designed NoSQL schema—is not scalable backend design; it's procrastination. This approach might temporarily mask symptoms, but it fundamentally ignores the root cause, leading to spiraling costs and increasing technical debt. Modern backend design demands a paradigm shift: treating your data layer as a strategic, highly optimized component rather than a generic storage utility. The path to true scalability, resilience, and cost-efficiency begins with intelligent data management from day one. Architectural Walkthrough: Embracing Smart Data Strategies Instead of "sharding your problems" through reactive, unguided horizontal scaling, embrace smart data partitioning . This isn't just about distributing data; it's about strategically organizing it to align with your application's access patterns and business domains. 1. Smart Data Partitioning & Query Patterns: Imagine an e-commerce application. Instead of sharding all orders data uniformly, consider partitioning by a natural business key, like customer_id or product_category . This ensures that common queries (e.g., "get all orders for customer X") are localized to a single partition, minimizing cross-partition operations. // Conceptual Service for Order Management class OrderService { private final

When going abroad, the right plugs are essential to keep your gadgets charged. These are my favorite travel adapters and chargers.

Google tries balancing AI data center emissions with clean energy efforts.

The Bublue BuVortex V5 ditches conventional skimming for a vortex-powered design that’s fascinating to watch, even if it’s severely impractical.

Slate is the latest automaker to transition to lower-cost batteries perfected in China, driven in part by the repeal of EV tax credits that required materials to be sourced domestically.

I ripped open $892 worth of Pokémon packs on my phone in under 15 minutes and walked away with 62 cents. My adrenaline rush felt like the future of gambling.

Apple chose Google Cloud to run Private Cloud Compute outside its own data centers for the first time, using NVIDIA Blackwell GPUs, Intel TDX, and Google's Titan chip. Apple maintains an independent append-only hardware ledger and dual-vendor attestation roots. AWS and Azure are not part of the collaboration. By Steef-Jan Wiggers

683 Test Files Later: How We Validate AI Agent Wallet Infrastructure Your AI agent can browse the web, write code, and manage files — but can it actually touch money? That's the gap WAIaaS was built to close: a self-hosted, open-source Wallet-as-a-Service that gives your AI agent a real blockchain wallet, a policy engine, and a transaction pipeline it can use autonomously. And before any of that ships to production, it has to pass more than 683 test files. Why Test Coverage Matters for Wallet Infrastructure When your agent sends an email, a bug means a bad email. When your agent sends 0.1 ETH to the wrong address, a bug means lost funds. The stakes are categorically different. This isn't about chasing a coverage number. It's about the fact that wallet infrastructure for AI agents sits at the intersection of two unforgiving domains: financial transactions (irreversible, high-stakes) and autonomous software (runs without human review). If you're building an agent on top of a wallet layer, you need to know that layer has been beaten up extensively before you trust it with real assets. Here's a practical look at what WAIaaS actually tests, and more importantly, what that means for you as a developer building on top of it. The Architecture Under Test WAIaaS is a 15-package monorepo. Each package has its own test suite, and together they cover every layer of the system an AI agent will touch. actions, adapters, admin, cli, core, daemon, desktop-spike, e2e-tests, mcp, openclaw-plugin, push-relay, sdk, shared, skills, wallet-sdk That's 683+ test files spread across packages that include: The transaction pipeline — a 7-stage pipeline covering validate, auth, policy, wait, execute, and confirm The policy engine — 21 policy types and 4 security tiers 45 MCP tools — every tool your Claude or LangChain agent will call 15 DeFi protocol integrations — including Jupiter, Aave v3, Hyperliquid, and more 39 REST API route modules — every endpoint the SDK talks to When you call client.