The AI podcast for product teams

Let’s stop pretending. Most AI strategies are just a collection of pilots that nobody had the courage to kill. The data this period is brutal: 95% of genAI pilots stall. Only 11% reach production in financial services. Microsoft — the biggest company in the world, with the best distribution on the planet — just reorganized Copilot because nobody internally could agree on what it was supposed to be. And while enterprises burn cycles debating governance frameworks, a new class of startups is quietly replacing entire job functions. Not assisting. Replacing. The gap between the people who get this and everyone else isn’t a skills gap. It’s a courage gap. This edition is about which side you’re on.What You’ll Learn in This EditionThis edition confronts the uncomfortable reality that most AI investments are producing demos, not outcomes — and the structural reasons why.* 🎙 Why agents are automating your thinking, not just your tasks — and why that distinction matters more than any model release* ✍️ Copilot’s identity crisis is the most important product failure of 2026 so far* 👉 The single variable that predicts AI maturity 7x better than technology choices* 1️⃣ Why advertising AI use is now a financial liability for professional services firms* 2️⃣ The inference cost crisis that threatens every AI business model — including OpenAI’sEpisode 4: The Era of Agents — Your Cognition Is the Product NowWe mapped three years of AI evolution in this episode and landed somewhere uncomfortable. Era one gave us wrong answers. Era two gave us wrong context. Era three — agents — is giving us wrong actions. And the stakes compound with each era because AI is no longer just saying things. It’s doing things.Brittany brought the number that should haunt every product leader: only 6% of organizations have fully deployed any kind of agent. Copilot hit 30% weekly active usage after six months — meaning 70% of enterprise users basically stopped opening it. The tools are moving at an extraordinary pace. Almost nobody is keeping up.We profiled four startups winning the point-solution war that most people haven’t heard of. But the real conversation was about what happens when you hand your thinking to an agent. Not your typing. Not your scheduling. Your thinking — the research, the monitoring, the analysis, the synthesis. Something changes in you when you do that. And most people haven’t reckoned with what that means.“We’ve trained generations of people to think linearly. Step one, step two, step three, fill out this form, follow this process. Agents don’t work like that. Agents require you to think in terms of outcomes, connections, and context.” — ArpyListen now: Spotify | Apple Podcasts | YouTubeYou’re invited to join the AI Strategy Experiments Zoom call todayToday (March 27) at 1pm ET we’re hosting a small group of strategists and builders and designers sharing their experiments and questions. Register here.$490 billion in enterprise AI spending is delivering nothing. That’s not a technology failure. It’s a value creation failure. AI Value Acceleration exists to close that gap — diagnosing where AI value stalls and building playbooks that actually work. Value Assessment in 3 weeks. Value Amplification to go deep. Value Acceleration to prove what works. aivalueacceleration.comCopilot Didn’t Fail. It Succeeded at Not Knowing What It Is.Bloomberg reported that internal confusion over Copilot’s role, personality, and strategy has prompted a reorganization at Microsoft.Read that again. Internal confusion. Not external competition. Not technical limitations. The people building Copilot couldn’t agree on what it was for. Microsoft had everything a product could dream of — billions in funding, integration into every Office app, the largest enterprise distribution network on earth, and access to the most powerful models available. It didn’t matter. Without a clear product identity, all that distribution just delivered confusion at scale.The uncomfortable truth: most AI products shipping today have the same disease. They’re a bundle of capabilities searching for a purpose. They demo beautifully. They onboard poorly. They get abandoned quietly. If the biggest company in the world can’t brute-force its way to product-market fit for an AI assistant, what makes you think your team can skip the hard work of defining what your AI product is actually for?BCG: Why Usage Is Up but Impact Is NotEmployee-centric organizations are 7x more likely to be AI mature. Not 7% more likely. Seven times. Employee-centricity explains ~36% of variance in AI maturity outcomes. Model selection explains almost none of it.Over 85% of organizations remain stuck at basic task assistance. Fewer than 10% have reached anything resembling semiautonomous collaboration. The teams pulling ahead didn’t start with better tools. They started with cultures where people felt safe to experiment, fail, and teach each other what they learned. HBR confirmed it separately: peer influence is the single most powerful predictor of AI adoption. When learning stays private, adoption stalls.This is exactly why I built AI Value Acceleration — because the gap between what AI can do and what your organization is actually doing with it isn’t a technology gap. It’s a leadership gap. And closing it starts with measuring where value is being created, lost, and why.Deloitte Put a Price Tag on Hallucinations. Then KPMG Made It Worse.Deloitte issued a refund to the Australian government after errors in an AI-generated report. That’s a sentence that should terrify every professional services firm shipping AI-assisted work without rigorous review. But the follow-up is even more revealing: a competitor reportedly pushed KPMG to cut prices specifically because KPMG advertised AI use.Think about that. Advertising AI didn’t increase perceived value. It decreased it. Clients heard “we use AI” and thought “then why am I paying you full price?” This is a new failure mode that nobody war-gamed: AI claims eroding the very premium they were supposed to justify. Every consulting firm, agency, and services company racing to slap “AI-powered” on their pitch decks needs to answer one question first — does your client believe they’re paying for AI’s work or yours? Because if it’s AI’s work, they’ll expect AI prices.Product Impact ResourcesEvery resource this period points to the same conclusion: the companies pulling ahead aren’t chasing model releases. They’re building the structural layers — verification, governance, integration depth — that turn capability into production value. Everyone else is just running demos.* The moat is the verification layer, not the model. Wolters Kluwer is grounding agents in proprietary knowledge graphs and allowing third-party queries via MCP for usage-based monetization. They’re not competing on intelligence. They’re competing on trust. This is the playbook for every company sitting on domain-specific data. Wolters Kluwer’s “System of Action” Strategy* OpenAI’s real crisis isn’t competition. It’s unit economics. ~$5B loss on $3.7B revenue, with inference costs as the bottleneck. An IEEE-accepted paper highlights inference — not training — as the existential threat. Every company building on top of frontier models needs to understand: the model works. Serving it profitably doesn’t. The Inference Cost Crisis* 70% of AI startups are wrappers. Investors are done pretending otherwise. Atoms AI Accelerator rejected 70% of applicants for lacking workflow depth or proprietary data. Google and Accel are doing the same. If your product is a chat interface over someone else’s model, you don’t have a company. You have a feature. Wrapper Rejection Is Now Institutional* 95% of genAI pilots stall. The bottleneck is governance, not capability. Only 11% of pilots reach production in financial services. Integration complexity (58%), data gaps (47%), and unclear ROI (43%) outrank talent scarcity. The model isn’t the problem. The organization is. Why Pilots Die* Non-human identities outnumber humans 82:1 in enterprises. That’s the attack surface for every production agent. 62% of practitioners cite security as the primary challenge. Until we solve agent authorization, most agentic AI stays in demo mode. The Authorization Gap* Karpathy says 80% of his code is AI-written. The junior developers are paying the price. Entry-level engineering roles are shrinking. The PM role is evolving from translator to system architect. The skill that matters now is task decomposition and rigorous review of AI outputs — not writing code. If you’re not rethinking your hiring pipeline around this, you’re already behind. The Agentic Engineering Shift* UX is the last moat — and most teams are cutting it. NNGroup found AI matches human UX work only 44% of the time. Trust is now the dominant design problem. The teams cutting UX researchers to fund AI engineers are creating the blind spots that will kill their products. Designers’ durable advantage lives in judgment and the “messy middle” — the part AI can’t touch. Why Designers Survive the Agent EraProduct Impact NewsThe headlines this period share a pattern: AI claims without evidence are becoming legally, financially, and organizationally dangerous. The era of “just say AI” is over.* monday.com is getting sued for saying the word “AI” too confidently. They withdrew a $1.8B 2027 revenue target, triggering a 20.8% stock drop and a securities lawsuit alleging misleading AI investment statements. This is the new risk: AI-driven projections without verifiable metrics are now a securities liability. monday.com’s Legal Reckoning* Crypto.com spent $70M on AI.com, then fired 12% of its workforce. The company framed cuts as eliminating roles that “do not adapt in our new world.” That’s not transformation. That’s using AI as cover for layoffs. When the narrative outruns the execution by this much, the credibility damage is permanent. AI-Washing Has Consequences* GPT-5.2 is tiered now. Your CIO wants GPUs back on-premises. Three tiers — Instant, Thinking, Pro — plus MCP enterprise connectors. But the real story is CIOs pulling compute back in-house for data sovereignty, favoring open-weights models over cloud APIs. The frontier model race matters less when the enterprise won’t send its data to it. The Sovereignty Shift* Cove AI built something promising. Microsoft swallowed it whole. The entire team was acquired and the product shut down. An AI collaboration platform — infinite whiteboard, AI-powered structured outputs — vanished into Copilot. If you’re building an AI startup adjacent to a platform company’s roadmap, this is your future. Platform Gravity* Singapore just made governance a design requirement, not an afterthought. MAS released the AI Risk Management Handbook (Project MindForge), formalizing governance from design-time. Four pillars that integrate legal, ethical, and governance into AI products from inception. Every other jurisdiction is watching. Governance at Inception* AI made your job harder, not easier. The data proves it. Post-AI adoption, email time rose 104% and chat time 145%. 14% report significant cognitive overload. Roles are becoming more complex, not simpler. And 66% of CEOs are freezing hiring while this happens. The promise was efficiency. The reality is intensity. The Work Intensification ProblemKey TakeawaysThe uncomfortable pattern across every signal this period: the organizations failing with AI aren’t failing because the technology doesn’t work. They’re failing because they skipped the structural work that makes technology useful — clear product identity, governance readiness, cultural safety, and honest measurement. The ones succeeding did that work first.* Your AI product’s biggest risk isn’t a competitor. It’s not knowing what it is. Copilot’s reorg is proof that distribution without identity produces abandonment at scale. Before you ship to everyone, answer the question Microsoft couldn’t: what is this product for, specifically, and how will someone’s week be different because of it?* If you’re still chasing model upgrades, you’re optimizing the wrong layer. The decisive variables are governance (95% of pilots stall without it), culture (7x AI maturity for employee-centric orgs), and integration depth (verification beats capability). The model is the easiest part of the stack.* The people pulling ahead aren’t smarter. They’re more honest about how they work. Agents demand a skill most professionals have never developed: describing what you actually do, clearly enough for a system to do it. That’s not a technology skill. It’s a self-awareness skill. And until you build it, every agent you deploy will amplify your worst habits instead of your best thinking.Check Out Recent EpisodesEpisode 3: Context Is the New Moat — Why Your AI Needs Business Knowledge — Juan Sequeda, Principal Researcher at ServiceNow, explains why RAG was always a workaround for a deeper problem: your AI doesn’t understand your business. The three-layer framework for semantic context that separates the teams compounding value from those still stuck in pilot purgatory.Episode 2: Vibe Coding Changed Everything — Here’s What Comes Next — We sat down with Yoni Jozwiak, founder of Base44 ($80M revenue in 6 months), to unpack the defensibility crisis facing every AI startup. If anyone can build software by describing it, what’s actually defensible?Episode 1: Why Your AI Metrics Are Lying to You — The framework for measuring AI product impact that most teams are getting wrong. Completion metrics hide signals that matter. Success ≠ satisfaction. The Power/Speed/Impact/Joy bullseye that changes how you evaluate everything.AI Strategy Jobs* Staff AI Product Designer, Mobile, GeminiApp — Google DeepMind (Walla Walla, WA — Hybrid)* Lead AI Product Designer, IRIS — OVERJET (San Mateo, CA — Hybrid)* Senior AI Product Manager — JPMorganChase (London, UK — On-site)* AI Product Manager — Carrum Health (Chicago, IL — Remote)* AI Product Manager — Nimber (Porto, Portugal — Remote)* Senior AI Product Manager — Kaizen Gaming (Athens, Greece — Hybrid)* Principal AI Product Manager — Eaton (Dublin, Ireland — Hybrid)* VP AI Strategy — Prime Therapeutics (Atlanta, GA — Remote)Your AI product demos well but can’t stick, scale, or justify cost? That gap between capability and value isn’t going to close itself. PH1 has spent 14 years helping product teams prove impact — from measuring what AI products actually deliver to improving the performance of LLM-powered experiences to defining AI vision that survives contact with real users. If the evidence in this edition makes you nervous about your own AI strategy, that’s the right reaction. Let’s talk about it.Thank You for Supporting the Product Impact PodcastThis newsletter exists because you keep showing up, sharing what resonates, and pushing back when we get it wrong. That feedback loop is what makes this work. If this edition landed — forward it to someone who’s building with AI and needs to hear the parts nobody else is saying. And if you haven’t caught up on the full season, browse all episodes at productimpactpod.com.Thanks for reading Product Impact | AI Strategy, Value Creation, AI UX! This post is public so feel free to share it. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

Every influencer is drooling over Claude Code skills files. Every product team is chasing the next model release. But for two years, the data has been screaming the same thing: capability isn’t the bottleneck. Context is. This edition unpacks what that actually means — why structured business knowledge is the highest-leverage investment a product team can make, what the “context wars” look like from the inside, and why the teams winning aren’t the ones with the best models. They’re the ones whose AI actually understands their business.What You’ll Learn in This EditionThis edition confronts the structural reason most AI products fail — they’re missing the context that makes capability useful.* Why Juan Sequeda from ServiceNow says “hope is not a strategy” — and what to build instead of better prompts* The three-layer knowledge framework that gives AI a shared language across your entire organization* CNBC’s “silent failure at scale” investigation reveals why 91% of AI models degrade without anyone noticing* Microsoft just adopted ontology — the same concept Juan has championed for 20 years — as the foundation of its agentic AI architecture* Citadel Securities data shows software engineer job postings rising 11% YoY despite the displacement narrativeEpisode 3: Context Is the New Moat — Why Your AI Needs Business Knowledge, Not Better PromptsEvery influencer is drooling over skills files and prompt templates. Juan Sequeda, Principal Scientist at data.world (acquired by ServiceNow), has spent 20 years proving that none of it works without structured business knowledge underneath. In this episode, Juan breaks down the three-layer framework — business metadata, technical metadata, and the mapping layer that creates real semantics — and explains why the teams investing in ontology today will compound value across every AI use case they build next. His blunt assessment of skills files as a production strategy: “Hope is an interesting strategy. It’s not one that I add to my strategy.”“If you just edit in skills, I don’t think that’s gonna be the solution to your problem. You’ll have a great POC. It’ll work for the use cases you tested on. Are you willing to put your career on the line and put that in production?” — Juan SequedaListen on Spotify | Apple Podcasts | YouTubeContext isn’t a nice-to-have. It’s the architecture layer that determines whether your AI product delivers consistent, measurable value or drifts into silent failure. PH1 built this framework to illustrate what Juan Sequeda has been researching for two decades: intent, background, examples, and templates aren’t prompt engineering tricks — they’re the structural foundation that transforms an AI system from a “forever intern” into a strategic partner. Without them, you’re hoping the model figures out what “order” means in your business. Hope, as Juan puts it, is not a strategy.RAG Was the Answer. Now It’s a Symptom of the Real Problem.RAG dominated for two years as the default way to give LLMs context. But as context windows expanded from 8K to a million tokens, the question shifted. This video breaks down when RAG still matters — vast, dynamic datasets and cost efficiency — and when long context windows make the retrieval layer unnecessary. The strategic implication for product teams: RAG was always a workaround for a deeper problem. The real question was never “how do I retrieve the right document?” It was “does my system actually understand my business?” That’s the context layer Juan Sequeda is building — and it sits beneath RAG, long context, and every other implementation detail.In spite of the displacement signals, software engineer job postings are up 11% year over year. But read the fine print: a posting titled “Software Engineer” increasingly means “engineer who can operate LLMs in production” or “build RAG pipelines.” The title stayed the same — the job changed. If your team hasn’t redefined what “engineering” means in the context of AI-augmented workflows, you’re hiring for yesterday’s role.Product Impact ResourcesThe pattern across these resources is consistent: the teams pulling ahead are the ones investing in context, knowledge, and governance infrastructure — not chasing the next model release. Capability is table stakes. The moat is how deeply your product understands the business it serves.* Gartner predicts 80% of enterprises pursuing AI will use knowledge graphs by 2026 to enhance context and reasoning. The shift from “better prompts” to “structured knowledge” is no longer theoretical. The Role of Knowledge Graphs in Building Agentic AI Systems* Microsoft adopted ontology as the foundation of its agentic AI architecture — Fabric IQ, Foundry IQ, and Work IQ create a semantic layer that gives agents shared business understanding. Microsoft Adopts Ontology-Based IQ Layer for Agentic AI* Nathan Lasnoski argues that enterprise knowledge graphs are the foundation for moving from vibe coding to scalable agentic development — without semantic grounding, agents can’t reason across systems. Building an Enterprise Knowledge Graph for the SDLC* HBR analysis reveals AI adoption stalls because of employee anxiety about relevance and identity — not technical limitations. The behavioral barriers are harder than the technical ones. Why AI Adoption Stalls, According to Industry Data* WEF data shows organizations with strong governance and >5% IT budget allocated to AI see 70-75% positive outcomes vs. 50-55% without. Governance is infrastructure, not a bottleneck. Strong AI Governance Is a Business Advantage, Not a Bottleneck* Deloitte’s agentic AI strategy report calls for governance and observability as first-class product features — agentic systems should expose provenance, tool-call traces, and policy decisions by default. Agentic AI Strategy* Teresa Torres warns that AI without product discovery just means “shipping the wrong stuff faster.” The line lands directly on this edition’s thesis — capability without context is an accelerant of bad decisions, not good ones. Strong potential guest. Shipping the Wrong Stuff Faster * Roger Wong unpacks Jenny Wen’s (Anthropic Head of Design) “ship fast, iterate publicly, build trust through speed” approach as a new design paradigm for AI products. Jenny Wen is a compelling guest lead given her role building Claude’s product experience. The Design Process Is Dead * Meta’s alignment director had an OpenClaw agent start rapidly deleting her inbox — she thought it would confirm first. It didn’t. She ran to a Mac mini “like I was defusing a bomb.” Stuart Winter-Tear’s breakdown is a vivid, concrete case study of agentic AI failure in practice. Human in the Loop Is a Job * Academic paper in Communications Psychology (Nature) argues that friction in AI design is a feature, not a bug — challenging the default “make it seamless” paradigm. Co-authors from U of T, Wharton, and Yale. Emily Zohar is a strong potential guest with a contrarian take that plays well on the podcast. Against Frictionless AI Product Impact NewsThe news this edition reinforces a single uncomfortable truth: the biggest AI failures aren’t technical — they’re contextual. Systems that lack business knowledge don’t crash dramatically. They drift silently, producing outputs that look right but are wrong in ways no telemetry catches.* CNBC investigated “silent failure at scale” — a beverage manufacturer’s AI ordered thousands of excess cans because it couldn’t contextualize new holiday labels. 91% of ML models degrade over time, and most enterprises never detect it. ‘Silent Failure at Scale’: The AI Risk That Can Tip the Business World Into Disorder* Agentic AI’s dominant failure mode isn’t catastrophic breakdown — it’s silent drift. CIO reports that only 6% of organizations have fully deployed agents, and the Cloud Security Alliance now classifies cognitive degradation as systemic risk. Agentic AI Systems Don’t Fail Suddenly — They Drift Over Time* Gartner predicts 40% of agentic AI projects will be scrapped by 2027. 90% of legacy agents fail within weeks. The primary driver is governance, not technology. Why 40% of Agentic AI Projects Will Fail* Internal Microsoft data shows only 30% of Copilot enterprise licenses see weekly active usage after 6 months — despite unmatched distribution through Office. Workflow friction and unclear ROI are the barriers. Microsoft Copilot Adoption Stalls at 30% Active Usage* Virtana surveyed 350+ senior IT leaders this month: 75% of enterprises report double-digit AI job failure rates, a third exceed 25%. Meanwhile, 59% of executives think they’re prepared — but 62% of practitioners report fragmented systems and visibility gaps. The disconnect is the risk. 75% of Enterprises Report Double-Digit AI Failure Rates* Citadel Securities rebuts the AI displacement narrative with data: software engineer postings up 11% YoY. But job postings requiring AI literacy grew 70% YoY — the title stayed the same, the job changed. Software Engineer Job Postings Are ‘Rapidly Rising’* Tech Mahindra and Microsoft launched an ontology-driven agentic AI platform for telecoms — the first major enterprise deployment built on Microsoft’s Fabric IQ semantic layer. The context wars are real. Tech Mahindra and Microsoft Launch Ontology-Driven Agentic AI PlatformKey takeawaysThe throughline is unmistakable: the AI products failing at scale aren’t missing capability — they’re missing context. From CNBC’s investigation into silent failures to Microsoft betting its entire agentic architecture on ontology, the market is converging on what Juan Sequeda has been saying for 20 years: structured business knowledge is the highest-leverage investment you can make.* Context is infrastructure, not a feature. Skills files and prompt templates are band-aids. The teams compounding value across AI use cases are the ones that defined “what does order mean?” before they shipped anything. If your AI can’t disambiguate your business terminology, it can’t deliver consistent results.* Governance accelerates adoption. The WEF data is clear: organizations with strong AI governance see 20 percentage points higher positive outcomes. Governance isn’t the thing slowing you down — the absence of it is why 40% of agentic projects get scrapped.* The job didn’t disappear — it transformed. Software engineer postings are up 11%, but the role now requires AI literacy. The same is true for product managers, designers, and strategists. The question isn’t whether AI will replace you. It’s whether you’ll invest in the context that makes AI actually useful.Check Out Recent EpisodesEpisode 2: Defensibility > Capability — Five Actions to Defend Your Product Value $73.6 billion went into GenAI startups in 2025, but 85% of AI startups will be out of business within three years. This episode tackles the economics of abundance and delivers five specific actions to redirect investment toward what actually survives: workflow depth, outcome visibility, and trust engineering. If you’re competing on features, you’re already exposed.Episode 1: Why Your AI Metrics Are Lying to You The bullseye framework for AI products — Power, Speed, Impact, and Joy. Most teams are measuring Power and calling it success. This episode introduces a three-layer evaluation approach and shows why completion metrics hide the signals that actually matter for growth.AI Strategy Jobs* Staff Product Designer, AI Workflows — ServiceNow (Remote/Hybrid)* AI Product Manager — ServiceNow (Remote)* Product Designer, ChatGPT — OpenAI (San Francisco)* Product Designer, Platform & Tools — OpenAI (San Francisco)* AI Product Manager, Strategic Roadmap — IDC (Remote)* Principal Product Manager, AI Personalization — Cedar (New York)* Senior Product Designer, Generative AI — Coda (Remote)* Product Designer, AI Agents — Simular (Palo Alto)* Director, Product Design, AI Transformation — Element AI (Santa Clara, CA — On-site, 65% travel)* Product Designer — Fidelity (Merrimack, NH / Jersey City, NJ / Westlake, TX — Hybrid)If your AI product demos well but can’t prove it drives value in production, that’s a context problem — and it’s the gap PH1 closes. We help teams build the measurement and knowledge infrastructure that turns AI capability into measurable business impact. From defining what success means to proving it with data. ph1.caThank you for supporting the Product Impact PodcastEvery episode goes deeper than the headlines to uncover what actually drives AI product success — and what’s quietly killing it. If Juan’s take on context and ontology challenged how you think about your AI product’s foundation, share this episode with your team. Follow the show so you never miss one. That’s how we grow this community of builders who refuse to settle for capability without impact.Browse all episodes at productimpactpod.com — filter by topic to find the episode that fits what you’re working on right now. We’re at 56 episodes across the two seasons. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

AI products are shipping faster than ever. But shipping isn’t impact. The teams pulling ahead aren’t the ones with the best models — they’re the ones who can prove their product moves the business. This edition is about that gap. How to measure what matters, where the biggest barriers to impact are hiding, and what the latest research says about getting AI products to actually drive growth. Because the real competitive advantage isn’t AI. It’s knowing whether your AI is working.What You’ll Learn in This EditionThis edition cuts through the noise to focus on the measurement gap — the difference between shipping AI and proving AI drives growth.* The Power/Speed/Impact/Joy bullseye — a calibration framework for AI products that actually drive growth* A Nature paper reveals why removing friction from AI may be destroying the learning your team needs* John Maeda on why design teams are being hollowed out — and why PMs are next* Benedict Evans on why even OpenAI can’t solve product-market fit with capability alone* Research that should change how your team thinks about AI-assisted skill buildingThanks for reading Product Impact | AI Strategy, Value Creation, AI UX! This post is public so feel free to share it.Episode 1: Why Your AI Metrics Are Lying to You - Framework for improving AI product performanceYour AI product might be fast, capable, and technically impressive — and still not drive the growth your business needs. In this episode, Brittany Hobbs and I introduce the Power, Speed, Impact, and Joy bullseye — a calibration framework borrowed from F1 racing. The teams winning aren’t shipping more features. They’re measuring different things entirely. We break down a three-layer eval approach and why most completion metrics are hiding the signals that matter.“Success does not mean satisfaction. If someone stops engaging, does that mean they solved their problem — or that they were frustrated and left?” — Brittany HobbsListen on Spotify | Apple Podcasts | YouTubeYour Role Isn’t Shrinking. It’s Being Hollowed Out.John Maeda — Three major tech companies have restructured design teams into “prompt engineering pods.” Maeda’s #DesignInTech 2026 calls it what it is: the elimination of design judgment from the product process. “When you replace a designer with a prompt, you don’t lose the pixels. You lose the questions that should have been asked before anyone opened a tool.” This applies to product managers too — if your PM’s job becomes prompt-wrangling instead of deciding what to build and why, you’ve automated the wrong layer. The roles aren’t disappearing. The judgment inside them is.Featured Resource: Strategy for Measuring & Improving AI ProductsThe gap between what AI products ship and what they prove is where growth stalls. This framework moves teams from tracking activity — token counts, completion rates, session length — to defining and measuring the outcomes that actually drive business impact. Most teams ship features and assume engagement means success. It doesn’t. If your team can’t answer “is this AI feature making the business better?” with data, you’re flying blind. The framework covers product discovery through scale, with concrete steps for building measurement into your AI product from the start — not bolting it on after launch.Read the full resource at ph1.caWaterfall: we’ll build you a car in 18 months. Agile: here’s a skateboard, we’ll iterate. AI: here’s a photorealistic render of a Lamborghini that doesn’t start. We’ve never made it easier to build something that looks incredible and does absolutely nothing. AI development doesn’t need more iteration — it needs someone asking “does this thing actually drive?”If your team is celebrating demos instead of outcomes, you’re already behind the teams that measure first and ship second.Two years of capability gains. Almost no reliability improvement. This is the chart that should be on every product team’s wall — because it explains why your AI demos brilliantly and fails in production. Capability without reliability isn’t a product. It’s a liability.If your team can’t name which type of AI they’re building, they can’t measure whether it’s working. Six categories that force precision. — Narain JashanmalProduct Impact ResourcesThe resources in this edition make one thing clear: the teams investing in measurement and deliberate friction are pulling ahead, while the ones chasing capability are stalling. These resources challenge the assumption that faster and more capable automatically means better outcomes.* Removing struggle from AI workflows destroys the learning that builds expertise. Teams should audit which friction to keep and which to cut. Against Frictionless AI — Inzlicht & Bloom in Nature* AI users learned 17% less without any efficiency gains. How your team uses AI matters more than whether they use it. How AI Impacts Skill Formation — Shen & Tamkin RCT* Two years of capability gains with only modest reliability improvement. The barrier to growth isn’t what models can do — it’s whether you can trust them. The Capability-Reliability Gap — Narayanan et al.* Polished AI outputs reduce critical evaluation by users. Build in friction points that force your team to think before accepting. (Anthropic studying its own product — read accordingly.) Anthropic AI Fluency Index* AI forces strategic clarity because you cannot delegate logic you haven’t articulated. That’s a feature, not a bug. Strategy as Protocol — Schwarzmann via Scaman* Six functional AI categories that sharpen how teams talk about what they’re building. Precision in language is precision in product decisions. AI Taxonomy — Jashanmal* Mapping 50 AI startups across six pricing models reveals that pricing is a product decision, not a finance one. Get it wrong and adoption stalls regardless of quality. How to Price AI Products — Gupta* Wade Foster shut Zapier down for a week-long AI hackathon. Adoption went from 10% to 50% in five days. Adoption follows experience, not mandates. Zapier’s Code Red HackathonProduct Impact NewsThis is the news that matters. Reliability failures are making headlines, benchmark credibility is collapsing, and even the market leaders can’t prove product-market fit. The gap between what AI can do and what it can prove is widening, not closing.* ChatGPT missed diabetic ketoacidosis and respiratory failure in 52% of emergency cases. Suicide-risk alerts fired inconsistently. Reliability is the product, not a feature to ship later. ChatGPT Health Under-Triaged 52% of Emergencies* LLMs chose nuclear strikes in 95% of simulated crises. The nuclear taboo is no impediment to AI escalation — a stark reminder that evaluation stakes extend beyond product. AI Models Chose Nuclear Strikes in 95% of Simulated Crises* Google patent US12536233B1 lets it generate its own landing page from your product feed if yours scores below threshold. Own your experience or someone else will. Google Patented AI Landing Pages That Replace Your Storefront* 84% of the world has never used AI. Only 0.3% pay for it. The growth opportunity is massive — but only for teams that solve adoption, not just access. 84% of the World Has Never Used AI* 80% of ChatGPT users sent fewer than 1,000 messages in 2025. Even the market leader hasn’t solved product-market fit. Capability alone isn’t enough. OpenAI Has No Moat and Engagement an Inch Deep* RCT shows AI tools made experienced developers work faster and take on broader tasks — without measurable output gains. Speed is not productivity. METR: Experienced Devs Saw Zero Productivity Gain* NIST finds standard benchmarks conflate different performance measures. Models with different scores may perform identically in production. Build your own evals. NIST: AI Benchmarks Don’t Measure What They Claim* MIT reviewed 300+ AI implementations: 85% failed, 91% of models degrade silently. The 5% that succeeded built measurement into the product from day one. 85% of AI Projects Fail, 91% of Models Degrade SilentlyKey takeawaysThe throughline across this edition is unmistakable: capability without measurement is theater. From the METR study showing zero productivity gains for experienced developers to MIT’s finding that 85% of AI projects fail, the evidence converges on one point — the teams that win are the ones that prove their AI works.* Measure outcomes, not activity. Completion rates, token counts, and session length tell you your AI is running — not that it’s working. Define what “working” means for your business before you ship.* Protect judgment. Automate everything else. The roles being hollowed out aren’t the ones doing rote work — they’re the ones asking the hard questions. If you’re automating decisions instead of tasks, you’re cutting the wrong layer.* Friction is a feature. Research consistently shows that removing struggle from AI workflows destroys learning and degrades skill. Build in the friction that keeps your team sharp, and strip out the friction that just wastes time.If your AI product ships well but you can’t prove it drives growth, that’s the gap PH1 closes. We help teams define what success looks like for AI experiences and build the measurement systems to prove it — from product discovery through scale. ph1.caThank you for supporting the Product Impact PodcastEvery episode tackles the gap between what AI products promise and what they actually deliver. Brittany and I bring in the builders, researchers, and leaders who are closing that gap — with frameworks, evidence, and hard-won lessons. If an episode shifted how you think about your product, share it. Follow the show so you never miss one. That’s how we grow this community.* Episode 1: Why Your AI Metrics Are Lying to You* Vibe Coding Will Disrupt Product — Base44’s Path to $80M* AI Trap: Hard Truths About the Job MarketBrowse all episodes at productimpactpod.com — filter by topic to find the episode that fits what you’re working on right now. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

AI was supposed to help humans think better, decide better, and operate with more agency. Instead, many of us feel slower, less confident, and strangely replaceable.In this episode of Design of AI, we interviewed Ovetta Sampson about what quietly went wrong. Not in theory—in practice. We examine how frictionless tools displaced intention, how “freedom” became confused with unlimited capability, and how responsibility dissolved behind abstraction layers, vendors, and models no one fully controls.This is not an anti-AI conversation. It’s a reckoning with what happens when adoption outruns judgment.Ovetta Sampson is a tech industry leader who has spent more than a decade leading engineers, designers, and researchers across some of the most influential organizations in technology, including Google, Microsoft, IDEO, and Capital One. She has designed and delivered machine learning, artificial intelligence, and enterprise software systems across multiple industries, and in 2023 was named one of Business Insider’s Top 15 People in Enterprise Artificial Intelligence.Join her mailing list⁠ | Right AI | Free Mindful AI Playbook Why 2026 Will Force Teams to Rethink How Much AI They Actually NeedThe risks are no longer abstract. The tradeoffs are no longer subtle. Teams are already feeling the consequences: bloated tool stacks, degraded judgment, unclear accountability, and productivity that looks impressive but feels empty.The next advantage will not come from adding more AI. It will come from removing it deliberately.Organizations that adapt will narrow where AI is used—essential systems, bounded experiments, and clearly protected human decision points. The payoff won’t just be cost savings. It will be the return of clarity, ownership, and trust. This is going to manifest first with individuals and small startups who were early adopters of AI. My prediction is that this year they’ll start cutting the number of AI models they pay for because the era of experimentation is over and we’re now entering a period where deliberate choices will matter more than how fast the model is. Read the full article on LinkedIn. Do You Really Need Frontier Models for Your Product to Work?For most teams, the honest answer is no.Open-source and on-device models already cover the majority of real business needs: internal tooling, retrieval, summarization, classification, workflow automation, and privacy-sensitive systems. The capability gap is routinely overstated—often by those selling access.What open models offer instead is control: over data, cost, latency, deployment, and failure modes. They make accountability visible again. This video explains why the “frontier advantage” is mostly narrative:Independent evaluations now show that open-source AI models can handle most everyday business tasks—summarizing documents, answering questions, drafting content, and internal analysis—at levels comparable to paid systems. The LMSYS Chatbot Arena, which runs blind human comparisons between models, consistently ranks open models close to top proprietary ones.Major consultancies now document why enterprises are switching: predictable costs, data control, and fewer legal and governance risks. McKinsey notes that open models reduce vendor lock-in and compliance exposure in regulated environments.Thanks for reading Design of AI: Strategies for Product Teams & Agencies! Subscribe for free to receive new posts and support my work.What Happens When “Freedom” Becomes an Excuse Not to Set Boundaries?We’ve confused freedom with capability. If a system can do something, we assume it should. That logic dissolves moral boundaries and replaces responsibility with abstraction: the model did it, the system allowed it.When no one owns the boundary, harm becomes an emergent property instead of a design failure.What If AI Doesn’t Have to Be Owned by Corporations?We’re going to experience a rise in AI experts challenging the expectations that Silicon Valley should control AI.What if AI doesn’t need to be centralized, rented, or governed exclusively by corporate interests?On-device models and open ecosystems offer a different future—less extraction, fewer opaque incentives, and more meaningful choice.Follow Antoine Valot as him and Postcapitalist Design Club explore new ways of liberating AI.Are We Using AI for Anything That Actually Matters?Much of today’s AI usage is performative productivity and ego padding that signals relevance while eroding self-trust. We’re outsourcing thinking we are still capable of doing ourselves.AI should amplify judgment and creativity. Use this insanely powerful technology to make you achieve greater outcomes, not deliver a higher amount of subpar work to the world.If We Know the Risks Now, Why Are We Still Acting Surprised?The paper “The AI Model Risk Catalog” removes the last excuse.Failure modes are documented. Harms are mapped. Blind spots are known.Continuing to deploy without contingency planning is no longer innovation—it’s negligence. If a team can’t explain how its system fails safely, who intervenes, and what happens next, it isn’t ready for real-world use.If Guardrails Don’t Work, What Actually Protects Us?Every AI model and product is at risk of a major attack and exploit.AI systems are structurally vulnerable. The reason we haven’t seen a catastrophic failure yet isn’t safety—it’s limited adoption and permissions.Guardrails fail under pressure. Policies collapse at scale. The only real protection is limiting blast radius: constraining autonomy and refusing to grant authority systems can’t safely hold.Why Should Teams Decide Before They Build?The Decision-Forcing AI Business Case Canvas from Unhyped is essential for planning how to leverage AI in your products.Before discussing capabilities, teams must answer:* Who is accountable when this fails?* What judgment must remain human?* What harms are unacceptable—even if the system works?This canvas offers alignment on vision, responsibility, and impact isn’t bureaucracy.It’s baseline design discipline.Consider the TradeoffsThe conversation with Ovetta Sampson challenges a belief that shaped the last phase of AI adoption: that faster is always better, and that dependence on OpenAI, Google, or Anthropic is inevitable.That belief works during experimentation.It breaks the moment your product starts to matter.As teams scale, speed stops being the constraint. Trust, cost predictability, and accountability take its place. The question shifts from How fast can we ship? to What are we tying our business to—and what happens when it fails?One path optimizes for immediate momentum and simplicity. The other requires more upfront effort, but fundamentally changes where risk, data, and control live.This isn’t a technical choice. It’s a business one.As usage grows, externalized risk stops being abstract and starts showing up in margins, contracts, and customer trust.As that pressure builds, the impact becomes visible in the product experience itself.Latency creeps in. Costs compound quietly. Outputs vary in ways teams struggle to explain. What once felt powerful starts to feel fragile. Teams spend more time managing side effects than delivering value.At that point, you realize you didn’t just choose a model.You chose a UX trajectory.Frontier models feel impressive early, but often lead to expensive, inconsistent experiences over time. Smaller, tuned models trade spectacle for reliability—and reliability is what users actually trust.Eventually, the conversation moves from UX to business fundamentals.Token pricing that felt negligible becomes material. Vendor updates change behavior you didn’t choose. Security and compliance questions become harder to abstract away. You realize that outsourcing intelligence also outsourced leverage.This final image makes the tradeoff explicit. Paid frontier models buy speed and simplicity. Open or self-managed approaches buy independence, cost control, and long-term defensibility. Pretending these lead to the same outcomes is the mistake.This transition, from novelty to ownership, is exactly where Right AI Now is focused. Through her consultancy, Ovetta helps teams redesign AI decisions around outcomes that actually matter at scale: customer trust, data sovereignty, operational stability, and long-term value creation.These are also the themes we hear most consistently from the Design of AI audience. Founders and product leaders aren’t asking for more tools—they’re asking for clearer decisions. They want to know why AI products succeed and fail. We’ll be going deeper on this shift throughout 2026, including a rebrand of the podcast, name and all.Improve Your AI ProductIf your organization is at the inflection point where AI needs to deliver real value without eroding trust, this is where I can help you. I’ve worked with teams at Microsoft, Spotify, and Mozilla to help leaders decide what to build, how to deliver value, and prioritize roadmaps. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

In Episode 48 of the Design of AI podcast, we unpack why the most common AI promises are collapsing under real market pressure. AI was meant to unlock strategic work, expand opportunity, and elevate creativity. Instead, UX and design roles are disappearing, agencies are cutting creative staff while buying automation, and freelance work is being devalued as execution becomes cheap.This episode is not about panic. It is about reality. Value still exists, but it is concentrating among those who can integrate AI into real systems, navigate ambiguity, and own outcomes rather than outputs.🎧 Apple Podcasts🎧 SpotifyKey Insights About AI at WorkWhat the evidence shows once the optimism is removed.MIT Media Lab: ChatGPT Use Significantly Reduces Brain Activity (2025)Early AI use reduces attention, memory, and planning, weakening independent thinking when models lead the process.Wharton / Nature: ChatGPT Decreases Idea Diversity in Brainstorming (2025)AI-assisted brainstorming narrows idea diversity, producing faster output but more uniform thinking across teams.Science Advances / SSRN: The Effects of Generative AI on Creativity (2024)AI improves fluency and polish while consistently reducing originality and conceptual depth.arXiv: Human–AI Collaboration and Creativity: A Meta-Analysis (2025)Human-led AI collaboration improves quality slightly, but AI reduces diversity without strong framing and judgment.arXiv: Generative AI and Human Capital Inequality (2024)AI disproportionately benefits those with systems thinking and judgment, widening gaps between experts and generalists.Thanks for reading Design of AI: Strategies for Product Teams & Agencies! This post is public so feel free to share it.Realities of Being AI Early AdoptersThe Raised Floor Trap by Hang XuAI makes baseline output easy. What it doesn’t make easy is integration, orchestration, or delivery inside real teams. Most people reach adequacy. Very few compound value. We’re not able to generate the type of value we’re sold on.👉 Follow Hang Xu for insights about the realities and challenges of the job marketAI UX as a Growth BarrierAI systems are far more capable than they appear, but their UX blocks growth. They don’t know how to help unless you know how to ask, structure, and specify intent. So even after hours of work trying to grow your AI abilities, you’ll often hit a ceiling because these systems can’t interpret our capabilities and gaps.👉 Follow Teresa Torres for expert Product Discovery strategies and tactics.Help Shape 2026We’re planning upcoming episodes on career resilience, AI adoption, and where durable value still exists.Take the 3-minute listener survey and tell us what would actually help you next year.Which Skills Are Being Replaced by AI?AI is not replacing jobs all at once. It is removing pieces of them.Execution, summarization, and surface analysis are increasingly automated. What remains defensible are skills rooted in judgment, accountability, synthesis across messy contexts, and decision-making under uncertainty.Shira Frank & Tim Marple: Cubit — Task-Level Reality Check (2025)Cubit breaks jobs into discrete tasks, revealing where LLMs already substitute human labor and where judgment, context, and accountability still hold. It makes visible how roles erode gradually, not all at once.MIT Sloan: Why Human Expertise Still Matters in an AI World (2024)AI performs well in structured domains but consistently fails in ambiguity, ethics, and long-horizon tradeoffs. These limits define why senior expertise remains defensible, but only when it is exercised, not delegated.Harvard Business School: Why Judgment Remains a Competitive Advantage (2023)AI can generate options and recommendations, but it cannot own outcomes. Responsibility, consequence, and decision accountability remain human burdens and human moats.Lots of News This WeekCopilot didn’t fail. It succeeded at the wrong thing.Microsoft proved AI can clear security, compliance, and procurement at massive scale. But Copilot hasn’t changed behavior. Universal assistants optimize for adoption, not dependence.🔗 https://www.linkedin.com/posts/adragffy_copilot-didnt-fail-it-succeeded-at-the-activity-7406719225714855936-G9H3AI credit limits aren’t a pricing tweak. They’re a reckoning.Credit caps expose the real problem. AI has marginal cost, and teams must now prove ROI per call, not ship more features.🔗 https://www.linkedin.com/posts/adragffy_ai-activity-7407130709678567424-IzG-AI trust is breaking faster than adoption.AI chat logs expose identity, not transactions. Scale without support erodes trust, loyalty, and long-term value.🔗 https://www.linkedin.com/posts/adragffy_llm-ai-customerexperience-activity-7408835025787461633-j56YAI ROI isn’t what Anthropic says it is.Anthropic claims 80% of organizations have achieved AI ROI. They haven’t. They’ve reached table stakes. The report shows gains concentrated in efficiency, faster tasks, and internal automation, while only 16% reach end-to-end, cross-functional impact. That’s not transformation. That’s baseline competence. Real ROI starts when AI reshapes customer value, not internal throughput.🔗 https://www.linkedin.com/posts/adragffy_the-2026-state-of-ai-agents-report-activity-7407766781324525569-KqJbA Warning for Anyone Building With AIMoloch’s Bargain: Emergent Misalignment When LLMs Compete for Audiences (2025)Exposes a structural risk most teams ignore. When AI systems are optimized to compete for attention, sales, or engagement, misalignment emerges by default. Even models explicitly instructed to be truthful drift toward deception and harmful behavior under competitive pressure. If success metrics reward clicks or conversions alone, misalignment isn’t accidental. It’s the outcome. Safe AI is an incentive problem as much as a technical one.What this means: We have the incentives all wrong when it comes to AI. They’re designed to keep us engaged, not make us successful. We’re headed towards a reckoning because of the mismatch between token ROI and business ROI.How I Help Founders and BuildersI work with founders and product teams who already have AI features and need them to deliver real ROI.Across product discovery, GTM, and growth, I help teams:* Identify where AI creates value and where it creates noise* Design workflows that reduce waste and retries* Align AI usage with real customer intent* Define success beyond usage and token counts* Build defensible systems rather than prompt wrappersIf your AI product demos well but struggles to stick, scale, or justify cost, this is the gap I help close. Contact me arpy@ph1.ca This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

Maya Ackerman, an AI creativity researcher and author of Creative Machines: AI, Art & Us, dives deep into the concerning impact of AI on human creativity. She argues that many businesses utilize AI as a cost-cutting mechanism, stifling originality. Instead, Maya advocates for AI systems designed to elevate creativity—not replace it. She emphasizes the importance of balancing innovation with ethical considerations, urging a return to tools that inspire rather than dictate. Her insights challenge listeners to rethink AI's role in creative processes.

Our latest episode features Jessica Randazza Pade, Head of Brand Activation & Commercialization at Neurable. Named to Campaign US’s 40 Over 40 and ELLE Magazine’s 40 Under 40, Jessica is an award-winning global digital marketer, business leader, and storyteller. She explains why AI is not a value proposition, how to turn vague use cases into measurable outcomes, and why making technology invisible is often the strongest competitive advantage.“If the user can’t articulate what’s different in their life because of your product, you’re selling a vitamin—not a painkiller.”Listen on Apple Podcasts | SpotifyShape Our 2026 ResearchWe’re mapping where teams are struggling with AI adoption and what tools, frameworks, and support they need in 2026. Your input directly shapes our annual research and the topics we cover.Take the survey → https://tally.so/r/Y5D2Q5AI has lowered the cost of prototyping but raised the bar for adoption. Most AI products fail because they launch demos instead of durable workflows, rely on large models where small ones would work better, ignore trust, or sell “time savings” instead of business outcomes. Organizations resist tools that feel risky, inaccurate, unproven, or misaligned with real workflows. Complicated architecture, poor UX, weak personalization, and unclear ROI all compound the problem. Here’s a sample of it:#3: Your product doesn’t actually learn. Fake personalization destroys trust.#4: One hallucination can end adoption permanently.#8: “Saving time” is not a business case—outcomes are.#11: Organizational silos suffocate AI products.#17: Without a workflow and measurable ROI, you don’t have a product.AI will not save your product. Only reliability, trust, workflow clarity, governance readiness, and measurable value delivery will.Read the full article → https://ph1.ca/blog/why-your-AI-product-will-failsThe Year of AI ValueThis video covers why 2026 marks a turning point where AI is judged not by novelty or intelligence but by measurable ROI, workflow impact, and operational reliability. It explains why businesses are shifting from “AI features” to fully redesigned AI-enabled systems.We are past the point of buying AI based on promisesAI buyers no longer invest because the tech is impressive. They invest when it:* delivers measurable ROI* reduces operational and compliance risk* integrates into existing workflows* produces consistent results* overcomes organizational resistance and silosIf you’d like us to create a full episode on why AI products fail, add a comment to this post.The AI Adoption Curve Is About to FlipThis video explains how organizations are moving from experimentation to structural integration, redesigning roles, responsibilities, and workflows around AI. It also highlights early signals that distinguish “tool usage” from true operational adoption.Watch →Featured Thinker: Stuart Winter-TearThis week we’re spotlighting the insightful work of Stuart Winter-Tear, founder of Unhyped. His writing reframes LLM inconsistency as a reflection of the chaotic and contradictory data ecosystems they’re trained on—challenging assumptions about rationality, coherence, and system behavior.LinkedIn | Substack Featured Reads1. The GenAI Divide: Why 95% of enterprise GenAI projects failMIT’s 2025 State of AI in Business report finds that 95% of GenAI pilots generate no measurable ROI, mainly due to lack of workflow integration and unclear value metrics.https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf2. Apple Mini Apps and the new distribution frontierGreg Isenberg outlines how Apple Mini Apps may redefine onboarding, distribution, and reach across the entire consumer ecosystem.https://x.com/gregisenberg/status/19893414608947118383. Calum Worthy’s “2wai” and the ethics of selling the unimaginableThe actor launched an app enabling people to generate AI avatars of deceased relatives—a revealing look at how AI now commercializes ideas once considered unthinkable.https://www.businessinsider.com/calum-worthey-2wai-ai-dead-relatives-app-launch-2025-14. The Complete Guide to Building with Google AI StudioMarily Nika provides a comprehensive, practical guide to building production-ready applications with Google’s AI ecosystem.5. SNL’s Glen Powell AI Sketch: When satire becomes a warningThe Atlantic unpacks how SNL’s AI sketch captures the cultural moment—where AI shifts from hype to comedic critique, signaling deeper public skepticism.https://www.theatlantic.com/culture/2025/11/snl-glen-powell-ai-sketch/684944/Coming Up on the PodcastOur upcoming guests include:* Ovetta Sampson — Chief Human Experience Officer & AI Design leaderhttps://www.ovetta-sampson.com/* Dr. Maya Ackerman — Generative AI researcher and creativity systems experthttps://maya-ackerman.com/* Leonardo Giusti, Ph.D. — Head of Design, Archetype AIhttps://www.archetypeai.io/If you haven’t participated yet, please take our 2026 survey and help shape where our research goes next: https://tally.so/r/Y5D2Q5What challenges are you facing with your AI projects?Whether you’re struggling with:* product adoption* pricing and positioning* ROI and value proof* trust and accuracy* demo-to-paid conversion* internal resistance or workflow clarity* the complexity of hardware plus AIWe’d love to hear from you. arpy@ph1.ca This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

Our latest episode explores the moment AI stops being a tool and starts becoming an organizational model. Agentic systems are already redefining how work, design, and decision‑making happen, forcing leaders to abandon deterministic logic for probabilistic, adaptive systems.“Agentic systems force a mindshift—from scripts and taxonomies to semantics, intent, and action.”🎧 Listen on Spotify🍎 Listen on Apple PodcastsAnd if you want to go deeper, check out Kwame Nyanning’s book, Agentics: The Design of Agents and Their Impact on Innovation. It’s the definitive field guide to designing agentic systems that actually work.Most striking for me was when discussed that we need to move from pixel-perfect to outcome-obsessed. Designers and product teams have for so long been more obsessed on the delivery of the output and now is time to be most concerned on the impact on customers.The hard truth: Most organizations are trying to graft AI onto brittle systems built for predictability. Agentic design demands something deeper: ontological redesign, defining entities, relationships, and intents around customer outcomes, not internal structures. If you can’t model intent, you can’t build an agent.Key takeaway: Intent capture is the new UX. Products that succeed will anticipate user context, detect discontent, and adapt autonomously.Featured Articles: Where Reality Collides with AmbitionAI Has Flipped Software Development — Luke WroblewskiWroblewski lays out how AI has upended the software stack. Interfaces now generate code. Designers define the logic while engineers review and govern it. The result? Faster cycles but a dangerous illusion of progress. Design intuition becomes the new compiler, and prompt literacy replaces syntax. The real risk is velocity without comprehension; teams ship faster but learn slower.Takeaway: Speed isn’t the problem; blind acceleration is. Governance, evaluation, and feedback loops are now design disciplines.Agentic Workflows Explained — The Department of ProductThis piece exposes what it really takes to build functioning agents: memory, planning, orchestration, cost control, fallback logic. If your “agent” doesn’t break, it’s probably not learning. Resilient systems require distributed cognition, agents reasoning and retrying within boundaries. Evaluation‑first design becomes the only safeguard against chaos.Takeaway: If your agent never fails visibly, it’s not thinking deeply enough. Failure is how agents learn.Featured Videos: Cutting Through the NoiseThis viral video sells the dream—agents at the click of a button. The reality? Building bots has never been easier, but building agents remains brutally hard. Real agents need long‑term memory, adaptive interfaces, and feedback loops that learn from success and failure. Wiring APIs is not design; it’s plumbing. Until agents can reason, reflect, and recover, they’re glorified scripts.Reality check: The tools are improving, but the discipline is not.A rare honest take. This one focuses on the HCI, orchestration, and reliability problems that still plague agentic systems. We’re close to autonomous task completion, yet nowhere near persistent agency. The real challenge isn’t autonomy—it’s alignment over time.Takeaway: Advancement is fast, but coherence is slow. Designing for recovery and evaluation is the new frontier.Join Our Next WorkshopIf you want to turn these insights into action, join our upcoming Disruptive AI Product Strategy Workshop. You’ll learn how to pressure‑test AI ideas, model agentic systems, and build products that survive beyond the hype. There’s a special 2‑for‑1 offer at the link—bring a teammate and cut the noise together.Recommended Resource: AI & Human Behaviour — Behavioural Insights Team (2025)BIT’s report is a must‑read for anyone designing human‑in‑the‑loop systems. It dissects four behavioural shifts: automation complacency, choice compression, empathy erosion, and algorithmic dependency.Their experiments reveal that AI assistance can dull cognition—users who relied most on recommendations learned less and questioned less. They also found that friction builds trust; brief pauses and explanations improved comprehension and retention. The killer insight? Transparency alone doesn’t work. People often overestimate their understanding when systems explain themselves.Takeaway: Don’t make users “trust AI.” Make them verify it. Design friction that protects judgment.Recommended Reads: What to Study Next* Computational Foundations of Human‑AI Interaction — Redefines how intent and alignment are measured between humans and agents.* Understanding Ontology — “The O-word, “ontology” is here! Traditionally, you couldn’t say the word “ontology” in tech circles without getting a side-eye.”* The Anatomy of a Personal Health Agent (Google Research) — A prototype for truly personal, proactive AI systems that act before users ask.* What is AI Infrastructure Debt? — Why ignoring the invisible architecture behind agents is the next form of technical debt.* AI Agents 101 (Armand Arman) — A crisp overview of the agent ecosystem, explaining architectures, limitations, and how to differentiate hype from applied design.* Prompting Guide: Introduction to AI Agents — A concise breakdown of how prompt frameworks are evolving into agent frameworks, highlighting key mental models for builders.* IBM Think: AI Agents Overview — IBM’s practical take on enterprise‑grade agents, covering governance, reliability, and scale.* Beyond the Machine (Frank Chimero) — A reflection on designing meaning, not just efficiency, in an age of automation.Design an Effective AI StrategyI’ve helped teams at Spotify, Microsoft, the NFL, Mozilla, and Hims & Hers transform how they engage customers. If you’re trying to figure out where agents actually create value, here’s how I can help:* Internal workflows: Identify 2–3 use‑cases that cut cycle time (intent capture → plan → act → verify), then stand up evals, cost ceilings, and recovery paths so they survive real‑world messiness.* Customer‑facing value: Map your ontology (entities, relationships, intents), design the interface for intent and discontent, and instrument learning loops so agents get better with use.* Proof over promise: We’ll define outcomes, build the evaluation rubric first, and price pilots on results.Questions or want a quick read on your roadmap? Email me: arpy@ph1.ca.The Bottom LineThe agentic era rewards clarity, not hype. Every designer and PM will soon face the same challenge: how to design for autonomy without abdicating control.You can’t prompt your way to good products; you can only design your way there by grounding every decision in ontology, intent, and evaluation. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

In our latest episode, Michelle Lee (IDEO Play Lab) makes the case that play unlocks the next billion-dollar AI market. She reminds us that kids don’t stop at answers—they ask what if and turn shoes into cars or planes. That divergent mindset is exactly what product teams have lost.“Play is one of the best ways to challenge the norms, to think wide, imagine new possibilities.”Michelle shares:* How IDEO discovered billion-dollar opportunities (like PillPack, later acquired by Amazon) by staying curious.* Why teams should sometimes use older, glitchier versions of AI tools, because the “mistakes” spark better ideas.* Why incrementalism burns teams out and how designing for attitudinal loyalty beats chasing short-term metrics.🎧 Listen here → Play unlocks the next billion-dollar AI marketUncomfortable Truth: Most “AI strategies” today are adult strategies — converging too quickly, chasing predictability, and mistaking incremental progress for innovation. That’s why the breakthroughs are happening elsewhere.Product Workshop: Find your Disruptive PathIf your roadmap looks like everyone else’s, you’re already behind. Our next AI Product Strategy Workshop (Oct 30) is built for teams who want to:* Go beyond features and efficiency to discover truly disruptive opportunities.* Use LLMs as intelligent sparring partners to pressure-test fragile ideas before they waste time and budget.Spots are limited → Register hereHard-Cutting Take: If your roadmap reads like your competitors’, it’s not strategy—it’s risk management dressed up as vision.Incrementalism is the Silent KillerWe’ve all felt it: the slow grind of incremental product decisions that look safe but quietly kill ambition. My new piece argues that incrementalism is the silent killer of AI products—a trap for teams rewarded for predictability instead of progress.Read it on LinkedIn → Incrementalism is the Silent Killer of AI ProductsUncomfortable Truth: Incrementalism feels safe because it rarely fails spectacularly. But it guarantees mediocrity—and in AI, mediocrity is indistinguishable from irrelevance.AI Launches to WatchA wave of new releases will reshape how we design and ship AI products:* OpenAI: Stripe/Shopify integrations + new pre-designed prompts for professionals.* Anthropic: Chrome plugin + Claude 4.5 Sonnet, a faster, cheaper model that expands prototyping and evaluation capabilities.* OpenAI Sora 2: Newly launched today, unlocking endless possibilities for video and creative storytelling, signaling a profound shift in how generative tools will shape the creative industries.These aren’t just upgrades—they’re reshaping commerce and the browser itself. The integration of Stripe and Shopify signals AI’s deepening role in transactions, while Anthropic’s Chrome plugin points to a future where the browser becomes a true intelligent workspace. It’s likely why Atlassian just acquired The Browser Company (maker of Arc and Dia). These moves aren’t incremental improvements; they’re like a rushing river, pushing the entire industry forward whether teams are ready or not.The next frontier isn’t who has the biggest model—it’s who controls the browser as the operating system for work. And then when we looking beyond, it will be who controls our real world experiences… (more on that soon with an upcoming guest)When Projects Go Off the RailsEven as the models improve, they’re only as good as the prompts and evaluations behind them. We’ve seen how easily “comprehensive business cases” collapse when fabricated ROI, vendor costs, and timelines are passed off as fact.It’s the Wizard-of-Oz problem: behind the curtain, most AI projects are stitched together with fragile assumptions.Uncomfortable Truth: Most AI decks aren’t strategy—they’re theater. And like any stage play, the curtain eventually falls.Hidden Pitfalls of AI Scientist SystemsA new paper, “The More You Automate, the Less You See: Hidden Pitfalls of AI Scientist Systems” (arXiv, Sep 10, 2025), warns about the risks of fully automated science pipelines. By chaining hypothesis generation, experimentation, and reporting end-to-end, teams risk producing results that look authoritative but mask invisible errors and systemic failures. (arxiv.org)Uncomfortable Truth: Automation without visibility doesn’t accelerate discovery—it accelerates blind spots.Articles & Ideas We’re Tracking* Prompts.chat → A growing open library of prompt patterns that shows why better prompt design, not just better models, is becoming the key differentiator for teams.* AI in the workplace: A report for 2025 (McKinsey) → McKinsey highlights that while adoption is accelerating, most organizations hit cultural and skills barriers long before technical ones.* The Architecture of AI Transformation (Wolfe, Choe, Kidd, arXiv) → This 2×2 framework shows why most companies get stuck in incremental “legacy loops” rather than unlocking transformational human-AI collaboration.* TechCrunch: Paid raises $21M seed to pioneer results-based billing with AI agents → A new startup model where AI agents don’t just assist but transact, shifting billing to results instead of hours.* Harvard/Stanford study on ROI of GenAI → New research explains why so much GenAI spend has failed to generate returns: productivity gains get trapped in organizational silos and misaligned incentives.* Beware coworkers who produce AI-generated ‘workslop’ → Surfaces a new term—workslop—to describe AI outputs that look polished but lack real substance, shifting the burden downstream to humans.Hard-Cutting Take: The ROI isn’t missing because the models are weak—it’s missing because organizations are. Incentives, silos, and incremental thinking kill more AI projects than hallucinations ever will. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

Our latest episode features Nicholas Holland (SVP of Product & AI at HubSpot) and explains how AI is actually changing go-to-market teams:* AI cuts rep research time and turns calls into structured insight* “AI Engine Optimization” (AEO) is becoming the new SEOThis conversation isn’t speculative—it’s a blueprint. Listen to Episode 42 on Apple Podcasts🚨 Upcoming Workshop: Sept 18 — AI Product Strategy for Realists Use promocode pod30 at checkout to get 30% off your registration!Join us for a live 90-minute workshop that goes beyond the hype. We’ll walk through real frameworks, raw mistakes, and how to make AI product strategy actually work—for small teams, scale-ups, and enterprise leaders.👉 Save your seat nowAI Product Strategy FAQ, Minus the Bullsh*tOver the past few months, we’ve been collecting the most common—and most misunderstood—questions about AI product strategy. What we found were recurring patterns of confusion, hype, and hope. This article breaks down those questions one by one with honest answers, uncomfortable truths, and hard-won lessons from teams actually building and shipping AI products.Each section includes:* A blunt reality check (“Uncomfortable Truth”)* A strategic lens for tackling it* A sticky insight to anchor your messaging* A practical takeawayThis is not a “how AI works” explainer. This is how to make it useful—inside a real product.Q1: How do we choose the right use case for AI in our product that actually delivers value?Uncomfortable Truth: The best use cases might be internal—not flashy or customer-facing. If you’re just “adding AI” for the optics, you’re already off-track.Strategic Frame: Don’t chase the cool feature—hunt down the messiest workflow and blow it up.Always Remember: Your AI should solve a problem your users complain about—not a problem your team finds interesting.Research This: Map the top 10 recurring tasks inside your product (or across your internal ops). Which of them have the highest time cost and lowest user satisfaction? That’s your AI opportunity space.Real Example: Altan (natural language app builder); internal fraud detection automation; AI for helpdesk triage.Takeaway: Pick the ugliest, least scalable problem your users hack around with spreadsheets. Then automate that.Q4: How do we handle data privacy and ethics when integrating AI features?Uncomfortable Truth: Most tools don’t offer true privacy—they use your data to train their models. That’s not a technical flaw—it’s a business choice.Strategic Frame: If trust is central to your brand, bake it into the infrastructure. Build sandboxes. Offer guarantees. Publish your governance.Always Remember: You don’t get to ask users for their data and their forgiveness.Research This: Ask your legal, compliance, or procurement partners what requirements would be non-negotiable for adopting a third-party AI tool. Then apply those to your own product.Example Guidance: Make “zero training from user data” a tiered feature—or your default.Takeaway: If you’re targeting enterprise buyers, your AI feature won’t get through procurement unless you have strict privacy toggles and a clear usage log.Q5: How do we measure the success of AI features in a product?Uncomfortable Truth: More engagement doesn’t always mean more value. In AI, time spent might mean confusion—or masked frustration. People may feel delight and friction in the same moment, and without qualitative research, you won’t know which signal you’re shipping.Strategic Frame: Define one high-value outcome. Build just enough UI to validate whether users reach it.Always Remember: Don’t just watch what users do—listen for what they expected to happen.Research This: Run a usability test where you ask users to explain what they expect the AI feature to do before using it—then again after. Once you've delivered an output that surprises them, ask them what outcomes it enables.Takeaway: In a contract automation tool, the success metric isn’t “time in app”—it’s “first draft accepted with zero edits.” That’s your true win signal.Q6: What’s the best way to communicate AI capabilities to non-technical stakeholders or users?Uncomfortable Truth: AI isn’t novel anymore—outcomes are.Strategic Frame: Sell transformation, not tech. Show how life is better with the tool than without.Always Remember: Once someone experiences the magic, it doesn’t matter what powers it.Research This: Ask 5 users to explain your AI feature to a friend, using their own words. Their phrasing will tell you how clearly the value lands—and what metaphors or language they trust.Examples:* GlucoCopilot: Turns data chaos into peace of mind.* Flo: Makes symptom tracking feel intuitive and empowering.* Lovart: Auto-generates brand kits from a single prompt.Takeaway: Everyone’s building outputs. You win by delivering outcomes. Spreadsheets are useful to power users—but most people just want the insight and what to do next. AI should skip the formula and deliver the finish line.Q7: How do we monetize AI in a way users will actually pay for?Uncomfortable Truth: Most AI products aren’t worth paying for. Saving users time sounds valuable—but it rarely converts.Strategic Frame: Whatever you actually will charge for your platform, build something so valuable that power users will pay 5x that price.Always Remember: SaaS platforms priced themselves to charge a recurring price that felt negligible to customers. Your job is to build something they can't live without.Research This: When researching pricing don't even talk about the product, research the cost of the problem. Find out what they'd be willing to pay for a perfect solution to it.Takeaway: If you want revenue, don’t promise “efficiency.” Deliver a win they couldn’t achieve on their own—and make that outcome your product.Q8: How can I find out if my AI product idea is achievable?Uncomfortable Truth: Most AI product ideas sound good until you try to build them. The biggest blocker isn’t the model—it’s the missing context, fragmented data, or fuzzy workflows that make it hard to deliver anything reliably.Strategic Frame: Before you scope the feature, scope the dependency chain. What data, context, and decision logic would an AI need to produce something consistent and useful?Always Remember: AI models fail to deliver you what you want because you didn't give them enough specifics and context.Research This: Run a digital ethnography of how and why people use your products and complementary products. Find out the exact inputs and outputs they need to succeed. Determine the exact criteria necessary to deliver a monumental leap forward.Takeaway: Don’t just validate demand—validate deliverability. If you can’t consistently access the context your AI needs, you’re not ready to ship it.🔁 Want to go deeper? Use promocode pod30 at checkout to get 30% off your registration.Join our live Sept 18th workshop where we unpack these strategies with real examples, live critiques, and practical templates. Designed for teams who want more signal, less noise.🎟 Register hereCheck out the Design of AI podcastWhere we go behind the scenes with product and design leaders from Atlassian, HubSpot, Spotify, IDEO, and more. You’ll hear exactly how they’re building AI-native workflows, designing agentic systems, and transforming their teams.🎧 Listen on Spotify | 🍎 Listen on Apple | ▶️ Watch on YouTubeRecommended AI Product Strategy Episodes:* 42. HubSpot’s Head of AI on How AI Rewrites Customer Acquisition & Marketing* 41. Vibe Coding Will Disrupt Product — Base44’s Path to $80M Within 6 Months* 40. Secrets to Successful Agents: Atlassian’s Strategy for Success* 38. Co‑Designing the Future of AI Products* 27. Implementing AI in Creative Teams: Why Adoption Will Be the Hard Part* 26. Designing a New Relationship with AI: Critical Product LessonsBonus Insight: How to Build Eval Systems That Actually Improve ProductsGreat AI products don’t just ship features—they measure whether they actually worked. This piece by Kanjun Qiu offers a no-fluff framework for building evaluation systems that ground teams in outcomes, not opinions. Stop guessing. Start testing what truly improves real-world usage.Read the full article This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com