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by Arpy Dragffy
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- May 21, 202629 min
Build the Context Layer Before the Agent
Atlassian spent three years connecting 150 billion organizational objects before the results appeared: 44% more accurate AI answers, 48% fewer tokens, a coding agent that reviewed 2 billion lines of code in two minutes. That’s the proof enterprises are pointing to when they argue that context graphs are the unlock. What the benchmark obscures is the order of operations — the graph had to exist before any of those numbers were possible.The reorganization bet is running in parallel, and it’s moving faster than the infrastructure. Airbnb’s CHRO is converting documentation to markdown, building skills libraries, mining meeting recordings before institutional memory disappears — five structural prerequisites before the first agent goes live. Meta is posting $26.8 billion in Q1 profit, laying off 8,000 people, and reporting “horrifically, historically low” employee morale. Both are restructuring around AI. Only one is sequencing it correctly.In AI customer experience, Twilio is working against a Qualtrics finding that 1 in 5 AI interactions delivers zero benefit. Rikki Singh’s diagnosis is precise: the orchestration layer is there, but it’s running without the context layer underneath it. FAQ automation with better packaging is still FAQ automation. The unlock is real — but only when all three pieces are in place, in order. The knowledge worker playbook in this edition addresses the fourth variable: what happens to the people whose roles disappear when the gathering does.Rikki Singh leads product innovation at Twilio — what the company is calling its biggest launch in 17 years. Before Twilio she was at McKinsey, where she co-authored the foundational research on what makes a great PM. The Qualtrics 2026 CX Trends Report found nearly 1 in 5 consumers who used AI customer service saw zero benefit — the baseline she is working against.* Why AI CX is still FAQ automation with better packaging* Why AI spend is as unpredictable as AI upside* The wrapper that makes AI feel like it thinks* Vitamins vs painkillers: the product sense filter* How to protect long-horizon bets inside a big company* Why the brand — not the vendor — owns AI failureListen: Spotify | Apple PodcastsJamil Valliani leads AI product at Atlassian, where he has spent three years building the Teamwork Graph across 300,000 companies. Recorded live at Team ‘26 in Anaheim, where Atlassian demonstrated what connecting 150 billion organizational objects produces: 44% more accurate AI answers using 48% fewer tokens, and a coding agent that reviewed 2 billion lines of code in 2 minutes.* Why your team spends 80% on gathering, not deciding* The adoption pattern that turns skeptics into converts* How to build trust with AI one small task at a time* Why giving AI less data often gets you a better answer* How leaders stop waiting for Friday status reports* From 2 ideas to 10: the creative unlock nobody explains“You didn’t hire your team to write reports. You hired them to advance the business forward.” — Jamil VallianiListen: Spotify | Apple PodcastsNo one is measuring ROI & fewer understand knowledge graphsWe attended Atlassian Team ‘26 in Anaheim to cover the Teamwork Graph and what knowledge graphs actually mean for the future of work. Key learnings:* Everyone is in such a rush to increase adoption numbers that no one cares to measure ROI, only velocity* In the rush to adopt, many orgs are discovering dozens of agents built by individuals that are unsanctioned and eating up tokens* While there’s excitement about announcements about getting access to more context, few understand what to do with the context that’s currently available to them today📅 productimpactpod.com is the hub for AI product strategy, news, and analysis. All the articles featured in this edition are sourced from Product Impact’s own reporting.WTF Is an AI-Native Org Anyways? Let’s Compare Airbnb & Meta’s Opposing Plans.The term “AI-native” gets used without definition until it has to mean something operationally. Airbnb’s CHRO Iain Roberts gave the clearest working answer at Stanford: restructure before you deploy. His five moves — converting do
- May 12, 202645 min
Governance, Context, and the Org-Design Reckoning
Atlassian connected its AI agents to a richer layer of company knowledge (documents, projects, goals, people) and measured a 44% improvement in answer accuracy using 48% fewer resources. Same models. Different information. Brian Armstrong restructured Coinbase the same week: 14% headcount cut, five management layers maximum. When AI can surface what previously required institutional memory and senior tenure, the organizational layers built around that knowledge become harder to justify.The visible shift gets covered in tech headlines. What gets lost in the announcement energy: none of this works if the company hasn’t decided what it wants AI to do.The more widespread barrier is upstream of governance. Most executives approving AI budgets are working through the aftermath of pilots that underdelivered, first-generation deployments that didn’t survive contact with their actual data, and early model results that left skepticism the current tools have since substantially outrun. That trust deficit — organizations evaluating new AI investment based on experiences two generations old — is where enterprise AI projects most commonly stall. Shadow AI governance and deployment intent are real risks, but they’re downstream of that harder problem. There is no closing the capability gap inside an organization that is quietly waiting for the next deployment to fail too.John Willis co-wrote The DevOps Handbook because software teams were shipping code fast without feedback loops or governance. He sees the same pattern repeating with AI — and he spent five decades documenting what happens when the gap between vendor promises and operational reality gets this wide.* Why shadow AI is more dangerous than an outright ban* Why throughput without governance means instability at scale* Why governance creates flow instead of stopping it* Why most teams have ML evaluation tools when they need audit trails* Why even a five-person startup needs digitally signed records of agent decisions* What AI winters teach us about where we actually are nowListen: Spotify | Apple PodcastsRikki Singh leads product innovation at Twilio — what the company calls its biggest launch in 17 years. Before Twilio she was at McKinsey, where she co-authored the definitive research on what makes a great PM. The Qualtrics 2026 CX Trends Report found nearly 1 in 5 consumers who used AI customer service saw zero benefit. That number is the benchmark she is working against.* Why most AI CX is still FAQ automation with better packaging* Why the LLM wrapper creates false confidence — the model generates strings, it is not thinking* Vitamins vs painkillers: how to parse what customers don’t say out loud* How to protect long-horizon bets inside a public company* Why the brand owns the accountability when AI gets a high-stakes interaction wrongListen: Spotify | Apple Podcasts📅 productimpactpod.com is the hub for AI product strategy, news, and analysis. All the articles featured in this edition are sourced from Product Impact’s own reporting.AI Value Acceleration is building a report on where enterprise AI investments are actually creating value. If you’re responsible for a major AI investment — leading it, funding it, or proving it’s working — we want to talk to you.Every CEO Will Post a Layoff Notice Like This. Here Is Why.Brian Armstrong’s May 5 Coinbase memo framed a 14% headcount cut as a structural prerequisite for AI adoption, not a consequence of it. Three principles: hard cap of five management layers, player-coaches who produce output alongside their teams, and AI-native pods where one person spans engineering, design, and product with agent support. Sequoia’s 2026 analysis found AI-native startups already ship three times faster with 60% fewer engineers — that’s the economic gap the restructuring is attempting to close.The jobs being cut are not cyclical. In
- Apr 29, 202618 min
The Most Important Data Points in AI Right Now
The Stanford AI Index’s headline is 88% — organizations using AI in some capacity. The Financial Times charted where it actually lands in the workforce: 62% of top-decile earners use it daily, versus 13% at the bottom. Board decks this quarter will cite Stanford. The FT chart is what they’re not showing.The economics that enabled this gap are under pressure. The three-year subsidized era is ending by financial necessity, not choice. The same optimization logic that built social media’s loneliness machine is now embedded in AI products at scale. And in the same week Anthropic’s most capable model autonomously found 271 zero-days in Firefox, two major platforms were breached through third-party integrations. The data and what to do about it follows.Episode 8: The Most Important Data Points in AI Right NowBrittany Hobbs solo — four segments moving from data to strategic implication. Essential for anyone making AI purchasing, hiring, or architecture decisions right now.The Stanford AI Index 2026. 88% organizational adoption is saturation, not a trend. $581 billion invested globally in 2025, up 129% year over year. The US-China AI performance gap collapsed from 17–31 percentage points in 2023 to 2.7% today — on 23 times less investment. China holds 69.7% of global AI patent filings. Architecture and application discipline closed a gap that capital alone could not. Stanford AI Index 2026 | The U.S. Can’t Buy an AI LeadToken economics. Anthropic’s current tiers: Haiku at $1/$5 per million input/output tokens, Sonnet at $3/$15, Opus at $5/$25. A 200-screen product built with Claude Design costs $0.22 for a first draft; the 50-iteration refinement cycle real design work requires runs to ~$2,600, plus $200–$900/month in system updates. Every comparable Figma interaction costs zero. Prompt caching provides ~90% discounts on repeated context; batch processing cuts 50%. Claude Design vs Figma cost breakdown | CNBC: Token economicsApple chose its hardware chief as next CEO. John Ternus — SVP of Hardware Engineering, architect of Apple Silicon — succeeds Tim Cook on September 1st. Johny Srouji, who designed every Apple Silicon chip, becomes Chief Hardware Officer. Apple posted $143.8 billion in Q1 FY2026 (up 16%, $109 billion in services, 92% retention) without shipping an industry-leading AI feature. The next decade of AI is decided at the silicon and device level. Apple CEO transition analysisVibe coding has never been more capable. Security has never been more exposed. Anthropic’s Mythos model identified 271 zero-day vulnerabilities in Firefox autonomously; the UK’s AI Security Institute found it succeeds at expert-level hacking tasks 73% of the time. Anthropic launched Project Glasswing (12 defensive security partners including Amazon, Microsoft, and Apple), then reported unauthorized Mythos access through a vendor. Vercel was breached through Context AI — customer credentials sold on BreachForums for $2 million. Lovable exposed source code and credentials via a basic authorization flaw for 48 days, fixed it, then broke it again for 76 more. TechCrunch: Anthropic Mythos | TechCrunch: Vercel breach | The Next Web: Lovable“If you’re making AI decisions for your team right now — what to buy, who to hire, what to build — there are numbers out this week that should change your approach.” — Brittany HobbsListen now: Spotify | Apple Podcasts | YouTube📅 productimpactpod.com just launched as a news platform. All the Stanford breakdowns, token economics case studies, and Apple CEO
- Mar 27, 202646 min
Your AI Strategy Is a Pile of Demos
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 dem
- Mar 16, 202652 min
75% of Enterprise AI Fails. The Fix Isn't a Better Model.
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
- Mar 2, 202635 min
The teams pulling ahead aren't the ones with the best models
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. — <a target="_blank" href="ht
- Jan 13, 202648 min
What Happens to Your Product When You Don’t Control Your AI?
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 t
- Dec 22, 202530 min
When AI Isn’t the Answer, It’s the Problem
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
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