The AI podcast for product teams

🎙 Episode 40: Atlassian’s Secrets to Successful AgentsIn this episode, Jamil Valliani (VP & Head of Product AI at Atlassian) shares how they embed AI across Jira, Confluence, and Trello through intelligent agents that blend into workflows—far from mere “+AI” buttons. He emphasizes starting small with tangible prototypes to build momentum and leadership alignment, showing that AI gains stick when they're experienced, not explained.Highlights from the episode:* Hands-on AI adoption at Atlassian: transforming workflows, not just products* From friction to flow: how prototypes bridge skepticism and trust* AI as teammate, not feature—designing for collaboration, not automation* Adoption baked into experience—make AI habitual, not optional“The most successful teams will treat AI not as a button you press, but as a teammate you collaborate with.”Listen on Spotify | Listen on Apple | Watch on YouTube —and share one workflow where AI acting more like a teammate could unlock unexpected value.About the Guest:Jamil Valliani brings two decades of product leadership (including 15 years at Microsoft) to Atlassian, where he’s spearheading AI-powered design.* LinkedIn* Atlassian RovoUpcoming Workshop: AI Product StrategyProduct teams everywhere are facing the same challenge: leadership wants AI integration for competitive advantage, but without certainty about which AI products will actually be valuable to customers.When: Thursday, September 18, 2025 (online)What you’ll gain:* Diagnose the highest-leverage AI use cases* Prototype with precision—avoid costly detours* Craft a resilient strategy that scales beyond pilot phaseRegister on Eventbrite and get a 2 for 1 promo.Learn to Synthesize or ElseIn a world awash with data, the real advantage lies not in knowing more—but in drawing clarity from the noise. Product and design leaders must become the translators of complexity, turning abundant knowledge into purposeful, actionable insight.h/t Stuart Winter TearEmerging Shift: Role-Dissolving AIFigma, OpenAI, and others are signaling a paradigm shift: AI is merging design, engineering, and research into a unified discipline. The competitive edge now lies in craft, judgment, and cross-disciplinary fluency—not siloed specialization.AI Merging Tech Roles, Favoring Generalists: Figma CEO Dylan FieldFeatured Video: Why Designers & Engineers Must Rethink Workflows for AI to Deliver Real ValueThis video pressurizes teams to question legacy workflows. Without overhauling collaboration models, decision-making structures, and design intent, even advanced AI remains misunderstood or underleveraged.Research To Reframe Your Strategy1️⃣ Mixture of Reasoning (MoR)Why it matters: LLMs can be trained to switch between reasoning styles—stepwise logic, analogies, symbolic reasoning—without prompt engineering.Strategy shift: Build assistants that adapt reasoning to task: planning one moment, diagnosing the next.Quick test: A/B fixed vs. adaptive reasoning in support/search flows to spot gains in mixed-query handling.2️⃣ In-Context Learning as Implicit Weight UpdatesWhy it matters: Transformers tweak their own behavior on-the-fly based on prompt context—no retraining required.Strategy shift: Enable products to adapt within interaction sessions, not over multiple deploy cycles.Quick test: Prototype context-aware replies and monitor when users feel seen vs. served.3️⃣ Chain-of-Thought (CoT) MonitorabilityWhy it matters: Exposing AI’s reasoning steps helps catch misalignment before it reaches users—but this safety window is fragile.Strategy shift: Don’t equate explanation with trust. For high-stakes domains, embed traceability and risk alerts.Quick test: Add CoT transparency to UX, measure user trust shifts when rationale is visible.Follow my co-host, Brittany Hobbs for essential research and product insights news.Your Next ChallengeMost teams drop AI into their products like sprinkles on a cupcake. But strategy—true product strategy—demands AI baked into the experience, from the core outward.Reply here or email me.Thanks for reading Design of AI: Strategies for Product Teams & Agencies! Subscribe for free to receive new posts and support my work. 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

When reports like Adecco’s Global Workforce of the Future survey find that the average saving for workers using AI is 1 hour a day, we should question this. * What did those workers do with their time savings? * Should that time savings benefit the employer or the employee?* Can we trust such a hard-to-measure stat?Our latest episode tackles this and other disruptions happening to the creative and production processes. Matthew Krissel is the Co-Founder of the Built Environment Futures Council and a Principal at Perkins&Will. For over two decades, he has led transformative architectural projects across North America and internationally. We discussed how AI is disrupting architecture and lessons for digital product teams. He really struck powerful points many times during our conversation about questioning the role of time and permanence in a world when we want more, faster.Other points covered in the conversation:* Commoditizing design makes production easier, enabling societies to tackle challenges like housing shortfalls* Commoditizing design devalues other vital processes, like community engagement, respectful place-making, and longevity of projects* Over-indexing AI’s potential as a workflow optimizer, while under-indexing the potential to reimagine how complex projects are planned and operationalizedListen on Spotify | Listen on Apple PodcastsIn this newsletter, I’d like to tackle the concept of time saving and what it means from the perspective of crafting an AI strategy. Here was the most important quote from the episode: So just because something took half the time it did before, what happened is we just did more. So we just filled the time. Is there something higher and better use? I suspect that somewhere along the line the designs got better. Also I suspect that somewhere along there was diminishing returns. We were just doing more because we could not that it was actually yielding anything better. Are you gonna focus on fewer, but better increase your quality? Are you going to spend more time on business development or some entrepreneurial side hustle? Just go home early? What you decide to do as we start to gain productivity time is going to shape a lot of where this is all happening.Newsletter recommendation: Scott BelskyEssential insights and lessons from Scott Belsky that anyone building with AI must read. His newsletter is fantastic and a must-subscribe because of his unique cross-section of expertise across creativity, product, and innovation. His books have also always been pivotal reads to advance your craft. Hopefully, we can do some of the same with our Design of AI podcast and newsletter. Who should benefit most from your ability to learn AI: You or your employer?The challenge to creatives and builders is to decide who should benefit from these transformative technologies if you’re self-taught:* Should you gift your employer the benefits if you’ve taught yourself ways of getting 25% more work accomplished in a day?* Should you gift yourself the benefits of your increased productivity and work on side projects, or spend more time with your family?Historically speaking, employers were responsible for the means and training of production. They paid for novel technologies —desktops, SaaS, big data— and were responsible for training you on how to use them. AI is different because employers are often lagging behind employees in embracing and educating on how to use the technology effectively. It is very easy to argue that the 200 hours you’ve spent learning AI outside of work hours should exclusively benefit you.AI Time Savings: Benefits & RisksTechnologies have consistently saved us time, but the resulting effects have been questionable. The internet and mobile phones connected the world, while also leading to increased poor health outcomes due to more time sitting. We also spend more time alone than ever.Further back, the Industrial Revolution raised the quality of life for everyone. Still, the commoditization of work led to industrialists exploiting child labour and putting everyone into deplorable working conditions that polluted communities. The time the workforce saved most benefited employers, with employees giving up their ways of life in favour of steady incomes. Most relocated to cities, got cut off from their families, and learned the pain of commuting for the first time.When it comes to AI, the benefits we hope for centre on automation and augmentation. The hope is that we will benefit from less shitty work (automated away) and that we can our new capabilities (augmented by AI) will enable us all become wealthy entrepreneurs. Sure, this may be true for the top 0.01% of AI users who learn how to run a typically 10-person business by themselves. For the rest of us our work may in fact get a lot shittier. At least that’s what the authors of the upcoming book, The AI Con, believe. The authors (and upcoming Design of AI guests), Alex Hanna and Emily M. Bender tell a tale of how AI’s risks have been severely hidden under the rug. In their book, they document many examples of the technology performing so poorly at tasks that products were shut down within weeks.Maybe the future of businesses will look a lot like Amazon: A business offering endless products of questionable quality and provenance with no humans in sight except those working the worst possible jobs in sorting information, like something out of Severance. In this scenario, the majority of humans will be employed as mall cops of the technology, swooping in when a problem happens that slips between the programming and policies. At this point, AI hypers would argue that even if the enshittification of work is inevitable, AI will open up new and better types of jobs. Only time will tell. How does AI change our relationship with time?When buying productivity-boosting hardware and software, the expectation has always been that the results are undeniable. Going from handwriting to using a typewriter was immensely faster. The same is true when buying a new Saas platform that makes managing projects infinitely easier. Now, with GenAI-powered products, the ROI is unpredictable. The vast majority of capabilities deliver the illusion of rapid progress. Think of image and video generation —the immediate results are shockingly impressive. But getting results to be production-ready requires mastery of probabilistic software and/or resetting your expectations. It all means that the operator —you— ultimately plays a bigger role in the ROI of using this technology than with previous ones.So-called Vibe coding is a major testament to the time savings that AI can create. Anyone can now build a website and app without writing a line of code.Vibe coding platforms —like Cursor, Lovable, Replit, and many more— are fantastically easy to use… until they’re absolutely painful to use. The stunning early rewards turn into confusingly broken components all over.Again, results depend on the operator’s ability to debug using an entirely new interface paradigm (conversational). This continues the technology’s remarkable inversion of the value paradigm, where workers define the quality of outputs.Looking ahead, mastery of data will triumph over mastery of interfaces. This favours employers who unlock the power of their first-party data and build solutions that augment and automate the expertise of their employees.Always worth reading, strategist and tech critic Tom Goodwin posted an intriguing analysis on LinkedIn this week. At the core of the guiding philosophy regarding AI-assistance is that the more complex the task, the less qualified AI is to work on the task unassisted.Check our previous podcast episode and newsletter for more details on how to unlock the power of your data. 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

“The future is already here, it's just not evenly distributed” Science fiction is inspiring, frightening, and often the best lens into the future. Many ideas about the future are b******t —just like this quote being misattributed to the ever-amazing William Gibson— but even the wildest idea shares truths worth discussing.This week’s newsletter is an exercise in imagining how AI will transform the way that we work. The future will impact us differently because some already live with a future-centred mindset, while others prefer to shift their thinking daily. One such future-centred thinker is John Whalen, the author of Design for How People Think and the Founder of Brilliant Experience. He shifted from being an AI skeptic to an advocate because he sees a tidal wave of change coming to how product teams operate.Listen on Spotify | Listen on Apple Podcasts | Watch on YouTubeIn the episode, we discuss how he’s implemented AI into his workflows and how he can now accomplish projects in one week that used to take seven weeks to complete. He makes a compelling case for why every team should use AI-moderation and synthetic users to enhance product outcomes. But most importantly, he’s become an AI advocate because, over his three-decade career, introducing new tools has always been met with doubts and resistance. Ultimately, businesses force the adoption of tools that deliver a clear ROI. There’s still much to debate about AI. Reports like this one from Microsoft continue to show that AI isn’t ready to replace humans at key tasks. Another 2024 study found that ChatGPT delivered inconsistent results on a key qualitative research task, compared to humans. The most important thing about this study wasn’t that humans outperformed LLMs; it was the significant performance improvement from GPT-3.5 to GPT-4.0. AI is getting much better at tasks that seemed unimaginable to automate. We’re hearing the same shocking stories across design, development, research, marketing, and sales. Undoubtedly, AI will be able to automate most of our work within a few years.Will that mean we’ll be replaced? Yes and no. Just like the industrial age and globalization destroyed artisans, AI will significantly reduce the headcount of “artisanal” product people and the rest of the work will be an assembly line of tool operators.Automation will significantly change many people’s lives in ways that may be painful and enduring. But for the economy as a whole, more jobs will be created, and those jobs will look different from those today.Thanks for reading Design of AI. Subscribe to receive new posts.Should we be worried about our jobs?These same conversations are happening across all fields:* Will AI Replace Therapists?* As Technology Progresses, Certain Accounting Jobs May Fade Away* The Risk of Dependence on Artificial Intelligence in Surgery* AI could terminate graphic designers before 2030You’re probably reading this with a sense of confidence that you’re shielded from the impacts of AI because you’re working on the bleeding edge of technology. It’s true. You should be better equipped to navigate the changes as they happen and adapt to the future better than others. Conversely, your roles face additional pressure to change faster than in other industries. The business realities of being backed by venture capital and private equity mean you’re always chasing the future. Tech and agencies have to unlock benefits from AI or risk losing market share and funding.The problem is that nobody can agree on AI's expected impact because it’s still just science fiction.According to the OECD report, the level of impact will largely depend on the level of adoption. High adopters might expect a 3x gain compared to those who adopt AI minimally. A McKinsey report highlights the pressure being placed on employees. Their data shows that C-suite executives blame employee readiness as a barrier to gaining benefits from AI. Only 1% of them believe their AI investments have reached maturity.Combined with last week’s conversation with Jan Emmanuele, AI investments in creative augmentation and automation will surge in 2026 and beyond. This suggests that employees will be under a lot of pressure to become more productive or else be replaced. Listen to that episode for more details on how AI is being adopted:Listen on Spotify | Listen on Apple PodcastsHow will jobs change as a result of AI?There’s no doubt that our jobs will change. They’ve had to change every time a transformative new technology becomes widely adopted. The only difference now is the speed at which change is happening.Let’s analyze how roles are changing from the perspective of product teams.* Our jobs used to be distinct. Each of us had specialties and expertise in areas that protected us.* Our jobs are increasingly commoditized, meaning people from other jobs can do many of our tasks.For example, a designer can now do tasks that previously were out of their sphere:* Use ChatGPT and Cove to explore a strategy and build a business case.* Use Wondering and Vurvey to launch and analyze a research campaign.* Use Lovable and Cursor to prototype and build out a product.Our roles are blending into one another, and employers no longer need as many people to deliver the same amount of work.How we work is also changing. AI is simplifying core tasks along our workflows and automating cumbersome steps. Here’s an example of how AI will transform UX Research:If you map your workflow, you’ll find a similar transformation happening to your role. Humans will drive decision-making, but AI will increasingly inform those decisions.Maybe John Whalen’s vision of product teams as AI-conductors is most appropriate: Maybe there will really be fewer UX researchers. Maybe they're more focused on this I'm calling sort of storytelling or conducting. I picture someone orchestrating these things.What you can do to enhance your futureJohn Whalen’s story shows that you can be an industry expert who has written a respected book and led a successful practice, yet still need to adapt to the coming change. He’s shifted from being a researcher to being a research technologist, one who delivers projects that used to take much more time and many distinct roles. This is similar to what Phillip Maggs said on episode 20 about becoming a design technolgist (Listen on Spotify | Apple).Recommendations to help you:1. Get closer to the decision-making processWe’re all anxious about the economy. The viability of many businesses is at risk, and job security is no longer guaranteed. Our goal should be to bring confidence and certainty to our work. That means pinpointing what our internal and external stakeholders are most worried about and delivering solutions that address those.In the case of John Whalen and UX researchers, stakeholders had questioned the certainty of insights. With AI, John and others can deliver a 10x larger sample size in more markets.Similarly, designers, writers, PMs, and developers should use AI to deliver work more confidently. You’re able to get more user feedback at every stage of the process. You can scale your work to be localized to more markets. You can automate tasks that are cumbersome and error-prone.None of this is to minimize being human-centred. But the industry has been questioning whether orgs have been perpetuating the illusion of user-centred design. Managing stakeholders’ expectations puts you closer to the decision-making process and gives you the ability to dictate how good work happens.2. Challenge the assumptions that limit expectationsNew apps are released every month that bend our perception of what’s possible. If you had collected a list of capabilities that you wished were possible, they probably exist now. Your job must be to push the work beyond the assumed limitations. To do this, you must test new apps and see if they can confidently overcome the limitations to your work. Explore new capabilities in the apps you already rely on. Experiment with combining applications that excel at key parts of your work.Being tied to a single legacy app is the worst thing you can do. You’re hitching your future to that product’s ability to be better than the dozens of other teams simultaneously trying to disrupt each other.3. Walk into every situation with clarity about your value drivers and superpowersWe can obsess over clients and our work, but understanding what you're exceptional at is more important than everything you deliver. We’re much more than our performance reports and more capable than the best project we’ve ever worked on.It requires us to be self-critical about what drives us, what limits us, and where we can excel. For example, you might identify that:* You’re envigorated by structuring and organizing * You’re envigorated by hacking solutions and testing capabilities* You’re exceptional at building alignment and support for initiatives* You’re exceptional at taking on complexity and uncertaintyThese fundamental truths enable you to dictate your path to success better:* Who you should be working for* What types of projects and roles you should be working on* What unique capabilities you should be highlighting* Which principles you should use as a north star for leveraging AIIf this is a topic you’d like to me dive deeper into, please leave a comment or send a message.4. Remember that the future is not evenly distributedThe closer you get to the centre of tech, the pace of change will increase. The gravity of the situation is exciting for some and utterly exhausting for others. Find the orbit that best suits you.If you’re reading this newsletter, you’re clearly a future-centred thinker. You can leverage that in the centre of tech to push projects and productivity to new heights. You could also work in a traditionally slower industry —healthcare, government, legal, education— and affect more change by challenging long-held assumptions.All change is relative but what brings you joy and meaning is deeply personal. Embrace that.One last and important consideration…Erika Hall speaks the uncomfortable truths that we need to hear. Follow her.Some jobs simply aren’t worth keeping. Some uses of AI are appalling. 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

Whether we admit it, like it, or believe it, we’re in a relationship with AI.That’s the first of many powerful reflections made by Sara Vienna, Metalab’s Chief Design Officer, in her must-read manifesto about how design and product must evolve. Unlike the design leaders who speculate about AI's impact, Sara and her world-class team are years ahead. They are designing disruptive AI product experiences and leveraging AI to elevate their workflows. Sara’s episode is one of the most important conversations we’ve had about the future of design and products.Listen on Spotify | Listen on Apple PodcastsShe believes that AI will change how we work and what we build. Those who embrace the potential of AI will succeed in the oncoming disruption. But most importantly, the future of product+AI will be in making five mindset shifts:They’re fundamentally principles for humanizing experiences. The hope is that AI will finally bridge the divide so products can deliver the value we’ve always wished was possible in the most humanized way possible. But there will be challenges in accomplishing this:* Most product orgs are built around the concept of delivery, not design excellence* Unlocking user data: Getting access to valuable data and knowing how to use it in a meaningful way are still more fantasy than reality* In every direction we turn, trust is being diluted* Design as we know it will need to be reborn to adapt to move from creating pixel-perfect interfaces to ones that adapt and spawn based on user interactionsAgain, I highly recommend listening to the entire episode.Thanks for reading Design of AI: Strategies & insights for product teams! This post is public so feel free to share it.Envisioning the future of design & productIf we extrapolate on Sara Vienna’s vision of how design should change, a couple of core reality checks come to mind:* Today, we can’t even conceptualize what products will be able to do tomorrow. Just like new AI tools are being released faster than we can read about them, more teams than ever are competing to deliver the use case & interaction model that will redefine a category. It’s a race to an undefined & moving finish line.* The underlying models may be the heartbeat of future products, but design will always be the brain. Products plug into whichever model suits them best at a particular moment, usually based on cost and accuracy. But just like each of our minds brings a different lived reality and way of using knowledge, the models are less important than the strategy that’s been designed into the product.* Fewer designers and product managers will yield immense power. AI automation platforms —like Make and Loveable— can effectively replicate more than half of products today. This percentage will grow until such a point that any product will soon be able to be cloned, undermining its competitive advantage. The designers and product managers working on the future of design will have the funding that enables them to compete in a global race that they’re likely to lose because they don’t know what competition they’re actually facing. The rest of us will be working to keep the lights on.Big question: How should we be using AI, today?Photoshop celebrated its 35th birthday today and is a perfect reminder of how disruptive platforms eventually become part of the boring vocabulary of the everyday.GenAI platforms, like ChatGPT, are in their infancy. Everything seems equal parts novel and confusing. We’re still unsure how to use this superintelligence, only that we should be using it. Photoshop’s rise was similar: a platform that opened up so many possibilities but whose ultimate impact wasn’t felt until it redefined the designer role many years later. What’s happening today is that employees are smuggling AI into work and this makes sense given the recent McKinsey report that finds that leaders are slow to adopt because of risks and a lack of vision.Our research finds the biggest barrier to scaling is not employees—who are ready—but leaders, who are not steering fast enough.Anthropic, the maker of Claude, published their Economic Index report and found that AI use is most prevalent in computer & mathematical occupations. Their AI model is mainly used for programming and administrative tasks.What the data also show is that design and creative tasks aren’t core use cases, yet. And rightfully so, large language models best serve requests about processing content and code, not pixels and ideas. A report about how generative AI is used in journalism showcases this by highlighting that even the creative tasks are largely operational ones, like resizing images and animating.This data highlights the divide in how leading organizations, like Metalab and Superside, leverage AI compared to the everyday user. While the average person uses Midjourney to generate stock art, leading designers automatically generate localized creative based on design systems and content guidelines.The reality is that product teams have three core workstreams:* Operations: Planning, organizing, editing, describing (e.g. Notion)* Creativity: Ideating, revising, collecting, analyzing (e.g. Cove)* Productivity: Deciding, planning, organizing, explaining (e.g. ChatGPT)Every designer, product manager, writer, producer, and researcher completes tasks in these three workstreams. And every one of you should be taking the time to break down your typical workflows into discrete activities so that you can explore what AI solutions can either augment or automate non-critical tasks.An example of this is how Kyle Soucy is using AI to streamline person and journey map creation. This type of knowledge work is considered sacred by traditionalists but as you can see in her article, she’s broken down her workflow to find effortful tasks to be augmented/automated. We can question AI’s accuracy all we want. We can challenge if models were trained ethically. And we can debate what percentage of your job may benefit from using AI. What will not change is the undeniable truth that the intelligence and capabilities of these models and tools will only improve. The sooner we embrace that truth, the better positioned we are to control our own fates. For example, a recent study evaluated AI vs. human-generated therapy responses from 13 expert therapists (clinical psychologists, counseling psychologists, marriage and family therapists, and a psychiatrist). The report found (questionable) data to indicate that users couldn’t tell if a human or an AI made the responses. The AI also outperformed human therapists on empathy, professionalism, and cultural competence.We’ll soon reach a point where generative AI can output designs that are indiscernibly human or automated. In this near-term reality, the role of designers must evolve or be replaced.A recommended action plan for how you should be using AI today:* Plan projects and workstreams using templates, resources, and added context* Communicate ideas and insights better by using AI to iterate and expand* Question the rationale, assumptions, and factors that impact the project goal* Compile inspiration, ideas, and information that will broaden your thinking* Analyze larger data sets and more sources than you could have before* Challenge your concepts by making variants and exploring new directions * Create more deliverables by automating localization, multiple formats, and generating content based on systemsIf you enjoy this content, please make sure to listen to the Design of AI podcast on Spotify and Apple. Make sure to follow us and rate the show if you like the show!Add me on LinkedIn if you want to ask any questions or discuss a project. 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

Up until recently Miro was the innovator’s defacto collaboration platform. In recent years a long list of apps added similar functionality to eat away at the online whiteboard segment. Our latest episode with Ioana Teleanu, Miro’s former Lead Product Designer for AI explores the challenges and opportunities of leveraging AI to enhance an existing product.Listen on Spotify | Listen on AppleKey takeaways:* When a product experience is already good, do we need to add AI?* AI makes it easier for more products to enter your category and add unexpected competition* Adding AI forces product teams to ship quickly to be able to learn, sometimes with uncertainty attached* You must consider if AI is the right solution to the problem you’re trying to solveIf you have any questions about these or other AI questions, reach out to us and we can help you upack what it means for your product.Thanks for reading Design of AI: Strategies & insights for product teams! This post is public so feel free to share it.Next week’s podcast episode features David Boyle who makes a case for why AI is transforming what we can learn about audiences and how those insights will improve our ability to strategize.Featured articlesAI Agents: How Businesses Must Adapt or Risk Obscurity (Arpy Dragffy)Ethan Mollick is right: #AIAgents are going to fundamentally change how websites, apps, and APIs are structured. But the implications go far deeper. We’re rapidly moving from a world where users seek out information to one where it is pushed to them by AI agents acting on their behalf. This shift has profound consequences for businesses of all sizes, and those that fail to adapt risk disappearing into the noise that these agents must sort through. (Read full article)The Wild Future of Commerce & The Rise of Conformative Software (Scott Belsky)This edition explores forecasts and implications around: (1) wild expectations for the future of commerce, (2) the era of “conformative software” that becomes more tailor-made as you use it, and (3) some surprises at the end, as always. (Read full article)25 Themes for 2025 (Bronwyn Williams)A cheat sheet of the 25 top things I'm watching unfold in 2025 (See presentation)Always remember… a good learner appreciates being proven wrongBiggest story of the week: Deepseek Ovetta Sampson is one of the must-follow voices in AI, bringing a rational perspective to an otherwise nonsensical chorus of voices. Read the full article here.Deepseek is the new Chinese model that is the biggest AI story of the year (so far). Yes the model is Chinese and may be compromised. But what’s most compelling about this story:* Despite the US places restrictions and pumping country-sized funding into AI, the model outperforms every model made in the USA.* Just like China has done in countless other industries (e.g. Shein and Huawei), they create copy-cat products that deliver 80% of the value for significantly lower cost.* We went into 2025 thinking OpenAI won the AI model wars that we’d all be subjected to whatever pricing they forced upon us. WRONG. Deepseek and many more will now come along and chip away at that expectation. More analysis about China’s disruptive Deepseek model:* Why DeepSeek Prompted a $1 Trillion Tech Sell-Off (Business Insider)* 🐳 I just finished a deep dive into DeepSeek’s latest R1 model & paper. I am genuinely impressed by their approach. A few thoughts. (Reuven Cohen)* Running DeepSeek r1 32B locally is kind of depressing. (Ethan Mollick)* DeepSeek is out, the Stock Market crashed and Silicon Valley is in tears. Here's what happened in the last 48 hours and what it means for your business (Tobias Zwingmann)* The labour market over the next few years is going to be a complete 💩show thanks to super smart, super cheap open source Ai. Here’s why. (Reuven Cohen)* What if I told you the $500B Nvidia selloff is missing the point entirely? I believe AI Revolution Just Had Its "iPhone Moment" (Simon Taylor)New AI products worth trying outStorm Stanford’s new platform enables you to build a paper about nearly any topic with summary and references. Helpful to get a deep dive into big topics, from your desired perspective.RileyNew market research platform which guides users through questions about their product, competitors, and data available. As a proof of concept of where things may head it is compelling.Thanks for reading Design of AI: Strategies & insights for product teams! Subscribe for free to receive new posts and support my work. 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, Lisa Weaver-Lambert dispels the belief that is incapable of delivering impact in her book "The AI Value Playbook." She also lays out principles for succeeding in your implementation of AI:1. Your tech stack determines winners: Orgs that already were built to process and leverage data as part of core decision making are at a huge advantage. Especially those that are focused on leveraging insights to learn and iterate.2. Leadership and strategy matter: The vision, guiding principles, and culture matter. They will dictate the strategy or lack of a cohesive strategy.3. AI shouldn’t be added on top: AI should be viewed as the pathway ro removing layers, friction, and complexity.4. Getting from proof of concept to value is harder: AI reduces the barrier to creating proof of concepts while also layering in a lot more uncertainty about how to make it production-ready.5. Centralize AI strategy & decentralize implementation: Orgs should have a cohesive strategy owned by a centralized team. But the workflows and use cases defined by the teams that are seeking to gain specific value.Listen on Spotify | Listen on Apple | Watch on YoutubePlease rate the podcastIf you’ve listened to the podcast, please help us by giving us a rating. It helps us get in front of more people and know that what we’re publishing is delivering value.Rate us on Spotify | Rate us on Apple PodcastsAnd if you have comments, questions, or suggestions: info@designof.ai New report showing use of Anthropic (Claude) doubled, while OpenAI lost 1/3Menlo Ventures published their 2024 report: The State of Generative AI in the Enterprise. It shows the continued maturation of the AI market and clear use cases where the tech is being leveraged. Not surprising, task-level use cases that can be directly evaluated/audited are coming out on top. Also, the layers of AI stack are becoming more distinct with some products starting to create their own moats. As we move into 2025 expect the Data layer to split as more orgs realize that they need a semantic layer to structure and make sense of first-party data.Thanks for reading Design of AI: News & resources for product teams! This post is public so feel free to share it.The LLM market share data makes OpenAI look like the big loser. But I suggest throwing out the 2022 and 2023 data since adoption was so low and leveraging the tech for experimentation rather than impact. 2024 is the year when AI became the workhorse for the first time powering countless products. Nonetheless, it is compelling to see Anthropic and Claude shoot up. Their focus on UX seems to be paying dividends, that or OpenAI’s dilution of trust is.Of no surprise, prompt engineering is falling off a cliff. It was a bandaid approach for a tech that had no standards yet. For reference a business that built their product through prompts often had to rebuild all those prompts whenever a model was updated. Thanks for reading Design of AI: News & resources for product teams! This post is public so feel free to share it.AI use & impact assessment surveyPlease share your experiences and point of view in our year-end AI research study.Your lessons and opinions will shape a critically important assessment of how & if AI is positively impacting individuals and teams.Less than 5-minutes of your time will help us a lot.Perplexity is one-upping Google by introducing AI-powered shopping journeysPerplexity, the upstart GenAI search form is firing shots at Google by taking a refreshing look at shopping. Rather than focusing on someone searching for a product (e.g. Patio furniture), they are taking a very human-centred approach by focusing on what a user is trying to accomplish (e.g. renovate my outdoor living space). The platform then provides ideas, support, and instructions. Plus, recommends products to buy.While this is immensely helpful, it brings up the ever-present concern that AI will pick winners and losers for us. Where Google served up dozens or hundreds of results and encouraged us to make our own decisions, AI only shows a handful of options. This is the beginning of the platform as expert and it could change how we interact with the world in a huge way. It could lead to small merchants being shut out or even grow distrust of options that aren’t recommended by a platform.Alarming data showing that achieving AGI could destroy market wagesEconomics at the International Monetary Fund have modeled data that shows that if Sam Altman & crew succeed at bringing AGI to the world faster than expected, it could set into motion a total destruction of market wages (aka devalue everything).Their model also showed that on the expected timeline of AGI, wages will continue to rise as humans continue to do the thinking for the machines.Read the reportThanks for reading Design of AI: News & resources for product teams! Subscribe for free to receive new posts and support my work. 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