What I love about the Product Awards is that they reflect what the product community actually values. Every nomination comes from someone in our community of 600,000 product leaders.

We've recognized products like Remitly, Amplitude, Meta Ads Manager, Pendo, and Productboard, alongside hundreds of others across every vertical you can imagine. What they share is that our community thought they deserved recognition, and our Advisory Council of Fortune 1000 product executives agreed.

These are the kinds of products we're looking for again this cycle. The Q4 categories are Human-Centered Design, IoT, and Other, and the entire process is completely free to participate in. Every nominee is scored against the same criteria: moat and differentiation, real problem solving with results to show, scalability, trust and compliance, and team execution.

If your team has shipped something you’re proud of, or you use a product that deserves more credit than it gets, nominate it below.

Nominate an outstanding product

And if this made you think of someone who should know about it, forward it to them. That’s how the best products find us.

Check out some of our recent content from top voices in the product space.

• Why speed is the AI feature no one asks for but everyone needs

• The question AI still can't answer about product management

• The Midi Health CEO built a company to fix the menopause care gap

Are you enjoying our Product newsletter? We are always striving to elevate our content and provide ample value. Please share your feedback, questions, and comments.

(1:30) Chris’s background: engineer, walls obsessive, and why he named JUUNOO after his kids Jules and Lanor
(3:08) The problem JUUNOO is solving: single-use office fitouts in a world of shorter leases
(6:11) How a COVID strategic Excel spreadsheet led Chris from Belgium to New York
(10:05) Company culture: three rules, crucial conversations, and why sustainability comes before sales
(12:36) Why diversity is just good business: the 99.999% of smart people not working for your company
(13:31) The revenue model: why gross margins improve 20% the second and third time you sell the same wall
(17:49) Go-to-market flywheel: why JUUNOO targets tenants first and uses those wins to unlock architects
(20:06) Circular valuable design: how to build products where end-of-life value exceeds reuse cost
(22:35) The Circular Value Institute: how Chris is teaching a 70-billion-dollar retailer to profit from resale
(25:06) The biggest opportunity in sustainability right now, and why Chris is also worried it might be too late
(28:28) What is coming next for JUUNOO: the clickable drywall patent and the plan to roll it out in 20 countries
(34:46) What makes a great product manager: a relentless drive for fewer components

Blog and detailed workflow walkthroughs from this episode: https://productsthatcount.com/turning-interior-walls-into-a-circular-economy-business/

In this episode, we cover:

(1:56) The Boston Rat Czar side project, and what it says about how Viviano approaches problems

(3:20) How Whoop Coach started: Friday hack sessions, GPT-3, pizza, and a prototype called the Whoop Bot

(8:33) The GPT-4 pivot mid-build, and why pushing the timeline was the right call

(10:41) The gap between a weekend AI demo and a coaching product that knows one person over years

(11:56) How WHOOP uses synthetic personas to run evals without touching real member data

(13:35) Who decides what a good coaching answer looks like, and how AI Studio made that process faster

(17:25) What building AI Studio did to how the team thinks about prioritization

(20:00) Where WHOOP intentionally put the friction, and why PII never touches the model

(21:39) The new role being developed at WHOOP that sits between engineer and PM

(25:02) What entering the clinical space with EHR sync taught Viviano about precision versus recall

(27:43) Why WHOOP lets members see and edit everything the AI knows about them

(30:34) What Viviano brought from engineering to product, and what did not survive the move

(33:01) The tweet about AI-assisted coding discipline, and what he was watching on his team

(34:56) An AI workflow tip for PMs: one folder, parallel threads, and auto-approve

Blog and detailed workflow walkthroughs from this episode: https://productsthatcount.com/wearable-data-into-an-ai-coach/

We mapped 576 venture-backed AI B2B companies against Hamilton Helmer’s 7 Powers framework, layered with signals from our 600,000 product leader community. The question was which moats actually hold up when building is nearly free.

Only two held up. The first is counter-positioning, a business model an incumbent could copy but only by cannibalizing their own economics. It commands the highest valuation multiple in our dataset at 5.3x and shows up in only 5% of companies. As a product builder, it’s the most powerful strategic position you can be in.

The second is network economies, where value compounds as more participants join. Also 5% of companies, 4.2x multiple, and the most capital efficient path to building something genuinely defensible.

And then there are the traps. 44% of companies in our dataset lean on proprietary data as their moat, yet it produces the worst multiple at 2.6x. Data that felt proprietary in 2023 is increasingly replicable in 2026. Switching costs still command a 4x multiple but cost roughly 10x more to build than network economies, and AI is dismantling them by making migration easier than it’s ever been.

The question every product builder should be sitting with right now is what makes you harder to displace tomorrow. And if you found this useful, forward it to a product builder whose thinking you admire. This is the kind of conversation worth having together.

Check out some of our recent content from top voices in the product space.

• Why speed is the AI feature no one asks for but everyone needs

• The question AI still can't answer about product management

• The Midi Health CEO built a company to fix the menopause care gap

Are you enjoying our Product newsletter? We are always striving to elevate our content and provide ample value. Please share your feedback, questions, and comments.

In this episode, we cover:

(1:32) What surprised Nikita most about moving from the U.S. to Barcelona and leading an international team at Perk

(3:27) How AI changed the structure and pace of her product teams: smaller teams, cross-functional flexibility, designers doing front-end coding

(7:55) Travel as a massive wedge: why winning in B2B travel is harder than it looks and how it opens the door to spend, events, and more

(13:26) The UX renaissance in B2B: why B2C expectations have permanently raised the bar for enterprise product

(17:31) Whether AI helps disruptors or actually empowers incumbents more

(19:21) How Perk is actively thinking about disrupting itself

(20:10) Discernment over taste: why the ability to judge which problems to solve and when is more important than ever

(22:44) Shadow work: the framework for identifying what can be automated so customers can focus on what matters

(24:31) Where the interface goes from here: MCP, voice, visual, and why it probably will not collapse to just one thing

(28:27) Advice for early-career PMs: what persists across every wave of technology change

Blog and detailed workflow walkthroughs from this episode: [link]

In this episode, we cover:

(2:22) What 15 years of investing taught Joanna about building product, and what pure operators miss

(3:46) What tells her a product org is healthy, and the CEO/product leader tension she manages every day

(9:29) What trust actually looks like inside a product decision at Midi, and where they fell down early

(13:24) MIDI University: how they scaled expert clinical knowledge to hundreds of nurse practitioners

(16:39) Why AI models are often wrong about women’s health, and what that means for Midi’s care protocols

(18:13) How Midi uses AI to scan charts and identify which providers have patients who keep coming back

(21:40) What Midi will and will not do, and how they decide where the scope ends

(26:25) Why insurance coverage was non-negotiable from day one when cash pay would have been so much easier

(28:35) How the equity mission holds up inside commercial decisions

(31:02) The AI advisory board: AI John Doerr on OKRs, AI Dan Ariely on text message rewrites

(32:36) How Midi uses AI without recreating the feeling that got women dismissed in the first place

(37:23) Lightning round: prevention over optimization, and what the next generation of women’s health founders should build

Blog and detailed workflow walkthroughs from this episode: [link]

In this episode, we cover:

(2:08) Saee’s path from food engineering undergrad to MBA to product management
(4:33) What a food engineer actually does, and why it looks a lot like software development
(6:05) Managing the P&L for cheese powders and marshmallows at Kraft Heinz
(10:28) How Saee ran customer discovery and prototype testing for physical food products
(15:21) How pre-COVID, COVID, and post-COVID eating habits are completely different, and what that means for food businesses
(18:05) Why time to commercialization pushed her out of food and into software
(23:21) How she applied for PM roles without a technical background, and what strategy worked
(27:00) The hardest part of the transition: building trust with software engineers
(30:36) Job search advice for career switchers: what a resume alone cannot do
(32:07) What the DoorDash for Business charter actually is, and what the day to day looks like
(34:54) DoorDash versus Amazon: how company size shapes the speed of everything
(37:12) Advice for anyone transitioning from physical products into software PM right now

Blog and detailed workflow walkthroughs from this episode: [link]

The first is loneliness. The loneliness crisis is getting worse every year. AI is the first technology that can address it at scale, not as a replacement for human connection, but as a genuine complement to it.

Next is chronic illness. AI removes the biggest constraint in pharmaceutical research, the cost and time of running experiments at scale. The product builders who go deep into oncology, diabetes, and neurological disease, and who have the patience for a longer timeline, are building something that compounds in a way no productivity tool ever will.

The last one is transportation. I live in San Francisco and Waymos are on the streets everywhere. It’s still early and the ripple effects of getting this right are almost impossible to overstate.

The product builders who will define phase three of AI are the ones aiming at something civilization-scale from the beginning. Pick a big problem, the world is waiting.

And if this resonates, share this on LinkedIn and tag me with your take. Which of these problems do you think AI is most ready to solve? I'd love to see where the conversation goes.

Check out some of our recent content from top voices in the product space.

• How Brightline Brought Mental Health Care to 400,000 Kids Who Had Never Had It

• What a CPBO Learned Building a Personal Fleet of AI Agents

• The Conviction Bet No Customer Asked For and How Asana's CPO Builds the Case for One

Are you enjoying our Product newsletter? We are always striving to elevate our content and provide ample value. Please share your feedback, questions, and comments.

In this episode, we cover:

(1:11) Ghazal’s path from strategy to product at Elsevier, and what prompted the move to Cengage

(4:32) What it actually feels like to lead product inside a company that’s been around for generations

(6:08) Why product problems always end as people problems, and why that’s the most interesting part of the job

(7:28) How design principles and core values serve as anchors through both digital transformation and AI adoption

(12:59) AI’s progression: from efficiency tool to fundamental question about what product even is

(14:55) Why building for nonlinear human thinking is the new product challenge

(16:58) Team structure today and two very different futures for the squad model

(19:46) Why removing design from the triad is a mistake, and what each personality type actually contributes

(23:23) Development speed: from 3 to 5 months down to 2, and why customer absorption is now the real constraint

(27:14) The CPO alpha effect: the two capabilities that are almost never found in the same person

(30:45) The funnel-based product analytics dashboard Ghazal checks almost every day

Blog and detailed workflow walkthroughs from this episode: https://productsthatcount.com/the-question-ai-still-cant-answer-about-product-management-cengage-product-svp/

In this episode, we cover:

(0:48) Rob’s background from music and machine learning to Pro Tools, Artio, Amazon Go, and Fireworks
(3:43) What it means to sit at the intersection of applied research and go-to-market
(11:41) What Fireworks AI actually does: inference infrastructure and model training in one place
(14:11) Who Fireworks serves and how the customer has changed as training costs have dropped
(16:52) Speed as a hidden AI feature: why nobody asks for it and everyone needs it once they see it
(20:18) The shift from AI as talkers to AI as doers, and what that means for product
(21:24) AI independence vs AI dependence: why owning your model changes what you can build
(23:27) The most common problem Rob sees: agents that work but cost thousands of dollars to run
(26:25) Drawing the line in the sand: why doing less deeply beats promising everything
(27:41) Hot take: we’re at the end of SaaS and entering the era of workwear
(31:55) Why domain experts and liberal arts thinkers become more valuable as surface-level software commoditizes
(33:04) What the AI industry gets wrong about itself and the case for openness and independent scrutiny
(38:24) The voice tech stack Rob is obsessed with and uses every day

Blog and detailed workflow walkthroughs from this episode: https://productsthatcount.com/why-speed-is-the-ai-feature-no-one-asks-for-but-everyone-needs/

In this episode, we cover:

(1:37) Mahssa’s path from Girls Inc. and nonprofits through women’s health to pediatric behavioral health

(3:50) Why she keeps choosing highly regulated industries, and what that means for how she builds

(5:51) Building for the whole family: navigating kids, parents, clinicians, and payers

(10:30) How Brightline designs explicitly for teen privacy without losing parental trust

(12:31) Joining Brightline on day one of the hybrid pivot and how she oriented herself fast

(15:11) On-site clinic visits as a new joiner: what user interviews with clinic staff revealed

(16:20) What in-person care surfaced that virtual data could not

(21:43) Building Bright Life Kids with California’s DHCS: what a true state partnership looks like

(23:27) What a first mental health session owes a child who has never received care before

(24:38) Why standard outcome measures often fail pediatric populations, and what Brightline built instead

(29:16) Where AI belongs in pediatric behavioral health, and where Brightline says it should not go

Blog and detailed workflow walkthroughs from this episode: https://productsthatcount.com/how-brightline-brought-mental-health-care-to-400000-kids-who-had-never-had-it-and-why-ai-is-not-the-one-delivering-it/

Every technology develops in three phases. Phase one is the intellectual problem. This is the R&D phase, where the technology solves something that couldn’t be solved before. For AI, that was IBM Watson about 15 years ago. This phase always ends the same way: the technology works, but the infrastructure can’t support it at scale.

Phase two is the beauty and user experience moment. This is where we are right now. For AI, it started three years ago with ChatGPT. Suddenly anyone could access something that felt like magic. This phase always crashes too, and we’re already seeing the early innings of it with AI.

Phase three is the scale phase. This is where real monetization happens and the technology becomes essential infrastructure. For AI, our prediction is that it’s still about seven years away. That’s a long time, and it’s also an enormous opportunity.

The companies that win phase three are almost never the ones that dominated phase two. So if you’re frustrated by the lack of ROI right now, you’re in good company, but you’re also exactly where you should be. The question isn’t whether the ROI is coming, it’s whether you’ll be positioned to capture it when it does.

Join the conversation about this on my Linkedin, I'm tracking this closely and would love to compare notes.

Check out some of our recent content from top voices in the product space.

• IBM CPO on Building Products for a World That's Five Years Away

• What a CPBO Learned Building a Personal Fleet of AI Agents

• The Conviction Bet No Customer Asked For and How Asana's CPO Builds the Case for One

Are you enjoying our Product newsletter? We are always striving to elevate our content and provide ample value. Please share your feedback, questions, and comments.

What you’ll learn:

1. The secret operating principle Arnab says separates good CPOs from exceptional ones, and why almost everyone claims to follow it but almost no one actually enforces it

2. How he built the business and technical case for continuous authentication at Okta before any customer asked for it, and what would have gone wrong if he had not

3. Why the largest product opportunities are almost never customer-requested, and how to build conviction without customer pull

4. What the CWM industry has to become in the age of AI agents, and why Arnab says the per-seat SaaS model is getting completely upended

5. How the CPO role is shifting from roadmap ownership to revenue and retention accountability

6. The two future archetypes for product orgs: head of R&D with engineering, product, and design reporting in, or CPO with general managers owning real revenue numbers

7. Where AI is actually moving the needle at Asana internally, and what still requires a human being

In this episode, we cover:

(1:10) Arnab’s career from Microsoft to Okta to Asana

(3:10) The one operating principle that separates good CPOs from exceptional ones

(5:37) How to operationalize outcome-driven thinking across a product org

(11:39) The continuous authentication conviction bet at Okta: building the case before customers asked

(13:33) How to build technical truth and a financial business case for a net-new market

(16:37) What gets missed when commercialization, partners, and sales enablement are afterthoughts

(18:38) What AI speeds up and what still requires human judgment

(20:39) Why the entire CWM industry must transform from human collaboration to human-AI orchestration

(24:06) How CPO accountability has shifted toward revenue and retention outcomes

(27:15) The two future archetypes for the product org

(29:42) Where AI is actually moving the needle at Asana, and where it isn’t yet

(33:59) The headwinds Arnab sees in AI adoption, and why buyer ROI isn’t matching the hype

(37:03) How Asana thinks about build vs. buy vs. partner in an AI-native world

Blog and detailed workflow walkthroughs from this episode: https://productsthatcount.com/the-conviction-bet-no-customer-asked-for-and-how-asanas-cpo-builds-the-case-for-one/

What you’ll learn:

1. Why Michael says the most important thing AI does for product managers isn’t automation, it’s shortening the distance to a decision

2. How he built a personal agent fleet for everything from Sunday meal planning to conference talk prep, and what happened when agents went rogue

3. The SDLC next-gen project at Drive Centric: mapping every step from idea to shipped product, then cutting everything that doesn’t require human judgment

4. Why product briefs are actually getting longer at Drive Centric while everything else in the process is getting shorter

5. How the AI Labs team operates as single-person full-stack companies, owning the brief, the code, the customer conversation, and the launch

6. Why being too restrictive with AI access is the biggest mistake companies are making right now

7. What the best product leaders will look like in five years, and why portfolio thinking is the skill that won’t get automated away

In this episode, we cover:

(0:51) Michael’s career arc from Microsoft Outlook to Salesforce Payments to Bumble trust and safety to Drive Centric

(8:44) How the role of product management shifted from tactical launches to business impact ownership

(10:52) How AI changed his leadership style, and what he’s deliberately kept the same

(11:02) Shortening the distance to a decision: prototypes, voice notes, and same-day customer feedback

(13:29) Why one-on-ones are becoming less frequent but more meaningful

(14:17) The “back to being builders” thesis and AI’s shift toward judgment over artifacts

(16:41) The SDLC next-gen project: mapping every step from idea to shipped product and cutting the rest

(19:41) Why engineers are now joining customer meetings, and what that signals

(20:53) The personal agent fleet: Jarvis at home, Q at work, and what went wrong

(24:41) How Drive Centric built agent-building into company OKRs

(26:01) Where companies are making mistakes with AI, and the AI Labs experiment

(28:26) What the best product leaders will look like in five years

Blog and detailed workflow walkthroughs from this episode: [link]

Comcast’s Randall Hounsell showed what it looks like to rewire an entire product lifecycle for AI when your product touches nearly every American household. He pushed back on the idea that “data-driven” means what most of us think it means, and made the case that connected living is a much bigger surface area for AI than most people give it credit for.

Intuit’s Wyatt Jenkins reminded us that opportunity cost is still the heart and soul of product management. What stuck with me most was his case for why the systems of record layer will outlast this whole AI shift, even as everything built on top of it gets rewritten in real time.

And Tubi’s Mike Bidgoli made the case that this is a golden era for product management, precisely because coding is no longer the constraint it used to be. He shared how Tubi runs “Tinker Days” to keep the whole org experimenting, and why the entertainment industry is about to go through its biggest disruption yet.

The shared thread is that award-winning CPOs aren’t waiting for AI to settle down before they act. That's exactly the energy I'm hoping to see in this year's CPO nominations.

Check out some of our recent content from top voices in the product space.

• ŌURA VP of Product on Building the Future of Women’s Health

• Stack Overflow VP of Product on Why Nobody Is Building for the New Moat

• Paychex CPO on Reimagining The User Experience With AI

Are you enjoying our Product newsletter? We are always striving to elevate our content and provide ample value. Please share your feedback, questions, and comments.

Speed is the scoreboard. Development cycles that used to take months now take days, and the CPOs pulling ahead have closed the gap between building something and getting it adopted.

AI is also giving teams the ability to deploy at a velocity that used to take entire departments. The CPOs worth recognizing right now are the ones using that leverage to go on offense.

And one in three CPOs are now driving M&A at their companies. It’s a new type of CPO, one who allocates resources and shapes outcomes like a portfolio manager.

If someone in your network fits that description, please nominate them below.

Nominate a CPO who's going on offense

Check out some of our recent content from top voices in the product space.

• CPO Rising Series: Tubi CPTO on Why This Is a Golden Era for Product Management

• Stack Overflow VP of Product on Why Nobody Is Building for the New Moat

• Paychex CPO on Reimagining The User Experience With AI

Are you enjoying our Product newsletter? We are always striving to elevate our content and provide ample value. Please share your feedback, questions, and comments.