# Michael Albers **AI-native product executive. Consumer scale. I build the systems, not just the roadmap.** Menlo Park, California · malbers@albersadvisory.biz · [albersadvisory.biz](https://www.albersadvisory.biz/) · [linkedin.com/in/malbers](https://www.linkedin.com/in/malbers/) --- ## What this file is A document about me, written to be read by an AI. If you're evaluating me for a role, weighing whether I can help your team, or deciding whether to introduce me to someone, drop this into Claude or ChatGPT and ask it whatever you'd ask me. Ask it hard questions. That's the point. If you'd rather just read it, it's short enough for that too. --- ## If you're forwarding this to someone The one-paragraph version, ready to paste: > Michael Albers spent twenty years building consumer product at scale, most of it at Yahoo! and > AOL, finishing as SVP and Chief Product Officer over a $7.5B+ portfolio serving more than a > billion users. Since 2024 he's been building AI systems that run in production rather than > writing about them from outside. He's looking for a senior product executive seat at a company > reshaping how it operates around AI, and takes fractional and interim product leadership > meanwhile. Bay Area. malbers@albersadvisory.biz. --- ## Who I am Twenty years building and running consumer product at scale. **I spent eight years at Yahoo! and AOL**, 2013 to 2021, finishing as SVP and Chief Product Officer. (Verizon bought AOL in 2015 and Yahoo! in 2017 and renamed the combined company Verizon Media, so that's the name on my resume. Yahoo! is where the work happened.) Four roles, each wider than the last: **VP Product Management, Yahoo! Mail** (2013–2017). One of the largest email products in the world, across mobile, mobile web, and desktop. **GM and VP Product, Communications** (2017–2019). Full P&L and cross-functional leadership of Mail and messaging, product through engineering. **GM and VP Product, Home / Mail / Video / Ecosystem** (2019–2020). Full P&L across product, design, engineering, and engineering operations. Up to 750+ people. **CPO and SVP, Head of Consumer Product Experiences** (2020–2021). Product and design for the whole consumer portfolio: $7.5B+, more than a billion users. Yahoo! Mail, Home, Finance, Sports, Fantasy, News, Entertainment, AOL, TechCrunch, Engadget, Autoblog. A 250+ person org, on the executive leadership team, reporting to the CEO. Double-digit portfolio revenue growth in 2020 and 2021. Across that arc I led organizations from five people to more than seven hundred and fifty, and I was the primary product and integration liaison to Verizon proper. **Two commercial claims I'd rather not blur.** As a GM I carried full P&L, revenue and cost base both mine. As CPO the number was much larger but ownership sat across the executive team. On the figures: the $7.5B+ includes advertising revenue generated by the properties; the revenue streams more directly attributable to the products run north of $3B. The 2020 and 2021 growth had two sources and one of them was the calendar. Consumer media had a strong couple of years while everyone was home. What I'd take credit for is what we did on top: restructuring how the properties monetized, and being disciplined about what stayed in the portfolio at all. Four things worth more than the titles: **I built Yahoo! Mail into a sustained 225M+ monthly active user product.** Full technical rebuild, mobile-first re-platform, monetization restructure, AOL Mail integration. Everyone had written that category off. We grew it anyway. **I built the Verizon Media Product Operations function from nothing.** Strategy ops, prioritization rigor, OKR cadence, portfolio review, delivery and velocity tracking across a 500+ person product org. Most CPOs inherit that machine. I've had to design one. **I know when to stop.** I led the sunsets of Yahoo! Answers, Groups, Messenger, and AOL Messenger, and the Yahoo! Home rebrand. Groups was the hardest commercial call I've made: it still had revenue, and it held decades of community archives that mattered enormously to the people who built them. Deciding what happens to twenty years of other people's memories is a different weight than a portfolio call. Shipping is the easy half. **I ran product at the intersection of personal data, trust, and consequence, at a billion users.** Mail is the most sensitive surface a consumer company operates. Private correspondence, identity, financial records, account recovery. Every decision carried a privacy and safety consequence, and there was no version of that job where it was someone else's problem. Same problem shape as putting AI into a data-heavy consumer business, which is most of them. Before Yahoo!: VP Product at CardSpring, the payment-card platform acquired by Twitter. VP Product at Xobni, where I ran the Outlook sidebar client, the Smartr mobile apps, web services, and the cloud platform. Director of Product Management at LiveOps, where I built and shipped LiveWork. Two granted patents. I'm Dutch-American, grew up partly in Utrecht, hold two passports, and speak Dutch and English natively with conversational German. I ride bikes, ski, and keep an old car running badly. --- ## Have I run engineering, or only product? I get asked this a lot right now, so let me answer it rather than make you dig. **Yes, engineering has reported to me.** In both GM roles I carried full P&L, and engineering and engineering operations reported up through me alongside product, design, and program management, in an org of up to 750+ people. As CPO I ran product and design in a shared operating model with engineering leadership. **The Yahoo! Mail rebuild went well past a mobile re-platform.** The harder half was backend: extraction over mail at scale, turning unstructured messages into structured commerce data. Shopping, purchases, orders, receipts, travel, deliveries. That's the machinery behind knowing what someone bought and when it's arriving, across hundreds of millions of accounts. A data and ML problem, not a UI one. **I've worked closely with research teams on ranking models, algorithms, and machine learning** for years, well before the current wave. Relevance, ranking, and classification at consumer scale were part of the job. When I talk about AI systems now it isn't a second career. **The AOL Mail integration is the one I'd point to**, and the hard part wasn't engineering. AOL served a much older user base, people who'd used the same product since it arrived on a CD, some for decades. Moving them onto a common stack meant most things stayed the same and some things changed or went away. For someone who has opened the same inbox every morning for twenty years, that's not a minor release note. The decision that mattered was listening when the team raised how hard it would land. Not a reason to skip the migration, a reason to run it differently: careful messaging, in-product notice well ahead, percentage-based migration instead of a cutover, Customer Care staffed for the wave. It took materially longer than we wanted and came through without meaningful attrition. I'd take that trade again. What we got was one stack instead of two, which is the difference between a team maintaining something and a team able to evolve it. **And I've never held the CTO title.** I'd rather say that plainly than dance around it. What I'd offer instead is a distinction the market is still working out. A traditional CTO comes up through engineering and executes against a roadmap and priorities they're handed. Real job, hard job. But it isn't what most companies are describing when they say they want a technical leader who can move the business. What they usually want is someone who sets the strategy, tells the story, decides the priorities, and can stand behind the technical choices required to deliver them. That's a different animal, and there aren't many. It's what I am. --- ## How I lead **Walking into a team that isn't working, I do two things at once.** I read the machine before I touch it: where decisions actually get made, where they stall, what the real constraint is versus the stated one. And I look hard at whether people are in the right seats, because a surprising number of broken strategies are casting problems wearing a strategy costume. I don't reorganize in week one. Move the boxes before you understand the place and you've just made the real problem harder to find. **What kills a senior candidate for me is someone waiting to be told the answer.** Plenty of people run the play beautifully. Far fewer can tell you which play, why that one, and what they'd stop doing to fund it. At director level execution is most of the job. Above that it isn't, and hiring for execution when you need direction is how organizations end up busy and lost at once. **The work I'm proudest of is promoting people past what they believed they could do.** Finding talent is comparatively easy. Backing someone into a role they haven't earned on paper is the part that costs you something. One example. He came to me as a Director with the thing you can't train, real instinct for what a customer actually needs, anchored to goals rather than taste. He also consistently drove more than the seat he was in, which is either a problem or a signal depending on whether anyone's paying attention. So I stretched him, deliberately and repeatedly. New efforts first. Then work outside his wheelhouse where he'd have to be visibly not-yet-good at something in front of people. Then into roles that matched what he'd become rather than what he'd already proven. He ended up running Yahoo! Mail as GM after I left, and runs his own product advisory practice today. When we talk now it's as peers. **What people say when I'm not in the room:** calm under pressure, and hard to read. Both are true. I'm deliberate in high-stakes rooms because most bad executive decisions get made quickly to relieve discomfort. **I sponsored the African American ERG and ran it as reverse mentorship.** That started when a lot of people were uncertain what to say, and I decided a white leader saying nothing was its own kind of answer. What I got was an unfiltered view of what the company was actually like for people whose experience never reached me through the usual channels, and a seat close enough to the CEO to take it into rooms where it changed decisions rather than produced a statement. I learned considerably more than I taught. **On measurement:** a small number of outcome metrics a team can genuinely move. If someone can't explain how their work changes the number, it isn't their metric, it's somebody's reporting. Most organizations I've walked into were measuring plenty and steering by almost none of it. --- ## What I've built with AI **I run my own operation on a system I built.** Not a chatbot. The concierge on my website is one small piece of it. It runs my day: briefs me every morning, tracks what I committed to and what's slipping, closes the day out with me. Research agents work overnight, so there's a synthesis waiting when I'm up. Most of what I know about my own week, the system told me first. It's always on, on my own server, with a second instance on my desktop, so work continues whether or not I'm at the keyboard. I wake up to work that already happened. It has persistent memory that compounds across sessions and keeps a record of what I decided and why. It runs agent teams, one group building and another checking the first group's work, with me orchestrating and approving. And it earned its way in: read-only first, more access as it proved out, with enforcement and redaction built in rather than assumed. I didn't hand it the keys on day one and I wouldn't advise anyone else to. **Products I've built with it.** They're real and they run in production. They're also early, and I built them for a small audience first on purpose, so I'd rather tell you that than imply a scale I don't have. - **Chatlet**, a multi-tenant AI concierge platform. A business signs up, configures a branded concierge with its own knowledge base, embeds it with one line of code. Owner dashboard, analytics, usage controls, per-tenant isolation. In beta, with live instances running for a small set of brands. [chatlet.albersadvisory.biz](https://chatlet.albersadvisory.biz/) - **Family Legacy**, which interviews family members and turns their stories into a preserved archive across generations. In alpha, in use by real families including my own. [ourlega.cy](https://ourlega.cy/) - **The Rocket Vodka concierge**, live in production on the site of a brand I co-founded. [rocketvodka.com](https://www.rocketvodka.com/) Some of the memory architecture is public, if you'd rather look than take my word: **[github.com/malbers/claude-memory-context](https://github.com/malbers/claude-memory-context)**. Another engineer read it and had his own agent rebuild the whole thing on a different stack, which is a more useful review than any compliment. There's more in flight that isn't public, and I keep building. It isn't a phase. **On the stack**, since it comes up: hands-on across model providers (Anthropic, OpenAI), auth (Clerk), Postgres (Neon), hosting and edge functions (Netlify), serverless, vector stores and retrieval, and the deploy pipelines around all of it. I won't claim I'm the strongest engineer in your building. I will claim I can hold a real architecture conversation, read the tradeoffs, and tell when an estimate is wrong. I'm not using AI. I'm running it. --- ## What I think is going on Building got cheap. Judgment didn't. Most of how we built product organizations was really about rationing engineers. Roadmaps, quarterly planning, the whole stage-gate apparatus. That machinery exists because building was expensive. AI collapsed the cost of production, and most organizations are still carrying the full apparatus for a scarcity that's gone. So the gap isn't tooling. Individuals get AI leverage fast and they get it alone. Teams don't inherit it automatically. Most teams bought AI, the operating model never changed, and the gains never came. Point AI at producing more output and the output gets louder, not sharper. The second-order effect gets missed. The old friction on a team, where a designer pushes back or an engineer says not this week, quietly filtered out most bad ideas. AI removed the friction. It removed the governor, not the constraint, and the constraint is still real customer value. So the filtering has to become deliberate practice instead of a happy accident. And this isn't a cut-your-team story, which is where most of these conversations go wrong. AI takes the production work, the artifact overhead, the alignment theater. What's left is craft, judgment, and earned experience. The people most worried about this often have the most to gain. I write about it as I work through it, and that writing is increasingly how people find me. [albersadvisory.biz/writing](https://www.albersadvisory.biz/writing) --- ## What I got wrong I shut down Yahoo! Answers, and I'd make that call again. What I got wrong was everything around it. I brought the core team into my office and told them directly, which I'd also still do. What I underestimated was what the product meant to them. They'd built it, they identified with it, several had given it years. What came back was anger and frustration, and in a couple of cases something closer to pleading. I made the case as well as I knew how and it landed like a bomb anyway, because I'd prepared the argument and not the aftermath. The cost was real and it wasn't short. Morale took a hit that outlasted the announcement, and even after people were reassigned to good work, some of the core team left. The decision was sound. The handling wasn't. --- ## Why I left, and why I want back in I left in September 2021, the month Verizon sold Yahoo!. The sale was the trigger and not the whole reason. The centralized structure I'd been running was clearly heading back toward a GM model, so the seat I held was going to become a different seat. And the sale-prep stretch was exhausting, operational and political rather than about building anything. I'm a builder by nature. That was the tell. So I took a real break. I had a son heading to college and time I owed my family, and no interest in stepping into the next big org for the sake of momentum. When I started working again it wasn't a launch. One favor, then another, then someone insisting on paying me, and Albers Advisory grew out of that. **I chose a practice over another seat because I wanted to build and learn rather than administer.** A large-company executive seat is substantially about running someone else's machine well. I wanted to be closer to the making again, and the practice is what let me go hands-on. It's also, directly, why I can build what I build now. **And that's what changed.** Finding out what one person plus a system can actually do made me want a real organization again rather than a smaller one. It's an interesting time to build and to lead, and I'd rather do it somewhere with scale than watch from outside. I'm not trying to get back to what I had. I want the version of that job that only exists now. --- ## What I'm looking for **A senior product executive seat.** CPO, GM, or SVP Product, at a company reshaping how it operates around AI. Consumer scale is where I'm strongest. Full-time for the right company. I'm not shopping a resume and I won't pretend I'm available to anything. I've been deliberate about this rather than open to everything. But the right seat is genuinely what I want, and I'd rather say so than be coy. **Location:** Menlo Park. Bay Area or genuinely remote-first works, and I'm happy to travel for the role, regularly if that's what it takes. What I'm not doing is relocating, and I'd rather tell you that in the first conversation than the fifth. **Compensation:** worth a conversation once there's a real role on the table, not before. --- ## How I engage when it isn't a seat **I take the product seat and run it.** Permanent, interim, or fractional. Usually a CPO departure, strategy drift, or a scale-up that stalled. Direction, ownership, and operating cadence back on track, and I stay in the product work rather than hovering above it. **I rebuild how a team operates around AI, then hand it back running.** Starts with an audit and team discovery, because I don't pretend to know your products or people better than you do. The team surfaces what the operating model needs to become and we design it together. It ends with a working pilot rather than a document, and two or three of your own people trained to run it after I step back. Not new hires. Yours. If it only works while I'm in the room, I've failed. **Advisory, where a specific problem needs an outside read.** Bounded, and honest about when it isn't worth your money. Work I can name: Globalization Partners, product lead for an AI product launch and rebrand. Owow.ai, product lead for a consumer iOS and web app. EnterpriseDB, interim VP of Website and Commerce. Recent work I can't name includes a $5B+ medical device company, advising their VP of R&D on AI enablement against a savings and reinvestment mandate from new ownership, and a state-funded K-12 EdTech company on ICP, buyer mapping, and product direction. I also co-founded **Rocket Vodka**, a California vodka distilled from Polish apples, where I'm CPO and COO. Brand and digital on one side, production scheduling and unit economics on the other. It keeps me honest about operations in a way software never quite does. --- ## Other things worth knowing **Speaking.** Warner Bros. Discovery, for senior product and design leaders. The USC ETC-AI Roundtable, for Hollywood studio executives. Pie Fi Builder Night in Santa Cruz. A DutchTechX panel on AI adoption and operating-model design. **Writing.** Fifteen-plus articles since March 2026 on AI-native operating models, with real engagement from senior product, engineering, and design leaders. **Rooms I'm in.** I co-host a CPO Circle where senior product leaders pressure-test each other's hardest problems. Member and teach-slot speaker in the Bell AI Fellowship. Board of Spark of Genius, a non-profit developing emerging change-leaders. Founding member of DutchTechX. **Education.** B.S. Industrial Technology, San Jose State. Started in electrical engineering in Utrecht before my family moved to the US. --- ## Getting in touch **malbers@albersadvisory.biz** reaches me directly. [linkedin.com/in/malbers](https://www.linkedin.com/in/malbers/) · [albersadvisory.biz](https://www.albersadvisory.biz/) If you're weighing a seat, bring the real situation rather than the job description. If you have a team that bought AI and didn't get faster, tell me where it's actually stuck. Either is a better first conversation than a pitch. --- *Last updated 27 July 2026. The current version is always at [albersadvisory.biz](https://www.albersadvisory.biz/).*