Chief Program Officer → Chief Product Officer at a global startup accelerator.
I architected MassChallenge's pivot from generalist accelerator to five-sector platform, redesigned how we match volunteer experts with startups, and built the AI-native tooling layer underneath all of it from first principles. This chapter isn't in past tense yet. It's the one I'm writing.
A global accelerator looking for its next chapter, right as generative AI landed.
MassChallenge runs one of the largest early-stage startup accelerators in the world. 16+ years, 5,000+ alumni, thousands of mentor touchpoints. By early 2024, the organization was ready for a structural pivot. From generalist accelerator to a sector-focused platform built around five high-impact verticals: health, climate, sustainable food, financial inclusion, and security & resiliency.
The pivot resolved into a three-product architecture. Traction programs deliver sector-specific scale support to revenue-generating startups. Challenge programs are sector-specific innovation cohorts run with anchor partners. Custom engagements are bespoke partnerships with corporate, government, and philanthropic funders. The 15-year industry-agnostic Early Stage program was retired in 2025 once the three-product model was running on its own.
I joined as Chief Program Officer with responsibility for Programs, Community, and Marketing. Together that's about 40% of the organization's headcount and a $3M operating budget. The remit was to architect the pivot, redesign the founder and mentor experience, and rebuild the tooling layer underneath everything. I was promoted to Chief Product Officer in September 2025 as the platform vision expanded.
The title shift followed a marketing rebuild. The original plan had me providing advisory support to an existing VP. When that team was replaced, I took the function over and rebuilt it at roughly half the prior cost: $1.32M down to $650K. The combination of expanded remit and cost discipline is what made the title change make sense.
The timing was right for something I'd been waiting to do. Generative AI had landed commercially in late 2022. For the first time, the tooling cost curve for a non-technical team to build meaningful internal software had collapsed. Same pattern I'd seen at One Degree Solar with LEDs and at Formlabs with desktop SLA. A technology going from expensive and specialist to cheap and generalist. MassChallenge was a place where that unlock could matter in weeks, not years.
Architected a generalist-to-sector-platform pivot across strategy, team, stack, and operating model.
The pivot wasn't cosmetic. We redesigned how the organization qualified and onboarded cohorts, how programs got delivered across five sectors, how the mentor network was organized, and how we measured outcomes. Some of that was structural. Most of it was operating discipline: weekly cadence, accountability design, and the cross-functional handoff work that either compounds or collapses an organization depending on how seriously you treat it.
The board also directed a parallel shift toward more mature startups. Companies with revenue, traction, and near-term capital needs, rather than pre-product concepts. 2025 cohorts represent $96M in collective revenue and $260M in funding. Roughly 2x prior-year totals.
We revamped the flagship Early Stage program, moving from a one-size-fits-all curriculum with 120+ startups to smaller, sector-specific cohorts of 25-35 founders each. Each cohort is anchored by personalized mentorship and advanced support from industry Experts in Residence. For the first time we set clear performance and commitment expectations, and we removed teams when they didn't meet them. We overhauled internal operations. We empowered the Community team to vet and nurture our pool of volunteer judges and mentors. We reimagined the digital tools, both the Accelerate platform for applications, judging, and matching, and the new MC Hub for program delivery and alumni engagement.
The result is measurable. Portfolio NPS moved from 47.9 the year before I arrived to 68.46 in 2025. Startup CSAT is at 89% year to date. Mentor CSAT at 84%. 100% industry-match rate across Challenge programs. The internal team is measurably happier and higher-performing too, which is the harder thing to engineer and the easier thing to feel.
The sector pivot couldn't actually run without a coherent vocabulary underneath it. When I arrived, the org was trying to match mentors to startups, design cohorts, and tell partners "we have N companies in your space" using twenty different industry and expertise fields scattered across HubSpot, AXLR8, Airtable, and profile types. The expertise field alone had 244 values, nested three levels deep. One value was literally a validation error someone had saved as a tag. Seven parallel taxonomies were coexisting in the same system: NAICS, LinkedIn, Innovation Categories, Industry Clusters, Big Bets, Partner Taxonomy, ES Innovation Sectors. None of them agreed with each other.
Sixty days of stakeholder interviews and field-by-field audit later, we collapsed the architecture to three fields. A Primary Industry (7 values, the sector bets). A Secondary Industry (54 sub-sectors, each with a written definition and a list of relevant technologies). An Expertise tag (8 categories). One source of truth across systems. No nesting in production.
That spine is what made everything else possible. The 100% industry-match rate across Challenge programs is a function of having a taxonomy that holds. The seven-touchpoint CSAT framework can compare results across sectors because the sectors are stable categories. The LLM sector classifier maps to these seven primary industries, which is why it could replace a keyword approach with a 26% error rate. Partner economics in the HealthTech franchise depended on being able to count startups in a way the old data model couldn't support.
The most structurally important piece of work was redesigning how MassChallenge captures, qualifies, nurtures, and places volunteer experts with startups. We call it the Network. At scale, an accelerator isn't really in the training business or the capital business. It's in the network-orchestration business. The whole game is matching the right mentor, investor, or operator to the right founder at the right moment.
The old model had legacy constraints. Mentor qualification was informal. Matching was human-intensive and inconsistent. Placement data was scattered across tools. I redesigned the full lifecycle (capture, qualify, nurture, place) and built AI-native tools underneath to run it at scale. This is the work most directly analogous to portfolio operations inside a venture firm, which is where my thinking about what comes next has been going.
Concretely, this past program year, the Community team manually vetted 1,005 new expert applications and ended the year with 1,594 vetted, active experts in the placeable pool. Satisfaction across the seven measured touchpoints in the founder journey has stayed above the 80% target. Experts and startups are now operating from a shared vocabulary that the prior data model couldn't support. A network of this size is only useful to founders if matching is consistent and measurable, and that's the work the redesign was built to do.
The Healthcare franchise is where the sector model proved itself first. I served as internal executive sponsor, using the six years at Formlabs to shape sector strategy across medtech, biotech, and digital health, with a health equity throughline. The franchise (BCBSMA HEBA, Healthcare Challenge, Traction Healthcare) operates at $2M in annual partner support across 10 partners and 75 startups. Anchor commitments included Lyda Hill at $2.6M over two years and Breakthrough Biotech at $1M across Taiwan and Israel.
Partner economics like this weren't available under the prior agnostic program model. They needed a real sector spine. Healthcare became the template the Climate and Security & Resiliency franchises were built from. Same pattern I'd seen before: pick the vertical with the clearest pain, build the proof point, let the pattern travel.
Not a "using AI" initiative. An AI-native operating layer.
By 2024 I was building functioning AI tools inside MassChallenge instead of commissioning them. The stack is Python, pandas, React, Anthropic API, Netlify. The specific tools matter less than the stance. AI infrastructure is now a core operating capability, and I wanted to be the person inside the organization who treated it that way.
A full-stack search tool indexing all 4,486 MassChallenge alumni companies across 16 years. Filter by sector, sub-sector, geography, funding, confidence tier, program year, and founder demographics. Natural-language AI search surfaces fuzzy matches. A query like "companies that help people with special needs" returns 48 relevant companies sorted by relevance tier instead of keyword hits. Shipped on Netlify, used daily by Programs and Community teams.
An internal tool built for the 2025 Rare Humans gala, an event honoring MassChallenge alumni who took an unusually long path to breakout. The tool scores every alumnus against a specific narrative filter (4+ years from MC program to a major funding milestone) and surfaces a ranked shortlist with supporting evidence: press, valuation, founder demographics, gap bars showing the years between cohort year and breakout. Purpose-built for a single decision, shipped in days.
An interactive dashboard analyzing 15 years of the MassChallenge portfolio: $27.1B raised across 4,486 startups, 59% active, 111 acquisitions, 12 IPOs, 14 unicorns. Six tabs covering portfolio growth, challenge-area composition, funding pipeline, survival benchmarks, founder demographics, and cohort-year dynamics. Built as internal strategy infrastructure and made publicly explorable at mc-impact-dash-v1.netlify.app.
A five-part LinkedIn series turning the portfolio dataset into narrative: the strategic pivot from Cross-Industry dominance to focused Challenge Areas, the $20.6B sector breakdown, the power law in unicorn outcomes, the healthcare sub-sector ecosystem dynamics, the time-in-market artifact that distorts short-term cohort metrics. Used internally as strategy material and externally as positioning.
LLM sector classifier. The foundation layer. It replaced a keyword-based approach with a 26% error rate that had made the database effectively unusable. The LLM-classified sectors brought error rates down meaningfully and made the Explorer, the Dashboard, and the data journalism all possible. None of the downstream work would have survived the underlying noise.
MC AI penetration analysis. A 2023-to-2025 cohort study using the classifier and a four-tier taxonomy (AI-native, AI-enabled, AI-adjacent, none). Full cohort: 32.2%. Software subset: 57.4%. 2025 software cohort: 69.9%, within ten points of Carta's startup benchmark. The year-over-year climb is visible: 27.3 → 33.7 → 38.0% on the full cohort, 49.3 → 59.5 → 69.9% on software. The analysis informed our 2026 sector strategy and replaced a prior keyword scan that had returned a misleading 33%.
AI content generation tool. Built on the Anthropic API with Netlify serverless functions to support specific content workflows across Community and Marketing. 2026 marketing framework. Excel planning model paired with an interactive HTML dashboard, replacing what had previously lived in static slide decks.
What this changed about the work, more than any single tool, is that AI infrastructure now sits inside my scope as an operator. Same way cloud infrastructure or analytics sat inside an operator's scope a generation ago. Knowing what these tools can and can't do, where they fit in an operating stack, and how to deploy them for actual leverage comes from building them. Not from reading about them.
The data spine is the work I'd point to first if anyone asked what made the rest of MassChallenge possible. Twenty fields collapsed to three. 244 expertise tags down to eight. The taxonomy is what lets mentor matching, sector cohorts, partner segmentation, and CSAT measurement all hold together. The visible wins compound on the invisible one.
The Network redesign is the piece I'm most proud of, because it's what most directly affects what founders actually get from MassChallenge. Placing the right person at the right moment is what an accelerator does. Everything else is scaffolding for that.
And the AI builder work has changed what my scope as an operator looks like. Being able to ship working tools rather than just commission them changes what kinds of bets are feasible and how fast a lean team can move. That's the capability I've been building inside this chapter, and it's the one I'm most interested in carrying into whatever comes next.