Product-Market Fit Treadmill

In fast-moving markets, PMF must be recaptured every 3 months, not sustained for years

Elena Verna
10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey)

Product-Market Fit Treadmill

"Every three months, I feel like we have to recapture our product-market fit and not just recapture on the same technology and with same customers. It's both of those pieces of the equation change every three months, and it's terrifying in a way." - Elena Verna

What It Is

The Product-Market Fit Treadmill describes a fundamental shift in how PMF works for companies in fast-moving markets, particularly AI. Traditional PMF thinking assumes you find product-market fit once and then spend years scaling it. The treadmill concept recognizes that in rapidly evolving markets, both the "product" (what's technically possible) and the "market" (customer expectations) change so quickly that PMF must be continuously recaptured.

Previously, the rate of PMF evolution was measured in years—you'd spend extended periods in "blitz growth stage" scaling your original PMF before needing to find a second horizon. Now that cycle has compressed to months.

How It Works

Two forces compress the PMF cycle simultaneously:

Product Side (Technology):

  • Underlying capabilities (like LLMs) change with each model release
  • New possibilities open up every 3 months that weren't possible before
  • You can't just wait for technology to improve—you must build ahead and have functionality ready when models catch up
  • What was cutting-edge becomes table stakes almost overnight

Market Side (Expectations):

  • Consumer expectations have never changed this fast
  • What users expected 8 months ago is completely different from today
  • Users quickly abandon products that don't meet new capability baselines
  • The gap between "wow, this is amazing" and "why doesn't it do X yet?" closes rapidly

The Result:

  • Traditional scaling playbooks don't apply
  • The team that finds PMF must also be able to scale it (repeatedly)
  • You can't separate "PMF team" from "scaling team" anymore
  • Growth efforts must constantly throttle between scaling and reinvention

How to Apply It

  1. Accept the new reality - Stop expecting PMF to be a milestone you pass once. Build organizational muscle for continuous PMF discovery.

  2. Watch both sides - Monitor both technological capability shifts AND customer expectation changes. Either can invalidate your current PMF.

  3. Build ahead of technology - Make bets on what will be possible in 3 months. When the model releases, you should already have functionality ready.

  4. Shorten planning cycles - Traditional annual planning is too slow. Work in shorter sprints with regular PMF reassessment.

  5. Keep discovery muscle active - Don't let your team's discovery skills atrophy. You'll need them again in 3 months, not 3 years.

  6. Balance pioneers and majority - Focus on recapturing pioneers while being mindful that the latent majority may get left behind if you move too fast.

When to Use It

  • AI-native companies - If your product is built on rapidly evolving AI capabilities
  • Fast-moving categories - Any market where technology and expectations shift quarterly
  • Competitive markets - When competitors can quickly match your capabilities
  • Planning and resource allocation - When deciding how much to invest in scaling vs. discovery
  • Team structure - When building teams that need both PMF discovery and scaling capabilities

Cautions

  • This framework is most applicable to AI and fast-moving technology sectors
  • Traditional companies in stable categories still operate on longer PMF cycles
  • Don't use this as an excuse to avoid finding PMF—you still need to find it, just more frequently
  • The adjacent user theory still applies—be careful not to alienate the latent majority by only serving pioneers

Source

  • Guest: Elena Verna
  • Episode: "10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey)"
  • Key Discussion: (01:00:49) - Elena explains how PMF cycles have compressed from years to months in AI
  • YouTube: Watch on YouTube

Related Frameworks

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