The Twenty Minute VCSaam Motamedi: Why Series B Won’t Make Money & Why $1M ARR is a BS Milestone for Series A | E1177
CHAPTERS
Frothy seed & Series B markets and why most VCs aren’t helpful
Saam opens with a blunt take: today’s seed pricing (often tens of millions, sometimes $100M+ post) and the Series B market feel even frothier than peak 2021. He frames the core Greylock mindset: independent thinking matters, and many investors add little value to founders.
- •Seed rounds frequently priced at many tens of millions, sometimes $100M+ post-money
- •Series B potentially frothier than 2021 despite a different macro backdrop
- •“Playing the game on the field” can be dangerously costly in venture cycles
- •Greylock’s view that many VCs are not meaningfully additive
- •Sets the tone for skepticism on valuations and investor behavior
Childhood foundations: debate competitiveness and small-team research mindset
Harry asks what shaped Saam growing up in Houston and moving to California for college. Saam credits policy debate for competitive drive and biomedical research for belief in small teams accomplishing outsized work—both now central to his investing style.
- •Policy debate built love of competition and performance under pressure
- •Biomedical research taught leverage of small, high-talent teams
- •These experiences translate into venture competitiveness and team evaluation
- •Early formative work: cancer detection research and publishing
- •Personal background frames his investing instincts
Is venture a young person’s game? Wiring, intensity, and the competitive reality
Saam mostly agrees venture favors the young—but argues it’s less about age and more about being wired for relentless competition. He points to his partner Ashim’s 20+ years of high-intensity work as proof that elite performance can persist with the right temperament.
- •Venture is brutally competitive; intensity is non-negotiable
- •Age matters less than personal wiring and willingness to grind
- •Example: Ashim’s responsiveness to great founders and 7-day work ethic
- •The business is “lagging,” so underperformance is noticed late
- •Competitive drive is a persistent advantage
We’re in an AI bubble: valuation dislocation vs public multiples and retention uncertainty
Saam answers directly: yes, AI investing is in an exuberant bubble—possibly bigger than 2021. He highlights the disconnect between private multiples (100–200x revenue) and public comps (often 15–20x), plus weak evidence on long-term retention for many fast-growing AI apps.
- •Seed and growth-stage AI valuations often extreme (100–200x revenue)
- •Public-market software leaders trade far lower (roughly 15–20x forward revenue)
- •Bubble could only make sense with unusually persistent, durable growth
- •Corporate/strategic investors distort pricing due to non-financial incentives
- •Many AI apps show spiky adoption with unclear long-term retention
How Greylock decides: data-driven excitement plus fundamentals on defensibility and market dynamics
Harry challenges whether investors must “play the game” when traction is explosive. Saam explains Greylock’s two-lens approach: respect breakout data, but still underwrite core fundamentals like retention, product-market fit, and defensibility—otherwise they’re willing to pass even on meteoric growth.
- •Lens 1: explosive data shifts the burden of proof to ‘why not invest?’
- •Lens 2: fundamentals—market dynamics, retention, defensibility—still decide
- •Willingness to pass even after rapid revenue ramps if fundamentals are weak
- •Early AI app categories have already shown post-hype deceleration
- •Avoiding FOMO is a deliberate strategy
Differentiating in crowded AI apps: it’s still SaaS—workflows, distribution, and teams
Responding to concerns about many lookalike AI tools, Saam argues this is the same problem SaaS investors have always faced. The winners separate through superior founders/teams, deeper workflows, stickier product value, and compounding distribution—rather than novelty of the AI layer itself.
- •Crowded competitive sets are normal in SaaS; AI just increases intensity
- •Selection focuses on founder quality and management strength
- •Defensibility comes from workflow depth, stickiness, and pricing power
- •Distribution advantages that compound matter more than technical ‘wrappers’
- •Many point solutions will stall; a few become iconic (HubSpot, Figma)
OpenAI platform risk: ‘foundational’ primitives vs vertical workflows that will endure
Saam acknowledges OpenAI’s ruthless execution but separates where they’re likely to compete from where startups can thrive. OpenAI will attack foundational primitives (writing, editing, pure code generation), while durable opportunity remains in domain-specific copilots and workflows (legal, medical, SRE/debugging).
- •OpenAI is a credible competitive threat for foundational AI capabilities
- •Foundational primitives likely to be commoditized by platform players
- •Vertical/role-specific applications can be huge and defensible
- •Examples: lawyer/physician copilots, incident response, debugging workflows
- •Investment posture: avoid direct ‘foundational’ head-on competition unless exceptional
Foundation model layer: skepticism on API margins, optimism on model+product tie-ins (agents)
Harry asks about venture opportunity in foundation models. Saam is uncertain, but outlines a view: pure model-as-a-service may not sustain differentiation/margins, while first-party products or agent experiences tightly coupled with models can create enduring enterprise value.
- •Hard to predict AI outcomes; put ‘high error bars’ on forecasts
- •Possibility of only 1–2 durable model leaders; differentiation may compress
- •Model APIs may struggle to sustain margins if separation is small
- •Best opportunity: model tightly integrated with product (agents, coding)
- •Customer service AI is cited as an area where startups should focus above the model
Is SaaS ending? Why AI revives the chance to rebuild systems of record (data, delivery, interface)
Saam strongly rejects the ‘end of SaaS’ thesis, arguing AI makes it the best time in years to build new core platforms. He explains why new horizontal systems of record are rare and how disruption happens only when the data model, delivery/pricing, or interface changes—conditions he believes AI now enables.
- •Disagrees with ‘companies will just build their own software’ narrative
- •Big SaaS outcomes come from deep systems of record (Salesforce, Workday, ServiceNow)
- •New systems emerge when data model, delivery model, or interface shifts
- •AI can upend CRM-like data schemas by synthesizing from raw interactions
- •Agents and generated UIs can replace clunky interfaces and create new platform openings
Pricing power in the AI era: hybrid models and paying for ‘work replaced,’ not features
The conversation turns to whether AI kills per-seat pricing and whether customers will pay more for AI add-ons. Saam predicts hybrid pricing and says real pricing power comes when software replaces meaningful labor (e.g., an “AI BDR”), not when it merely makes existing tools marginally better.
- •Per-seat pricing may persist but increasingly becomes hybrid (seat + usage/work)
- •AI add-ons alone may not justify higher prices (Box/Notion examples)
- •Markets corrected after earnings showed slower-than-hyped monetization
- •True willingness-to-pay rises when tools replace discrete work, not augment it
- •Example: an AI BDR booking meetings is materially monetizable
Seed & Series A pricing: what’s ‘normal’ now and why power-law math changes the price debate
Harry asks what pricing looks like on the ground. Saam describes current ranges (seed often 20–40 post, sometimes right after incorporation; Series A often 80–200 post) and argues that, for exceptional teams, quibbling over small price differences matters less than securing exposure to power-law outcomes.
- •Typical seed pricing for strong teams: ~20–40M post; can be pre-product/pre-revenue
- •Series A pricing for early PMF: ~80–200M post at the high end
- •Power-law outcomes dominate; intermediate outcomes don’t move fund performance
- •Price sensitivity within a narrow band is often irrational for top-tier opportunities
- •Portfolio-level discipline still matters even if exceptions are warranted
Ownership, check size psychology, and the Greylock bar: time is the true constraint
Harry challenges whether big funds can maintain standards when checks feel “irrelevant.” Saam responds that Greylock invests sparingly (often 1–2 deals per partner per year) and emphasizes accountability and time commitment—sometimes spending months with founders before investing—making selectivity structurally necessary.
- •Greylock’s limiting factor is partner time, not capital availability
- •Partners make few investments; many start as small checks that scale over time
- •Deep pre-investment work is possible if relationships begin pre-company
- •Accountability to founders is treated as a long-term obligation
- •Ownership target historically 20–25%+, but flexibility has increased
Signaling is ‘bullshit’: choosing the right board, not collecting brands, and how follow-ons work
Saam takes a strong stance that signaling risk is overstated, arguing strong companies raise regardless of whether the seed investor leads the next round. He describes a founder-centric approach: only add new investors if they will truly change the company, otherwise keep the cap table tight and avoid non-additive board seats.
- •Saam argues signaling concerns rarely matter in practice
- •Example pattern: different firms lead seed/A/B without harming outcomes
- •Greylock encourages founders to build a short list of truly additive board candidates
- •Most VCs are not helpful; incremental ‘brand’ is often unnecessary
- •Follow-on decisions are framed around what’s best for the company, not ownership accumulation
Reserves strategy: playing offense and defense—supporting winners and saving companies in air pockets
Saam explains Greylock’s reserve mindset: keep meaningful capital available because early positions can require large pro-rata later, and because even great companies can hit “air pockets” where insiders must step up. Reserves are not just about doubling down on winners, but also about protecting companies through difficult stretches.
- •Reserves are managed flexibly rather than by rigid formula
- •Small initial checks can scale into very large total positions over time
- •Insider support is critical when strong companies face temporary setbacks
- •Inside rounds may be defensive, not offensive, and require prepared reserves
- •Maintaining flexibility is part of long-duration early-stage investing
Why $1M ARR is a bad Series A filter: founders + markets first, traction second
Saam critiques the common Series A heuristic of requiring $1M+ ARR, arguing it’s a weak predictor of building a $100M+ ARR company. He prefers starting with founder quality and market dynamics, using ARR as a secondary proxy for early fit; he cites Wiz raising a major Series A at $0 ARR as an example of backing the right team in a major transition.
- •$1M ARR is a misleading milestone for Series A selection
- •Most companies that hit $1–2M ARR never reach $10M, $50M, or $100M+
- •To ‘make money’ at Series A in SaaS, you need a path to hundreds of millions ARR
- •Example: Wiz had $0 ARR at Series A but exceptional founder and market timing
- •Investment order: founder + market → then ARR/traction as confirmation
Founder vs market debate: ‘great founder, poor market’ decisions and painful missed opportunities
They explore the hardest underwriting question: investing in a great founder with a questionable market. Saam shares a practical heuristic (good zip code vs wrong zip code) and notes they’re more willing at seed, but admits many mistakes come from underestimating truly iconic founders and failing to “win the right to invest.”
- •Markets have gravity; at later stages bad markets become harder to escape
- •Heuristic: good ‘zip code’ but wrong ‘street’ is fixable; wrong ‘zip code’ is not
- •More willingness to back founder over idea at seed, but it still causes big mistakes
- •Key self-check: is the founder truly iconic (very high bar)?
- •Missed deals often came from not competing hard enough for exceptional founders
Series B won’t make money (as a vintage): new capital, fewer top assets, and ‘field’ thinking risk
Saam argues growth investing isn’t dead for elite companies, but the Series B basket may underperform because pricing is exuberant, competition is intense, and public multiples haven’t re-expanded. He attributes froth to new growth pools at large early-stage platforms and fewer truly top companies, and rejects the idea that you must “play the game on the field.”
- •Best companies can still raise growth rounds; median-company data misleads
- •Series B pricing may be frothier than 2021 for scarce top-tier assets
- •Capital shifted from traditional crossover funds to large early-stage platforms’ growth vehicles
- •Without public multiple expansion or extreme growth durability, the B vintage may not return well
- •Independent thinking beats ‘playing the game on the field’ during exuberant cycles
Operating as a great VC: sourcing advantage, career advice, and the closing quick-fire
In quick-fire and career reflections, Saam emphasizes sourcing as the hardest and most important part of venture—especially for small teams competing with large platforms. He advises young investors to focus less on personal deal tally and more on building a durable reason founders would choose them, and he closes with views on incubations, investors helping pre-PMF, and what he’s changed his mind on (focused models moving up the stack).
- •Sourcing is the core bottleneck; servicing can be optional for the best founders
- •Young investors should build a distinct ‘reason to work with me’ over chasing volume
- •Incubations usually fail due to cap table overreach and founder selection, though some are legendary
- •Investors can help pre-PMF via ICP focus, segmentation, and customer feedback
- •Changed view: focused models can win by moving quickly into workflow/app layers (ElevenLabs example)
