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Benchmark’s General Partner Chetan Puttagunta joins GTMnow to give founders/operators an inside view to how they invest and think about go-to-market.
He breaks down the $5.6B legal AI company that grew from $1M to $100M ARR in 18 months (despite a competitor already raising at $3B before they launched), Benchmark’s first-ever $2B growth fund, and tips on scaling in the AI era.
Discussed in this episode
Why the first $1M now takes longer than the next $99M in the AI era
How top AI startups compress a 180 day sales cycle into 30 days
The “trusted vendor” playbook for breaking incumbent distribution advantages
Why Legora embedded inside a law firm for a year before launching
How a magical demo plus a tightly scoped pilot collapses six month deals
Why value is moving from the product build to the service and outcome
What Benchmark actually looks for: technical insight that creates demand pull
Why direct sales and forward deployed engineers are exploding in AI
How buying one AI app triggers an enterprise to buy 100 more
Episode Highlights
1:13 - Why Max + Paul were furious taking notes on this one
2:10 - Benchmark’s $2B growth fund and the changing strategy
4:57 - POC to trial and the power of direct sales in AI
11:33 - Meeting Max early: $0 to $100M in 18 months
15:05 - Manus: 0 to $100M in eight months through PLG
17:10 - Will the AI native window close?
19:09 - Sizing the window: $40B software vs $1T services in legal
21:50 - How Benchmark picks the winners
25:31 - Turning a 180 day sales cycle into 30 days
30:45 - The Legora deep dive: research, pilots, legal engineers
41:46 - Why $0 to $100M keeps getting faster
42:29 - The harder problem: getting to your first $1M
44:04 - When code goes to zero, what do customers pay for?
48:46 - Sales led vs PLG in the AI era
51:49 - How to spot the right founder
55:18 - What company Chetan wishes someone would build
56:48 - Investors founders should follow
57:40 - Working with Jack Altman at Benchmark
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Key takeaways
In AI, the code is the cheap part.
When the cost of writing code trends toward zero, the product itself stops being defensible. The moat moves to everything that isn’t code: the customer research, the trust, the last-mile work of making a business measurably better - and of course, distribution.
The great inversion: building got slower, scaling got faster.
The dominant narrative says AI collapses build time. Chetan flips it. Going from $1M to $100M in ARR has never been faster, but getting to that first million may now take longer than it did in the cloud era, because the hard last-mile problems have to be solved before launch. Legora spent over a year embedded inside a law firm before they had something worth selling. The build stretches out while the scale compresses, and founders who plan for the old shape will be caught off guard.
The return of direct sales.
Everyone crowned product-led growth as the AI-native motion, the Cursors and Lovables of the world growing on credit-card swipes. Chetan’s view is that the next and far larger wave gets built on direct sales. Enterprises signing $1M to $10M contracts in a chaotic market aren’t buying features, they’re buying a trusted vendor to de-risk every new model release. The leanest AI companies run efficient engineering and traditional-size go-to-market, and that isn’t changing soon.
The new enterprise speed record, and what it actually cost.
Legora went from $1M to $100M ARR in 18 months, launching against a competitor already valued at $3B. The headline is the speed. The lesson is how they earned it: a year of unglamorous research, shadowing lawyers, and hiring practicing attorneys as “legal engineers” to sit beside senior partners. The speed was the output of patience, not the absence of it.
Software is becoming a service business.
If a sophisticated buyer can vibe code a simple version themselves, a thin product earns nothing. Chetan sees value migrating to the expertise around the code and the outcomes it delivers, which makes modern software look more like a service-provider model paid on results. The MVP as a revenue event is fading.
The physical ceiling of AI.
Asked what company he wishes existed, Chetan went straight to the constraint nobody can engineer around fast enough: the United States can only build so many data centers. His last bet, StarCloud, puts them in space. What sounded outlandish five years ago reads as realistic in 2026, and it points to a decade of building compute wherever inference is possible.
Follow Chetan Puttagunta
X / Twitter: https://x.com/chetanp
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Paul’s LinkedIn: https://www.linkedin.com/in/paulsirving/
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