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Hi, I'm the engineer that built out Vercel's first AI SDR agent mentioned below👋
AMA
AMA
My biggest learnings from Jeanne DeWitt Grosser (ex-Chief Business Officer at @Stripe, now @Vercel COO):
1. What failed seven years ago now works with AI. In 2017, Jeanne tried to build a system at Stripe that would automatically personalize outbound emails based on company data. Despite working with world-class data scientists, it failed due to too many errors. Today, that exact same approach works. This shows how AI has made previously impossible ideas suddenly viable.
2. A single GTM engineer at Vercel reduced a 10-person sales team to 1 (in just 6 weeks). Jeanne’s team at Vercel had an engineer build an AI agent that handles inbound lead qualification, outbound prospecting, and deal loss evaluation. The agent costs $1,000 per year to run versus over $1 million in salaries for the sales team. The nine displaced team members moved to higher-value work rather than being laid off, and the remaining salesperson is 10 times more efficient.
3. Their AI deal-loss bot has become better at understanding what went wrong than humans. When Jeanne analyzed her biggest loss of the quarter, the salesperson blamed pricing. But an AI agent reviewed every email, call transcript, and Slack message and discovered the real reason: they never spoke to the person who controls the budget, and when ROI came up, the customer clearly didn’t believe the value claims. They are now using AI to analyze sales calls in real time and send alerts like “You’re halfway through the sales process and haven’t talked to a budget decision-maker yet.”
4. Wait until $1 million in revenue before hiring your first salesperson. Founders should continue selling themselves until they reach around $1 million in annual revenue with a repeatable process. The key is having a defined ideal customer profile—customers who look alike.
5. Segment customers on what drives their buying decisions, not just company size. OpenAI has roughly 3,000 employees, which would typically put them in the “mid-market” category. But they’re a top-25 website globally by traffic, so Vercel treats them as enterprise customers requiring complex sales. Effective segmentation combines company size with growth rate, web traffic, workload type, and industry—because selling to e-commerce companies requires completely different language than selling to crypto companies.
6. Most customers buy to avoid risk, not to gain opportunity. About 80% of customers purchase to reduce pain or avoid problems, while only 20% buy to increase upside. This means you should focus your sales messaging on what could go wrong without your product—like falling behind competitors or damaging their reputation—rather than just talking about exciting features. This is especially true when selling to larger companies, where individual careers are on the line.
7. Sales teams should be indistinguishable from product managers—for a bit. Jeanne hires salespeople who have such deep product knowledge that if you put one in front of a group of engineers, it should take 10 minutes to realize they’re not a product manager. This credibility allows sales teams to serve as an extension of research and development—a 20-person sales team talks to hundreds of customers weekly and can translate those conversations into product insights at scale.
8. Building your own AI sales tools may beat buying off-the-shelf software. Because AI is so new and every company’s sales process is unique, Jeanne finds that building custom internal agents often delivers more value than buying vendor solutions. A single go-to-market engineer built their deal analysis bot in just two days, perfectly tailored to their specific workflow. These engineers shadow top salespeople to understand their workflows, then build automation that would have taken months or been impossible just a few years ago.
9. Make every sales interaction great, whether customers buy or not. Jeanne replaced boring discovery calls at Stripe with collaborative whiteboarding sessions where customers drew their payment architecture. Many customers had never visualized their own systems before. They left with a useful asset and a feeling of collaboration, regardless of whether they bought. Many returned years later to purchase. Think about your go-to-market process like a product, not just a sales function.
10. Product-led growth has a ceiling—no $100 billion company runs on it alone. While product-led growth (where users can sign up and start using a product without talking to sales) works well for early growth, customers generally won’t spend a million dollars through a self-service flow. Every major technology company eventually builds a sales team for larger deals. The mistake is waiting too long, since building a predictable sales process takes time.
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Regardless of the GPU build out, I'm very bullish on standard enterprises adopting AI and growing earnings durably over the next 10 years.
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It’s kind of wild how much build vs buy has changed.
Now I build the whole thing before deciding to buy.
Now I build the whole thing before deciding to buy.
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My read: Charlie would love the Anthropic S-1 but would be thinking through this:
AI labs are great businesses. Add in brand love and I think Munger would call these wonderful businesses.
> The fact that Ant revenue has been 10x-ing year over year, while compute has only been 3x-ing, suggests that there are very strong economies of scale in the model business
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S-1 confirms it. It's (not) so over drew.tech/newsletter/iss…
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you can measure everything now
the trick is to not micro manage as a result
the trick is to not micro manage as a result
A thing I see happening at large organizations as a result of AI is the expansion of the role and span of control of senior management who can directly monitor the whole firm. It vastly changes company operations. It also is straightforward to implement compared to firm redesign.
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I love new agent first onboarding flows so much. Simplicity.
Bye, Hotjar and PostHog!
I want to record user sessions. I don't want to watch hours of videos. I definitely don't want to interpret them.
I made a tool that records your users and lets Claude Code watch the recording and figure out how to get more users to convert.
It's got:
🎥 Snippet to record, just like Hotjar
🤖 MCP to feed Claude and let him analyze
🏆 Goals, so Claude knows what you're pushing for
🤑 Free for the first 5k visits/month
try it and let me know what you think!
getuserproof.com
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The photographers who leaned into the digital and social waves made great businesses
drew.tech/posts/commodit…
drew.tech/posts/commodit…
The levels of cope I've been seeing lately are surreal
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When cameras got cheap, studio photography jobs fell by a third.
But there are more photographers now than in 2004. 67% work for themselves.
Software developers have a lot to learn from the photography industry.
drew.tech/posts/commodit…
But there are more photographers now than in 2004. 67% work for themselves.
Software developers have a lot to learn from the photography industry.
drew.tech/posts/commodit…
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Read Charlie Munger. Study your own motivated reasoning. Study confirmation bias.
Then spend an hour journaling about how sad you feel about AI taking away the feeling of being in flow.
Or don't and get stuck on "anger" in the five stages of grief.
And it will cost you.
Then spend an hour journaling about how sad you feel about AI taking away the feeling of being in flow.
Or don't and get stuck on "anger" in the five stages of grief.
And it will cost you.
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A few corollaries:
- your token use is the average token use of the 10 people you surround yourself with
- yes, you’re early, but that still doesn’t give you an excuse to coast due to how exponentials work
- a large percentage of people’s meaningful AI use will come from products that embed AI into them, not coding agents
- the alpha to applying AI to a sector of the economy is exactly inverse of how many early adopters that sector naturally has
- diffusion is memetic per sector (people like us do things like this)
- fields in Mediocristan are pretty safe from disruption
- fields in Extremistan are most at risk. If your field was already full of barbell-type outcomes this will likely increase
There are no signs we’re running out of ways to scale. Sounds like swarms are the next one.
- your token use is the average token use of the 10 people you surround yourself with
- yes, you’re early, but that still doesn’t give you an excuse to coast due to how exponentials work
- a large percentage of people’s meaningful AI use will come from products that embed AI into them, not coding agents
- the alpha to applying AI to a sector of the economy is exactly inverse of how many early adopters that sector naturally has
- diffusion is memetic per sector (people like us do things like this)
- fields in Mediocristan are pretty safe from disruption
- fields in Extremistan are most at risk. If your field was already full of barbell-type outcomes this will likely increase
There are no signs we’re running out of ways to scale. Sounds like swarms are the next one.
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
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Right on schedule (this is the start)
x.com/ClaudeDevs/sta…
x.com/ClaudeDevs/sta…
Sign in with/Bring your OpenAI credits with you is going to make so many banger one-time sale apps.
Token cost is the only reason this renaissance hasn't already started.
As soon as OpenAI opens this up broadly, the flood gates will open.
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