Nick Miller

@jet_hr_italy @payfit @buzzhire @eatfirst @jumiakenya @zalora

[Lenny Rachitsky @lennysan]

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.

[Nick Miller @nickam30]

@krandiash SONIC

[Nick Miller @nickam30]

@FabrizioRomano @grok price?

[Harry Stebbings @HarryStebbings]

2. People Are Going to Lose Their Jobs on Mass

We do not need SMB sales reps, marketing managers, customer success…

More of these people were unemployed 12 months ago than you think.

As soon as AI is even 80% as good as a human, they’re all gone.

Love to hear your thoughts on this @eliast @severinhacker @destraynor?

[Jason ✨👾SaaStr.Ai✨ Lemkin @jasonlk]

More here: https://t.co/G3KcbXv6zZ

[Tanvi Ratna @tanvi_ratna]

Trump’s new tariffs aren’t a trade tweak—they’re the first move in a full-spectrum reset.

$9.2T in debt matures in 2025. Inflation lingers. Alliances are shifting.

One announcement just set a dozen wheels in motion.
Here’s what’s really happening—and why it matters 🧵

[Nick Miller @nickam30]

@HarryStebbings Benchmarking vs rare outliers will never make you happy. Triple triple double double is a great growth rate in the real world.

[Siyuan Huang @siyuanhuang95]

The CEO of Unitree, XingXing Wang, posted a dancing video at Rednote against the hype that the previous dance video was AI- or CG- generated.

The dance is performed before a mirror and with sound, which makes it 100% real. Really cool demo!

#Unitree #Humanoid #RobotDance

[Joe Falter @joefalter]

A handful of companies built in the next 3 years will define the trajectory of our largest industries for the next 50

[Startup Archive @StartupArchive_]

Shopify CEO Tobi Lutke: It’s impossible to make great products if you don’t give a sh*t

“Every product in the world, at the end of the day, is simply a reflection of how much the people who created it gave a sh*t… It is not possible to make great products if the people who work on it do not give a sh*t about the product.”

Tobi continues:

“I actually think this is a very important role for product leaders: To make sure that the team gives a sh*it… The product leader has to give a sh*t. Do not engage in work on a product that you don’t care about.”

Tobi believes anyone who wants to build great products has two roles:

1. “You have to understand the thing that’s being done better than everyone else.”

2. “You’ve got to be exothermically infectious with actually caring about this thing because just that one thing alone will make a 10x better product. It’s crazy how much of a change this makes.”

Video source: @lennysan (2025)

[Wall St Engine @wallstengine]

SemiAnalysis published an analysis on DeepSeek, addressing recent claims about its cost and performance. $NVDA

The report states that the widely circulated $6M training cost for DeepSeek V3 is incorrect, as it only accounts for GPU pre-training expenses and excludes R&D, infrastructure, and other critical costs. According to their findings, DeepSeek’s total server CapEx is around $1.3B, with a significant portion allocated to maintaining and operating its GPU clusters.

The report also states that DeepSeek has access to roughly 50,000 Hopper GPUs, but clarifies that this does not mean 50,000 H100s, as some have suggested. Instead, it’s a mix of H800s, H100s, and the China-specific H20s, which NVIDIA has been producing in response to U.S. export restrictions. SemiAnalysis points out that DeepSeek operates its own datacenters and has a more streamlined structure compared to larger AI labs.

On performance, the report notes that R1 matches OpenAI’s o1 in reasoning tasks but is not the clear leader across all metrics. It also highlights that while DeepSeek has gained attention for its pricing and efficiency, Google’s Gemini Flash 2.0 is similarly capable and even cheaper when accessed through API.

A key innovation cited is Multi-Head Latent Attention (MLA), which significantly reduces inference costs by cutting KV cache usage by 93.3%. The report suggests that any improvements DeepSeek makes will likely be adopted by Western AI labs almost immediately.

SemiAnalysis also mentions that costs could fall another 5x by the end of the year, and that DeepSeek’s structure allows it to move quickly compared to larger, more bureaucratic AI labs. However, it notes that scaling up in the face of tightening U.S. export controls remains a challenge.

[sam lessin 🏴‍☠️ @lessin]

Short all the structured CRM companies... a personal example with NotebookLM.

[Nick Miller @nickam30]

insights from a great ceo

[Brian Halligan @bhalligan]

I wrote down literally everything I've learned about the journey from startup founder ($0) to scaleup CEO ($25billion). This thread is the on the lessons in LEADING. Several more threads coming on different topics.

[Rowan Cheung @rowancheung]

In a recent interview with T-Mobile, Sam Altman compared o1’s current state to the ‘GPT-2 stage’ of reasoning models

He also revealed that the development of o1 unlocks a much quicker path to fully capable AI agents

Hear it from the man himself:

[Morning Brew ☕️ @MorningBrew]

Wow—Andy Jassy announces that Amazon employees are expected to be in the office five days a week starting January 2nd, 2025

[Nick Miller @nickam30]

@patelkavit Whatever your opinion on that, I don’t think it can be blamed directly on Kamala

[Nick Miller @nickam30]

@patelkavit I’m referring to Trump refusing the outcome of an election. A real thing that happened 4 years ago. This isn’t a theoretical thing a candidate “would love” to do, it’s something that actually happened.

[Amjad Masad @amasad]

AI is incredible at writing code.

But that's not enough to create software. You need to set up a dev environment, install packages, configure DB, and, if lucky, deploy.

It's time to automate all this.

Announcing Replit Agent in early access—available today for subscribers:

[Shreyas Doshi @shreyas]

As performance review season gets underway in many places now & through Q4, here’s a rough model to help you think about how you are perceived in almost every mid-sized to large company.

Your work is generally observed and perceived [0] by others along the following 3 dimensions:

1) Content: this is about the insights & ideas you have, the proposals you make, how you solve problems, the things you ship, the metrics you move in the short-term, the business impact you create in the long-term, etc.

2) Confidence: this is about the image you project as you do your work, do you seem to have things under control, do you seem to be able to tackle tough tasks, how you communicate, do you come across as “leadership material”, are your peers and people above / below in the hierarchy confident in you, etc.

3) Context: this is about your sensibility around your company’s implicit culture, how you adapt your approach to your org’s power structure, that important exec’s quirks & preferences, general biases of important peers & stakeholders, etc.

Now, here are some crucial observations to consider as you think about how you’re perceived and how that affects your odds of getting promoted:

A) In most companies, if you are extremely good at just 1 of these and average / below average at the other 2, you are going to “get stuck” beyond certain levels (usually Manager / Sr. Manager will be your ceiling).

This is unfortunately the cause of a lot persistent frustration for otherwise-talented people who are GREAT at Content, but repeatedly get passed over for promotion to higher levels.

They don't understand why this keeps happening. And usually no one explains to them the perception side of things i.e. no one explains that it is usually because they are not projecting as much Confidence as they ought to for the next level and they are not as attuned to the Context of the org & the company [1].

B) To have a chance of getting Director / VP level scope, you must be very good at a minimum of 2 of these and you must not suck at the 3rd one.

And btw, this is how you get different types of leaders at the mid / upper management level of a company.

e.g. a leader who spikes on Content + Context but not on Confidence is going to have a VERY different style than a leader who spikes on Confidence + Context but not on Content.

This observation alone will explain a lot of confusing promotions, where someone seems not competent-enough to be a senior leader, but yet they somehow are the one chosen for the VP job [3].

C) Employees who get promoted to and show longevity at the highest levels (Executive / CEO) in top tier companies tend to be very good at all 3, especially Context.

Last but not the least: as with any model, this is by no means a perfect predictor of how things will always work everywhere. But hopefully this helps clarify some perpetually confusing things that happen in our career & in the careers of people around us.

~
Footnotes:

[0] If you haven’t already noticed, everything here — Content, Confidence, Context — is about how you are perceived i.e. how Optics plays a role in who gets ahead in midsized & large companies.

Now naturally, no company will ever admit or explicitly tell you that this is directionally how things work. They will point to the career ladder and give you some technical reason why the committee’s interpretation was that you did not fulfill one or more of the promo criteria.

[1] This is not to say that this is right. IMO it is quite wrong. However, me just saying THIS IS WRONG isn’t going to change the long-held opinions and dogmas of the members of your org’s promo committee in November 2024 or January 2025.

[2] In an ideal world, it’s your true Impact & your Execution that should dictate how you get recognized & rewarded.

Unfortunately though, in any sufficiently large group of humans the idea of “just do good work and let your work speak for itself” doesn’t work optimally. In good companies it will work for you early on, but even in those companies it will stop working at some point, at some level.

When you reach that level, it is fine to decide to opt out of this game (I did that at some point, just before I started this next chapter of my career). But if you do that, be clear on the reasons why you’re doing it so you can remain steadfast while others seem to be playing silly games to get ahead.

And even if you want to opt-out at some point, the onus is on you to make sure you’re able to create the life you want to create for yourself from that point forward.

[3] This is very likely the most interesting observation in this post for those of you who pay attention to why certain people get promoted / selected for certain senior roles.