Ardis Kadiu

@Dave_Geoghegan_ Sometimes taste just comes from deep knowledge in a vertical specialty. you learn what’s actually good vs bad, what works and what doesn’t. That’s how you get good at editing the crap

@ardis the taste point is underrated. when everyone can ship code, the differentiator becomes knowing which problems are worth solving and having opinions about how the solution should feel

I truly believe that when code is cheap knowing what to build matters most, knowing and having taste is an edge when building products. Deep domain expertise matters more than ever. This mechanical engineer shipping real software with Claude in 8 weeks is the perfect proof. (The experts are coming.)

AI for education woks.

First time I’m really using this account to talk about what I’m building. Would love your thoughts — reply, quote, or just tell me what part of family life feels heaviest for you right now. Andi isn’t another app. It’s the intelligence layer we all wish we had. → Join the waitlist (it’s free) 👉 andi.ai

Go see it for yourself — the site says it better than I can andi.ai We just opened the waitlist and already have 2,100+ families lined up. If you’re a parent who’s exhausted from being the family OS… this one’s for you.

Hey everyone — I almost never post here, but today I’m breaking that rule. For years I was my family’s operating system. Every babysitter briefing. Every grandparent visit. Every “wait, what’s Noah allergic to again?” text. My wife Diana carried even more of it. And then our kids (13 & 11) started using AI… but that AI didn’t know them at all. I got tired of it. So we built something.

the most underrated hire right now is a great product person. when i say product person i'm def not talking about a product manager. perhaps i think there has to be somewhat of a new role. i don't have a good name for it yet but maybe something like "product thinker".. someone with an intuitive grasp of the product as it exists, where it's soft, where it sings, & how to iterate it toward something even sharper. in some sense, this person has to cohesively hold in their head where this product should be 2 years from now & work backwards from that. i say this cuz when building was hard, engineering was the bottleneck & the status hierarchy often reflected that. building is no longer hard. which means the variance in outcomes has shifted almost entirely to judgment on what to build, how to sequence it, & how to talk about it. & the story matters as much as the thing. internally, it organizes the team around a shared model of why. externally, it shapes the interpretive frame users bring to their first experience. you can't retrofit narrative onto a product & expect it to land, it has to be load bearing from the start. the rarest version of this person sits at the intersection of culture & deep technology. someone genuinely bilingual. they know what's technically possible & they know which cultural currents are real vs. ephemeral. that combo is what separates products that feel inevitable from products that feel assembled. before ppl clap back with this person has always been valuable, i know.. i am just saying now they might be the most *important* person in the room. their value compounds like never before.

So true

Great holiday read.

Great thread.

Don't think of LLMs as entities but as simulators. For example, when exploring a topic, don't ask: "What do you think about xyz"? There is no "you". Next time try: "What would be a good group of people to explore xyz? What would they say?" The LLM can channel/simulate many perspectives but it hasn't "thought about" xyz for a while and over time and formed its own opinions in the way we're used to. If you force it via the use of "you", it will give you something by adopting a personality embedding vector implied by the statistics of its finetuning data and then simulate that. It's fine to do, but there is a lot less mystique to it than I find people naively attribute to "asking an AI".

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.

Collaborating on the same thread is so powerful. OpenAI has tried to make collaboration possible with shared projects and even shared links before however all of that led to duplication. I can see this being very popular with families.

"When you think about it, the act of selling 'software-as-a-service' from one business to another is a fascinating exercise in applied astrophysics. Really. "Each company is its own star system. It has a central, massive body—its core mission—and orbiting planets: the various departments, employees, and assets, all held in a delicate gravitational dance. They have established physical laws, a certain orbital mechanics that dictates how they operate. "Now, you, the sales professional, you are not merely a visitor. You are an interstellar event. You are introducing a new celestial body—your software—into their stable system. And the fundamental question is, what will the gravitational consequences be? "Your cold call? That's the first radio signal detected from a distant star. Mostly noise. But your demo... ah, the demonstration... that is a sophisticated simulation. You're using the universal language of mathematics and logic to model a new future for their solar system. You're demonstrating how your 'software' can act as a warp drive, bending the spacetime of their productivity, allowing them to travel from Point A to Point B in a fraction of the time. "An objection is not a rejection. It is simply unaccounted-for gravitational turbulence. It's an asteroid field that was not on your initial charts, but one whose trajectory can be calculated and navigated. "And when you 'close the deal,' what have you actually done? You've achieved orbital capture. Your two systems are now gravitationally bound. But here's the truly mind-altering part: The connection isn't one-way. The data, the feedback, the very relationship itself creates a subtle, but measurable, perturbation in your own system. It's a form of corporate quantum entanglement. Once linked, the fates of these two commercial entities, across the vast, cold vacuum of the marketplace, are intertwined. "So, B2B sales isn't just business. It is the practice of shaping new corporate constellations. It is the architecture of economic galaxies. And that... is cosmic."

China just won.

@Zboozee @Jason YES!!

Never been so excited for an All In pod to drop… Ya better make sure they freaking deliver @Jason !