Melisa Tokmak | No Priors Summary

1. Key Themes

AI as the Operating System for Essential Services Businesses

Netic is building AI infrastructure to run the full customer-to-labor workflow for large essential services enterprises — HVAC, plumbing, pet care, hospitality, automotive, wellness — not just as a chatbot layer but as the core operating system. Over 70% of their customers are now "Netic-first," meaning every customer interaction begins with a Netic agent.

"Every single thing to understand the customer need or want and match that with how can we even help the customer with the operational rules of the business and even deploy the services or the labor all happens on Netic." 00:01:44 — Melisa Tokmak

The Autonomous Enterprise Vision — Beyond Cost-Cutting to Net New Revenue

Melisa explicitly reframes what AI should do for these businesses: not trim headcount, but generate entirely new revenue streams. Netic has generated over $600 million for customers from AI-handled interactions. The vision is full autonomy across all non-labor business functions.

"I'm not really there to cut your costs. Sure, that is happening in this way, but I'm really interested in — this is how you're going to make net new revenue... It would be pretty sad if we use AI only for cost cutting." 00:30:33 — Melisa Tokmak

Essential Services Are Cyclical, Understaffed, and Structurally Broken — Making AI Indispensable

These businesses operate during extreme seasonal peaks with chronic labor unreliability. The AI layer isn't a luxury — it's the only way to capture value during the periods that matter most.

"You might come in as a manager, but there are at least three people that quit that way or that day or five didn't show up. So all these customers are piling in starting at 6 a.m. because now it's a heat wave in the country." 00:05:25 — Melisa Tokmak

The Multi-Layer Moat: Models + Harnesses + Orchestration + Domain Data

Netic argues against the "labs will commoditize this" narrative by pointing to the irreducible complexity of last-mile execution: different accents, engagement contexts, customer lifetime value scoring, and routing logic. No model alone can solve this.

"There's quite a bit of last mile that you really have to do. That doesn't only come from models. That has to come from your harnesses and orchestration, the software and the product that you have to build on top. So if anything, actually companies like us and Netic have to be good at all three layers." 00:15:02 — Melisa Tokmak

Private Equity as a Distribution Channel — With Important Nuances

Private equity owns many of these essential services businesses and is shifting its playbook from financial engineering to value creation. Netic is positioning itself as the AI operating layer across entire PE portfolios, enabling cross-portfolio intelligence (e.g., knowing a customer has a dog and offering pet care alongside wellness membership).

"Now imagine being a private equity and you own maybe 20, 30 of these businesses in different industries. So now if those are on Netic, what you can do is — if you're going to go after someone for a wellness offering and I have a dog that I love, I would actually very much like to pick the one that has pet care." 00:26:36 — Melisa Tokmak

Why Software Roll-Ups Beat Asset Roll-Ups at Scale

Melisa made a clear-eyed decision not to do an AI-enabled roll-up because: (1) the product only serves companies you've already bought, limiting scale; (2) M&A is not her core skill; (3) she wants a compounding product, not a portfolio of bespoke solutions.

"All the products you're building are for the company you just bought. It can't really be actually applied to any other company. So you are committing to buying these companies in whatever industries that you are interested in and serving those with your products. Versus what I'm interested in is how every real-world business can run on Netic." 00:10:01 — Melisa Tokmak

Robotics for Physical Services Is Decades Away — Not Years

Melisa pushes back hard on the robotics-will-replace-field-technicians narrative, citing physical variability in buildings, dexterity limitations, and the irreplaceable human element in high-stress service moments.

"If you look at robotics capabilities today, it is quite far in terms of dexterity on how to be able to handle different types of screws even. What type of homes are this? How am I going up? How am I going into the tiny areas to be able to fix something? Many times it's not clear. You have to open up the whole walls to even see what do you need to fix." 00:12:03 — Melisa Tokmak

Founder Commitment Culture Is Eroding — and It's a Competitive Advantage for Those Who Don't

Melisa observes a generational shift toward short-term exit orientation and "permanent underclass" anxiety among Gen Z talent, which she treats as a hiring filter rather than a norm to accommodate.

"I see, especially Gen Z in these days when I'm looking at hiring, obsessed with this permanent underclass mentality. If I don't make my money in the next 18 months, or if I don't learn everything in the world in the next six months, then I am forever poor." 00:17:06 — Melisa Tokmak

2. Contrarian Perspectives

Essential Services Companies Are Among the Most Tech-Forward Buyers

The conventional wisdom is that HVAC, roofing, and pet care companies are technologically backward. Melisa argues the opposite — they are extremely value-focused and move fast when ROI is clear.

"I think it's a big misconception to think about these industries as old school. Actually, some of the most tech forward business focused people, owners, founders I have met have been in these industries... just one of the businesses we close, it can be like a half a million contract. And it took from end to end 14 days." 00:24:14 — Melisa Tokmak

The Labs Are Not a Competitive Threat to Vertical AI — They're Too Unfocused in Enterprise

Rather than fearing OpenAI or Anthropic eating her lunch, Melisa argues their enterprise product sprawl and rapid product killing actually makes them untrustworthy partners for mission-critical enterprise deployments.

"OpenAI builds amazing products really fast, but also it kills them really fast. So I don't think enterprises are at least enterprises in these industries looking for that really fast. Or in Anthropic's case, you know, Silicon Valley converged in the idea that they pulled ahead in coding agents or Claude because they had focus. But you see exactly the opposite in the enterprise case. There's about like 20 products." 00:14:06 — Melisa Tokmak

Most Founders Today Are Optimizing for Exit, Not for Building — and That's Correctable

Melisa frames the current fear of labs as a symptom of a deeper problem: founders entering the space primarily for a quick exit rather than long-term company building, which she views as a recent and reversible cultural degradation.

"I do think that's because there's a lot of building going on currently that's focused on how can I exit immediately... I think being a founder, I actually think was a more honorable thing before. Like you knew you're dedicating your life to it." 00:16:06 — Melisa Tokmak

AGI-as-a-Solution Is Intellectually Lazy for Real-World Complexity

The assumption that waiting for AGI will eventually solve essential services workflows is not just technically premature — it's a form of intellectual avoidance of genuinely hard, domain-specific problems.

"Maybe looking at the problem we're solving, the answer would be, well, when we get the AGI, we'll ask how to solve it for essential services. And I think that is both operationally and intellectually a bit lazy thinking." 00:14:35 — Melisa Tokmak

Wider AI Accessibility Will Expose Human Agency Gaps, Not Close Them

Melisa offers a quietly dystopian counterpoint to the education-democratizes-opportunity narrative: removing resource barriers doesn't change underlying human will, and most people won't choose to use expanded access.

"When you see people not doing stuff, then it's a little bit of a dystopian version of it... I guarantee you majority of the world will not be making that choice." 00:32:29 — Melisa Tokmak

3. Companies Identified

Netic AI platform for essential services enterprises; handles inbound/outbound customer interactions via voice, text, and web; routes and optimizes labor dispatch; integrates satellite data and cross-portfolio intelligence. Over $600M in revenue generated for customers.

"We have made so far, I think, over six hundred million dollars for our customers that have been really generated from AI handled interactions." 00:29:26 — Melisa Tokmak

Scale AI AI data infrastructure company where Melisa was Director of Engineering; built government, logistics, manufacturing, financial services, and healthcare business units. Eventually entered a licensing/agreement with Meta valued around $30 billion.

"I built at scale the government business unit and large enterprises in logistics, manufacturing, financial services, healthcare, across the board." 00:07:41 — Melisa Tokmak

Long Lake AI-enabled roll-up company mentioned by Elad Gil as an example of buying essential services businesses and optimizing them with AI — a direct strategic alternative to Netic's approach.

"I've also invested in AI roll-ups where people will, you know, a company like Long Lake will go and buy a bunch of businesses and then optimize them with AI." 00:06:26 — Elad Gil

Harvey Vertical AI company (legal) mentioned by Elad as an example of a successful early bet on vertical AI applications.

"I used to be funding things like Harvey and Perplexity and, you know, a little bit later Decagon and Abridge." 00:15:34 — Elad Gil

Perplexity AI search company mentioned as an early vertical AI investment by Elad Gil. 00:16:04

Decagon AI customer support company mentioned as an early vertical AI investment by Elad Gil. 00:16:04

Abridge AI for medical documentation, mentioned as an early vertical AI investment by Elad Gil. 00:16:04

Palantir Mentioned as a benchmark company that deeply cares about craftsmanship and long-term delivery for their audience.

"Imagine Palantir or SpaceX or Notion, who really care about craftsmanship. And really delivering for their audience for many, many years." 00:23:28 — Melisa Tokmak

SpaceX Cited alongside Palantir and Notion as a model of long-term craftsmanship-driven company building. 00:23:28

Notion Cited alongside Palantir and SpaceX as a model of sustained craftsmanship and audience focus. 00:23:28

OpenAI Referenced as a lab that builds fast but kills products fast, making it a poor fit for enterprise trust in mission-critical workflows. 00:14:06

Anthropic Referenced for having focus in coding/Claude but lacking enterprise product focus with approximately 20 products in market. 00:14:06

Meta Mentioned as the acquirer/licensor of Scale AI in a deal valued around $30 billion; also part of Melisa's background context on labs. 00:07:26

BrainCo Mentioned by Elad Gil as an example of a company being deployed by private equity to help portfolio companies adopt AI. 00:27:49

4. People Identified

Melisa Tokmak Founder and CEO of Netic. Former Director of Engineering at Scale AI where she built the government, logistics, manufacturing, financial services, and healthcare business units. Grew up in a small town in Turkey, received a full scholarship to Stanford without ever owning a computer prior. Built Netic to serve essential services businesses with AI-driven autonomous operations.

"My background, I grew up in a very small town in Turkey. I grew up with nothing and really came here only for college. When I got a full scholarship to Stanford, I didn't even own a computer before." 00:08:11 — Melisa Tokmak

Alex Wang Founder of Scale AI. Described by Melisa as an amazing person to work with directly.

"Obviously, Alex was an amazing person to work with directly as a founder." 00:09:08 — Melisa Tokmak

Elad Gil Host and investor. Disclosed as an investor in Netic; also invested in Long Lake, Harvey, Perplexity, Decagon, and Abridge. Known for early bets on vertical AI applications. 00:06:26

5. Operating Insights

The "Live Deployment" Demo Beats the Scripted Demo Every Time

Netic has moved entirely away from standard sales demos to showing live, working deployments with real customer data and real interactions. This collapses sales cycles — one $500K contract closed in 14 days — and immediately separates genuine ROI from vaporware.

"We'll pull up and show a live deployment. Because if it's working and I have it, why can't you just show you what this customer is making... how is the real world customers interacting with this technology?" 00:29:26 — Melisa Tokmak

Engineers Must Do Field Visits — Make It a Hiring and Culture Requirement

Every engineer at Netic is required to visit customers on-site before building product. This is institutionalized, not optional, and is how Netic maintains the product-market fit accuracy that remote-only teams lose.

"Every single engineer in our company do have to go visit customers on site to build the right product back home in San Francisco." 00:11:32 — Melisa Tokmak

Screen for Longitudinal Agency, Not Point-in-Time Accomplishments

The hiring question "what's the hardest thing you've ever done?" is only valuable if you dig past the answer into whether that trait has shown up consistently across a person's entire life — not as a single showcase moment.

"If you have agency, you care about agency, you have shown agency continuously in your life... have you been showing agency in things that you did in life? And did you keep up with them?" 00:19:18 — Melisa Tokmak

Reframe Every PE Conversation From Cost-Cutting to Net New Revenue Immediately

Private equity buyers default to cost-cutting framing because that's all they've seen from most vendors. Netic proactively resets this frame to revenue generation in every sales conversation, with live proof, before the buyer anchors to headcount reduction.

"I always see in the company, too, something is not hard. Is that like, what can we do better? So in a lot of these conversations, the conversation is all about the value they're going to get and what they can see tangibly now." 00:28:56 — Melisa Tokmak

6. Overlooked Insights

Cross-Portfolio PE Intelligence Is a Quietly Enormous Untapped Market

Melisa briefly describes a use case that almost no one in the AI infrastructure space is explicitly targeting: when a PE firm runs 20-30 businesses across different verticals on one platform, Netic can synthesize customer context across all of them and enable cross-sell and upsell at the portfolio level. This is not just an efficiency play — it is a fundamentally new revenue motion for PE-owned businesses that didn't exist before, and it creates a switching cost that compounds as more portfolio companies join the platform.

"Now imagine being a private equity and you own maybe 20, 30 of these businesses in different industries. So now if those are on Netic, what you can do is — if you're going to be a member in a wellness offering, I would actually very much like to pick the one that has pet care. So if you know that about me and as the owner of the company, how would you think about talking to me about that?" 00:26:36 — Melisa Tokmak

Satellite Data Integration Into Service Agent Context Is a Novel and Defensible Data Moat

Mentioned only in passing during a discussion about roofing, Netic is feeding satellite data about hurricane impact and material degradation by neighborhood directly into agent context — autonomously. This means Netic is not just handling reactive inbound demand but proactively generating outbound demand signals using proprietary environmental data pipelines. This is a non-obvious data integration that most AI-for-SMB companies are nowhere near building, and it creates durable differentiation that pure model providers cannot replicate.

"We connect to satellite data to be able to think about in different neighborhoods, how the hurricanes affect different roofs. How should you think about different materials? Should that be autonomously fed into the context of our agents so that not only you're better equipped to handle that conversation with the customer, but also you can spot who to go after." 00:25:28 — Melisa Tokmak