Second Brain

Masterclass: How FDEs make $1M/yr deploying AI agents

Channel: Greg Isenberg
Published: 2026-10-01
URL: https://www.youtube.com/watch?v=1a5HxU52vCQ

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Transcript

Why are people getting paid a million dollars as forward-deployed engineers? It sounds crazy, right? But if you think about it, it isn't. Because if you're deploying agents and you're able to drive $5, $10, $25 million of value for companies, would they be willing to give you a slice of that pie? It turns out yes. So the question becomes, how could you become a forward-deployed engineer? How could you deploy agents to make companies run more efficiently, drive revenue, lift margins?

Well, today I brought on Voss from Veric Agents for an inside look at how this works. He's sharing examples from client engagements with the details changed that you just don't get to see publicly. This is for the first time ever on the internet. I think a lot of people have talked about forward-deployed engineering on the internet, but they haven't gone into concrete examples for how you can actually do this. By the end of this episode, you're going to understand how to find the work worth automating. You're going to be able to decide where agents belong. You're going to understand open-source versus closed-source. You're going to understand where Muse, Grockbot, and others fit in all of this and how you can start putting this into practice. This is a masterclass for how to become a forward-deployed engineer. We kept it high level before, but by popular demand, we're going deeper.

How I Use Voice AI Everyday

One of the most common questions I get nowadays is, Greg, how are you using voice AI in your everyday life? Well, I'm going to break it down in 60 seconds. I'm going to give you my voice AI toolkit right now, and this section is sponsored by Google.

Google's actually been crushing it with voice AI. I've been using Gemini 3.8 Live recently. What's really cool about 3.8 Live is you can just go and have a phone call basically with it and say something like, "Hey, you know, how am I doing with my launch campaign?" And it's connected to all my tools and it's basically running my business in the background. I think about it as if it's almost like my chief of staff. It's smart. It's intelligent. It's connected to my tools. And it allows me to live my life while having voice AI help me run my business.

The second is Gemini 3.5 Transcribe. What's really cool about this is I can go and leave voice notes and it'll parse those voice notes. It removes the ums, the likes, and you know how I use it is, I've got a lot of ideas. I got a lot of startup ideas and it just allows me to go on walks and just basically give those ideas and get back text that is clear. And that also allows me to remember because frankly I forget things.

The third is Gemini 3.8 Flash Text to Speech. What's really cool about this is you can go and say, "Hey, build me a voice that has a Brooklyn accent." And it'll go and do it. There's so many products that need a voice. For me recently, I've been building a mobile app and I just included an onboarding voice into the mobile app.

And the last thing is Speech to Speech. Not everyone speaks English. So if you're doing business in Mandarin in China, you get this realtime translation via Gemini 3.5 Live Translate and it just opens up new markets. These are just four tools that I've been obsessed with lately. Shout out again to Google for sponsoring this part of the episode, and I'll include links for where you can play with these tools and models in the description.

The End-to-End Workflow of AI Transformation

Voss, by the end of this episode, what are people going to learn?

People are going to hopefully learn the end-to-end workflows that we're seeing as part of AI transformation and better understand how they can go ahead and do this themselves.

Yeah, because there's lots of talk and I'm sort of guilty for this too of just keeping it high level. Sometimes I'm just like, here's an interesting topic. Here's an interesting business model. Just apply AI. But the big question is, well, how do you actually apply AI? What does this forward-deployed engineer piece look like? So we're going to actually get into the nitty-gritty of that. So that's the commitment you're going to make to the person listening to this. By the end of this episode, they'll understand what it actually means to apply AI. What does it actually mean to forward deploy into something and where are the business opportunities and monetization opportunities and how big is this thing? Is that the commitment you are going to make today?

100%. You have my word. Let's do it.

AI Isn't Applied—It's Reengineered

I put out an article a few weeks ago and it was titled "Don't Apply AI." Obviously a play on, you know, applied AI, etc. The point of that is to go into how AI isn't something that can be applied like a coat of paint. It's something that really involves process re-engineering. And that's the goal of today's kind of presentation.

As I'm sure everyone already knows, this is already happening, right? Rollups are a huge part of the private equity playbook these days where you'll buy a firm that runs very much on people, outdated processes, maybe outdated software as well. For example, accounting firms, IT shops, law practices, etc. And these AI holding companies or AI transformation companies are buying up these portfolio companies and putting AI engineers, forward-deployed engineers, inside of it.

What they do is they'll find the processes. They'll map out the systems of record, the exceptions, what happens where, what cycle times occur as a result of handoffs between two different pods of people. And they'll rebuild that process with agents from the ground up. Thrive, for example, comes to mind. They have 35 engineers across 70 firms, and what they're seeing is that it's actually quite successful. The numbers move. Tax returns 30% faster at 98% accuracy. Agents are actually able to take work off of people's plates and it is feasible and possible. The only caveat being is that it's a lot more involved than people had maybe initially surmised.

One example: gross margin at one call center firm was 60% and above, which is actually quite high. When they buy the firm, let's say they buy it for a billion dollars and they increase the margin and they double it for example in the best case, that actually translates into the valuation of the entire company. So all of a sudden you can buy it for a billion, implement AI across 3, 6, 12, 24 months, whatever that is, and then you can sell it for $2 billion or $4 billion or $8 billion. And that's really the goal of these companies.

You can see a few different examples of that: Thrive Holdings with Josh Kushner, General Catalyst, AI-enabled roll-ups, and the people who are doing those steps two and three are those forward-deployed engineers.

I think the big question a lot of people have is like, okay, cool. But you're basically saying buy a business, add AI and you know, question mark, question mark, profit. You know what I mean? So how do you actually do this?

We will talk about that. I'm not going to keep it super high level. We're going to get into it. The analogy that I like to give everyone as you're thinking about what it means to be a forward-deployed engineer is your life is already complicated, right? You have five different inboxes, four file stores—Google Drive, Notion, iCloud Drive, the desktop. You have five messaging apps: iMessage, WhatsApp, Slack, Google Calendar, Outlook Calendar, all this stuff. And it's actually very hard to understand exactly how you like to use your systems, right? You can make a simple tool call to an API of whatever system of record of your choosing. But if a client emails you in Gmail for work, and if a family member messages you on iMessage or WhatsApp, understanding this is quite complicated even at a personal level.

Imagine at a company. This company has acquired eight other companies in the past. So now they're existing in five different regions: São Paulo, Bangalore, Australia, Sydney. They have 23 different systems of record and each region is doing things differently. Chicago is on SAP, Salesforce and Workday. Toronto is on NetSuite, HubSpot and ADP and so on and so forth. Really, it is quite complicated. This is exactly why AI cannot be applied. If you're applying AI over this entire company, which literally spans the entire globe, you're going to end up with just making faster—I hope I can curse on this podcast—mess.

And that's the fundamental issue. I don't want to be too much beating a dead horse on the problem. The problem is very clear. It's very complicated. So, how do you actually go about doing this? This is our view, and I'm sure there's many different ways of doing this, but our view is process mapping, then re-engineering, then building, deploying, and rolling it out.

The Three-Step Process Mapping Approach

If you were to go into a company on day zero, here's what I would suggest that you do.

The first is interviews on the human side. You'll start off with a single department, right? In finance, you'll talk to the head of AP, AR, reconciliations, billing, banking, FP&A, etc., and you'll work your way down from there. The reason why these interviews are super helpful is because a lot of information lives in their heads. Very rarely do you have a very clean document source that you can just point your agent at, it'll learn it, and go from there. Very often, it's not written down. It's not documented. We hear this all the time when we talk to companies. They tell us, "Oh yeah, this person's been at the firm for 20 years and they just handle it." It's so common. Doesn't matter the size of the company. Could be a massive Fortune 50. Could be a small SMB. They all have these critical key people where information lives in their head.

If you think that you're going to walk into a company and they're going to have like an Obsidian second brain hooked up to AI agents, you are just mistaken, my friend. There is no second brain happening here. There's nothing you can just plug into, call an API, and all of a sudden you have your knowledge store. It's kind of on you to create that and it's per department and it's cross-department and it's very, very involved.

So step one is basically, in a sense, creating like a human API. It's getting all the data, systems, SOPs, you know, all that stuff into modern digital systems.

Absolutely. You always have a good way of articulating. I like that—it's a human API. That's what it is. You have to understand why do they do things the way they do them today? Who really decides them? Which step is theater? Which step is legitimate? What happens when exceptions take place? How often do exceptions take place? All of these things are not written down.

From there, we move on to mining the systems of record and mining everything else. Really, there's a lot of data that exists in their Salesforce, their NetSuite, their Dynamics, their Workday, etc. And that doesn't mean it's written down in a document which has like, step one, step two, etc. But you can sort of create that SOP by living on top of their systems of record.

For example, if I have real-time access to a Salesforce, over the course of 3 to 4 weeks I have a pretty good understanding of what sort of data enters Salesforce, how often is it getting corrected, etc. This runs constantly and you can use AI to analyze what the hidden meaning is behind each one of those actions. If a certain record comes in from a certain company of a certain size, we see these actions taking place and now we can create sort of a graph: okay, once something happens, once something enters of a certain category, A goes to X, B goes to Y, C goes to Z, etc.

But obviously if you just do that without the interviews, you're missing half the picture.

The final aspect is what lives outside of them. So then you actually go into the existing documentation. Sometimes outdated, sometimes it's pretty good. This is in SharePoint. This is in Drive. This is in Notion. This is in Slack. This is in Teams. This is in Gmail. This is in spreadsheets.

Through these three steps, you have literally the entire company's picture. And it varies—the split among companies, right? For a very large company, they've had 10 years of Salesforce historical data for you to go off of. For an SMB, they probably don't. They probably have mostly in people's heads in interviews, and no processes, no system of record, no software. So it's up to you to determine what angle you need to take per company and it varies.

A Concrete Example: The $5B Software Company

So here's a very concrete example. You wanted to go away from high level. This is what we're trying to do. This is a public software company. This is actually an engagement that we completed. It was a $5 billion in revenue company. They have over 150 products, tons of solution consultants, and we were brought in by the CRO. They're public, so this actually makes them quite complicated. You have to deal with regulation, certain laws apply, etc.

The process document which they actually had, and they had completed this—I think it was an engagement with Deloitte that mapped this out for them—was: you build a quote, then you submit it, then it goes to deal desk, then you approve it, then you send it, you negotiate, then you sign it. Very cut and dry. And this is always what happens if you just look at one angle, even just one person, right? They might tell you the...

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