ChatGPT Image Jul 9, 2025, 02_42_05 PM-1

LodeStar

Your Signal in the Noise

Welcome back friends. This week I'd like to start by posing a hot-topic GTM question... to buy or to build?

In the AI-native and early-stage trenches, something interesting is happening. While the rest of the world debates which vendor is the right fit for usecase or the best integration roadmap, the real operators are quietly choosing Door #3: build it themselves.

"Build" used to be unrealistic. It meant bloated timelines, fragile hacks, and burned-out engineers. But now? It’s quietly stealing the show. Especially for AI-native startups and early-stage companies, building isn't just a viable path, it's often the smarter one.

Why? Because stitching together five different point solutions is like assembling IKEA furniture blindfolded. You waste time hunting for hex wrenches, curse at missing screws, and pray it doesn’t collapse when someone leans on it. In the same way, companies end up duct-taping half-baked APIs, juggling billing models, and burning sprint cycles integrating tools that were never designed to play nice together.

Meanwhile, the builders? They skip the vendor speed dating (less wasted time). They focus on the outcomes because tools now exist to drive the orchestration. They build purpose-fit solutions that map directly to the problem in front of them. No more hoping that Vendor X ships Feature Y next quarter.

The payoff? Faster results, tighter feedback loops, and lower long-term costs all without the bloat. Those last two trip people up, because sure, building has an upfront price, but maintaining a Rube Goldberg machine of SaaS tools is its own kind of debt, technical, operational, and financial.

And in AI, where the landscape changes weekly, and context is everything, owning your core systems gives you leverage. You can adapt fast. Tune the engine. Push the boundaries. Try doing that when you're locked into a third-party black box.

So if you’re early-stage and still clinging to the old "buy, don't build" gospel, it's time to rethink. The new generation isn’t asking for permission. They’re grabbing the tools, rolling up their sleeves, and building their own way out of the maze.

More on how to know when not to build, coming next week.

You Can't Skip The Base Level

AI has become the new shortcut.

Every GTM problem suddenly has the same proposed fix: automate it, augment it, and then accelerate it… RAPIDLY. What rarely gets questioned is whether there’s anything solid underneath all that speed.

In this Lodestar conversation, Phil Burrows and I talk about the base layer most teams are actively avoiding. The foundations of structure, judgment, and consistency. Because acceleration without a foundation creates nothing but volatility.

What's moving the needle:

Designing the work instead of reacting to it
Trusting judgment over dashboards when it matters
Using tools to reduce friction, not replace thinking
Treating recovery and downtime as performance infrastructure, not indulgence

If your GTM motion feels frantic, noisy, or overly tool-dependent, the issue probably isn’t ambition. It’s that the base layer hasn’t been built to support speed yet.

🎧 Give it a listen.

Listen on Spotify

Listen on Youtube

A Signal Worth Your Time

Jordan Crawford joined Kyle Norton on the Revenue Leadership Podcast recently, and the conversation brought a clear message for GTM leaders: The “ChatGPT era” is already behind us.

Jordan breaks down the shift from:

Text generation (ChatGPT)
To linear automation (Clay-style workflows)
To autonomous GTM engineering with Claude Code

This is the point where AI stops being a helper and starts behaving like infrastructure.

Jordan walks through how local environments let agents retain memory, write code, execute multi-step objectives, and improve over time. Not demos or toys, but real systems that can be deployed against the most manual, error-prone parts of GTM.

This phase will finally remove the drag that keeps good teams stuck doing low-leverage work.

Great piece, well worth some time this weekend.

Experience the Revenue Leadership Podcast

The Ultimate Role of RevOps

The role of RevOps has changed, and a lot of teams haven’t caught up yet.

In this webinar session, RevOps leaders from Qobra, Polaris Ops, and n8n unpack how high-performing teams are using automation, AI, and technical depth to turn RevOps into a true revenue driver.

You’ll learn: