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How to Lead AI Engineering Teams

Have you ever wondered why some teams seem to effortlessly deliver value while others stay busy but make no real progress?

I recently had a conversation that completely changed how I think about leading teams. While discussing team performance with a VP of Engineering who was frustrated with their team's slow progress, I suggested focusing on better standups and more experiments.

That's when Skylar Payne dropped a truth bomb that made me completely rethink everything:

"Leaders are living and breathing the business strategy through their meetings and context, but the people on the ground don't have any fucking clue what that is. They're kind of trying to read the tea leaves to understand what it is."

That moment was a wake-up call.

I had been so focused on the mechanics of execution that I'd missed something fundamental: The best processes in the world won't help if your team doesn't understand how their work drives real value.

In less than an hour, I learned more about effective leadership than I had in the past year. Let me share what I discovered.

The Process Trap

For years, I believed the answer to team performance was better processes. More standups, better ticket tracking, clearer KPIs.

I was dead wrong.

Here's the truth that surprised me: The most effective teams have very little process. What they do have is: - Crystal clear alignment on what matters - A shared understanding of how the business works - The ability to make independent decisions - A systematic way to learn and improve

Let me break down how to build this kind of team.

The "North Star" Framework

Instead of more process, teams need a clear way to connect their daily work to real business value. This is where the North Star Framework comes in.

Here's how it works:

  1. Define One Key Metric: Choose a single metric that summarizes the value you deliver to customers. For example, Amplitude uses "insights shared and read by at least three people."

  2. Break It Down: Identify the key drivers that teams can actually impact. These become your focus areas.

  3. Create a Rhythm:

  4. Weekly: Review input metrics
  5. Quarterly: Check relationships between inputs and your North Star

  6. Yearly: Validate that your North Star predicts revenue

  7. Make It Visible: Run weekly business reviews where leadership shares these metrics with everyone. Start manual before building dashboards - trustworthy data matters more than automation.

This framework does something powerful: it helps every team member understand how their work drives real value.

The Weekly Business Review

One of the most powerful tools in this framework is the weekly business review. But this isn't your typical metrics meeting.

Here's how to make it work: - Make it a leadership-level meeting that ICs can attend - Focus on building business intuition, not just sharing numbers - Take notes on anomalies and patterns - Share readouts with the entire team - Use it to develop a shared mental model of how the business works

Rethinking Team Structure

Here's another counterintuitive insight: how you organize your teams might be creating unnecessary friction.

Instead of dividing responsibilities by project, try dividing them by metrics. Here's why: - Project-based teams require precise communication boundaries - Metric-based teams can work more fluidly - It reduces communication overhead - Teams naturally align around outcomes instead of outputs

Think about it: When teams own metrics instead of projects, they have the freedom to find the best way to move those metrics.

Early Stage? Even More Important

I know what you're thinking: "This sounds great for big companies, but we're too early for this."

That's what I thought too. But here's what I learned: Being early stage isn't an excuse for throwing spaghetti at the wall.

You can still be systematic, just differently:

  1. Start Qualitative:
  2. Draft clear goals and hypotheses
  3. Generate specific questions to validate them
  4. Talk to customers systematically

  5. Document and learn methodically

  6. Focus on Learning:

  7. Treat tickets as experiments, not features
  8. Make outcomes about learning, not just shipping
  9. Accept that progress is nonlinear

  10. Build systematic ways to capture insights

  11. Build Foundations:

  12. Document your strategy clearly
  13. Make metrics and goals transparent
  14. Share regular updates on progress
  15. Create systems for capturing and sharing learnings

The Experiment Mindset

One crucial shift is thinking about work differently: - The ticket is not the feature - The ticket is the experiment - The outcome is learning

This mindset change helps teams focus on value and learning rather than just shipping features.

Put It Into Practice

Here are five things you can do today to start implementing these ideas:

  1. Define Your North Star: What's the one metric that best captures the value you deliver to customers?

  2. Start Weekly Business Reviews: Schedule a weekly meeting to review key metrics with your entire team. Start simple - even a manual spreadsheet is fine.

  3. Audit Your Process: Look at every process you have. Ask: "Is this helping people make better decisions?" If not, consider dropping it.

  4. Document Your Strategy: Write down how you think the business works. Share it widely and iterate based on feedback.

  5. Shift to Experiments: Start treating work as experiments to test hypotheses rather than features to ship.

The Real Test

The real test of whether this is working isn't in your processes or even your metrics. It's in whether every team member can confidently answer these questions:

  • "What should I be spending my time on today?"
  • "How does my work drive value for our business?"
  • "What am I learning that could change our direction?"

When your team can answer these without hesitation, you've built something special.

Remember: Your team members are smart, capable people. They don't need more process - they need context and clarity to make good decisions.

Give them that, and you'll be amazed at what they can achieve.

P.S. What would you say is your team's biggest obstacle to working this way? Leave a comment below.

SWE vs AI Engineering Standups

When I talk to engineering leaders struggling with their AI teams, I often hear the same frustration: "Why is everything taking so long? Why can't we just ship features like our other teams?"

This frustration stems from a fundamental misunderstanding: AI development isn't just engineering - it's applied research. And this changes everything about how we need to think about progress, goals, and team management. In a previous article I wrote about communication for AI teams. Today I want to talk about standups specifically.

The ticket is not the feature, the ticket is the experiment, the outcome is learning.

The right way to do AI engineering updates

Helping software engineers enhance their AI engineering processes through rigorous and insightful updates.

In the dynamic realm of AI engineering, effective communication is crucial for project success. Consider two scenarios:

Scenario A: "We made some improvements to the model. It seems better now."

Scenario B: "Our hypothesis was that fine-tuning on domain-specific data would improve accuracy. We implemented this change and observed a 15% increase in F1 score, from 0.72 to 0.83, on our test set. However, inference time increased by 20ms on average."

Scenario B clearly provides more value and allows for informed decision-making. After collaborating with numerous startups on their AI initiatives, I've witnessed the transformative power of precise, data-driven communication. It's not just about relaying information; it's about enabling action, fostering alignment, and driving progress.

Dear Future AI Consultant

Picture this: Four AI engineers walk out of some big tech office, maybe its layoffs, or burn out, or the golden handcuffs, but they're finally free. Fast forward one year, and their paths couldn't be more different.

  • Engineer 1 went the traditional freelance route. He's making $300 an hour, meticulously tracking every minute, and constantly hearing he's "too expensive." Yet, he's always scrambling for the next gig.

  • Engineer 2 dove into the startup world. She's now a VC-backed founder, drowning in pitch decks and investor meetings. Sure, the TechCrunch headlines are nice, but she hasn't had a good night's sleep in months, constantly worrying about runway and growth metrics.

  • Engineer 3 became an indie hacker. He's over the moon about hitting $2,000 in Monthly Recurring Revenue with his AI product. It's a great start, but he's realizing that building the product was just the beginning. Now he's grappling with the challenges of distribution and marketing, discovering that these skills are just as crucial as his technical expertise.

  • And then there's me, Engineer 4. I'm writing this from a business class seat on my way to San Francisco, closing a six-figure deal with a client. But that's just the tip of the iceberg.

As an AI consultant, I've built a personal brand, grown an audience, and mastered marketing and distribution - all while confidently selling my expertise. I've made friends with VCs, hosted dinners for industry leaders, and built a network that spans the tech world.

The best part? I've got $500K sitting in my business account, deployed 100k in angel investments and have paid my friends 100s of 1000s of dollars to work together and on projects we love. If I ever decide to start a company, raise money, or build a product, I've already developed all the skills I need - plus the cash to get started.

What Made The Difference?

It wasn't talent or hard work. We're all putting in the hours. The difference was in how we chose to leverage our AI skills in this booming market.

That's why I created the "Indie AI Consultant" newsletter. In just 10 days I share one email with you, the reader. It's packed with insights and strategies that I've learned from my own experience.

About those four AI engineers I mentioned earlier: we all had the same starting point, but our paths diverged dramatically. The difference? Knowledge about how to turn AI skills into a profitable consulting business that offers both freedom and financial success.

I can't promise you'll be flying business class next month if you join. But I can guarantee you'll find it packed with actionable insights to launch your consulting career - and potentially never worry about hourly rates, investor pressure, or how to price your services ever again.

Just enter your email below to get started, you'll get an email from me every 10 days or so, and I'll see you on the inside.

Why Freelancers Will Win the AI Gold Rush

I remember stories about the tech booms of past decades. In the late 1990s, the web revolution began. Teenagers who knew HTML were earning thousands per project, building websites for local businesses. In the late 2000s, a similar trend emerged with mobile apps. Young developers were profiting from creating applications for smartphones.

"This is the future," people would say. Many were skeptical about how these basic websites or simple apps could change anything. We know how those predictions turned out.

Now, in 2024, I'm feeling the same way about AI freelancers and consultants. But this time, I'm not skeptical. I believe we're at the start of something significant, a change that will surpass the web and mobile revolutions in its impact.

Here's why:

Chasing Chase: Why I'll Never Trust Chase Bank Again, A Yuppie Nightmare

It always goes this way. Someone will try teaching you a parable or life story, but you never really understand it until you have to experience it yourself. Some call this ‘learning things the hard way.’ Some call this life. Now, folks always told me that consulting was either a feast or famine, which sounds straightforward, but it turns out I didn’t really know what this meant until I had to deal with Chase.

Earlier this year, for reasons that are still not fully known to me, and despite my existing relationship with Chase (dating back to my first job out of university over a decade ago!), Chase froze $180,000 of my money without warning. This left me scrambling to pay employees and nearly derailed my business—all without explanation. It was a major wake-up call not just for me but for any entrepreneur. The importance of diversifying your banking and choosing financial partners that actually support small businesses has never been more important.

A surprising reason to not list your consulting prices

As I've shared insights on indie consulting, marketing strategies, and referral techniques, a recurring question from my newsletter subscribers is about pricing. Specifically, many ask if they should lower their rates or make them public.

In this article, we'll delve into the counterintuitive reasons why listing your consulting prices might not be the best strategy, regardless of whether you're aiming to appear affordable or exclusive. We'll explore the potential drawbacks of transparent pricing, introduce more effective alternatives like minimum level of engagement pricing, and provide actionable strategies to help you maximize your value and earnings as a consultant.

Building on the foundation laid in my previous posts about building a consulting practice and using the right tools, this piece will add another crucial element to your consulting toolkit: strategic pricing.