Let's Talk AI

Every developer struggles with context-switching, managing multiple sessions, and maintaining focus. Enter Tmux.

Tmux is a tool Thomas Bustos calls the backbone of his productivity. In this episode, he walks through his full tech stack, explains how deep work sessions are structured, and shows how Tmux integrates with NeoVim and Cloud Code for maximum efficiency.

Learn how to track metrics, iterate on user experience, and keep your workflow aligned with your goals. 

If you want to move from “busy” to “productive,” this episode is a must-listen.

Top Takeaways:
Cloud tools can enhance productivity but aren't the sole solution.

Tmux is a core tool for managing sessions effectively.

Daily skills help manage entropy and keep goals aligned.

Using a tech stack that includes NeoVim and Cloud Code is beneficial.

Iterating on user experience is crucial for app development.

Context management is key in software engineering.

Open source tools can be integrated for better workflow.

Security and setup considerations are important for cloud tools.

Deep work sessions can lead to more productive outcomes.

Feedback and iteration are essential in the design process.

Chapters:
00:00 Welcome!

02:47 Exploring the Tech Stack

06:07 Deep Work and Project Management

08:55 Iterating on User Experience

12:12 Final Thoughts on Engineering Tools

Connect with Thomas Bustos
Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

Let’s Talk AI - https://thomasbustos.substack.com/ 

Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

The role of AI leaders has quietly transformed.

Not long ago, being an “AI leader” meant hiring a few ML engineers, experimenting with models, and hoping something stuck. Today, that approach fails. AI leaders are no longer just technical champions. They are system architects of decision-making, accountability, and value creation.

In this episode of Let’s Talk AI, Thomas Bustos breaks down the three pillars every AI leader must master to build real, measurable impact inside an organization. He explains why teaching is no longer optional, why strategy without execution collapses, and why implementation is where most AI initiatives die.

If you’re serious about becoming an AI leader, or building AI leadership inside your company, this episode gives you the blueprint.
Listen now.

Top Takeaways:
The core goal is to balance leading and delivering technology.

AI leaders must teach, strategize, and implement effectively.

Successful AI adoption can compound gains for organizations.

Metrics like error rates and active users are crucial for success.

Every build should enhance observability for future improvements.

AI leaders need to understand the latest tools and their applications.

Reverting to previous versions is essential for error handling.

Quantifying AI's impact in terms of revenue is recommended.

A learning organization adapts and grows through shared knowledge.

Effective implementation requires speed, reliability, and quality.

Chapters:
00:00 Welcome!

02:50 The AI Leaders Playbook: Key Pillars

06:03 Understanding AI Adoption and Its Impact

09:10 Strategies for Effective Implementation

11:58 Metrics for AI Success

14:51 Navigating AI Tools and Technologies

18:07 Building a Learning Organization

20:59 Final Thoughts

Connect with Thomas Bustos
Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

Let’s Talk AI - https://thomasbustos.substack.com/ 

Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

What is an AI Native employee?

It’s not someone who occasionally uses ChatGPT.
It’s not someone who automates a few workflows.
It’s someone who integrates AI into how they think, decide, and operate.

In this episode, Thomas Bustos explores the rise of the AI Native employee—not as a job title, but as a new operating standard. Before AI, employees manually summarized, synthesized, reported, and shared knowledge. Decision-making required slow coordination. Context was fragmented.

Now, the AI Native shift is changing how organizations think, decide, and execute.

The AI Native employee defines what great looks like.
They set constraints.
They design systems.
They use AI to enhance clarity, not to outsource thinking.

Thomas Bustos breaks down why companies that fail to build AI Native systems will struggle with accountability, context gaps, and slow decision loops. And why the real competitive advantage is how employees integrate AI into learning velocity and decision quality.

This episode is a blueprint for leaders who want to move from AI curiosity to AI Native execution.

Top Takeaways:
If your company is just getting seats for people to ask things to ChatGPT, you definitely need to change something.

Create accountability and a motion of learning velocity.

The more connected your context system is, the better decisions can be made.

The quality of decisions depends on how much reality your team can see.

Software is going to zero, meaning the cost of building solutions is decreasing.

The jobs are not going anywhere; they are evolving with technology.

If you can generate more impact, you are more valuable.

Creating systems that enhance learning and context is crucial for growth.

This is why we're playing this game: to continuously learn and adapt.

Connect with Thomas Bustos
Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

Let’s Talk AI - https://thomasbustos.substack.com/ 

Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

AI native companies represent a structural evolution in startup design. Unlike traditional companies that adopt AI as an efficiency layer, AI native companies integrate AI into the core decision-making fabric of the organization.

In this episode, Thomas Bustos analyzes how AI native companies restructure three critical dimensions: learning velocity, decision quality, and organizational leverage. The discussion outlines how teams can use AI to improve strategic clarity, accelerate feedback loops, and create shared maps of reality across product, sales, and leadership.

The episode also examines how AI native companies change internal dynamics—shifting from siloed expertise toward collaborative intelligence systems where humans and AI co-create insights. Thomas breaks down the implications for early-stage growth, team empowerment, and the future of work, highlighting why startups that fail to evolve into AI native companies will struggle to compete with organizations built natively for AI-enabled decision-making.

This framework positions AI not as a productivity hack, but as an organizational multiplier.

Top Takeaways:
The better your questions, the more valuable you are as the founder.

AI native companies focus on culture and systems, not just tools.

You need blueprints and processes to exceed early revenue stages.

Context lives in people's heads and dies when they leave the room.

AI amplifies opinions and requires strong leadership to be effective.

No human in the loop means empowering humans to make better decisions.

Building a context engine can improve decision-making and growth.

AI native companies have a unique competitive advantage over larger firms.

Understanding core concepts is essential for strategic growth.

Evaluating AI nativeness helps identify areas for improvement.

Connect with Thomas Bustos
Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

Let’s Talk AI - https://thomasbustos.substack.com/ 

Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.

Most advice for early stage founders is wrong because it assumes you’re ready to scale.

In this episode, Thomas Bustos challenges the obsession with growth, optimization, and “best practices.” He argues that for an early stage founder, those things are distractions without a learning engine underneath them.

Instead of asking “How do we grow faster?”, the better question is “How do we learn faster?” This episode explains why premature scaling breaks teams, why metrics without context mislead founders, and why decision quality compounds more than growth ever will.

If you’re an early stage founder feeling pressure to move faster without clarity, this episode is a necessary counterweight.

Top Takeaways:
The journey to scaling requires a clear understanding of what to do and what to stop doing.

Early stage founders must focus on building a strong operating system to navigate growth.

A learning engine is crucial for making informed decisions and improving outcomes.

Resource allocation should be strategic, focusing on the best bets for growth.

Aligning sales and product development is key to delivering value to customers.

Understanding the ideal customer profile helps in refining business strategies.

Pattern identification can lead to better decision-making and revenue generation.

Founders should embrace chaos as a learning opportunity to refine their approach.

A methodical path reduces uncertainty and enhances clarity in decision-making.

The ability to adapt and iterate is essential for early stage success.

Connect with Thomas Bustos
Thomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ 

Let’s Talk AI - https://thomasbustos.substack.com/ 

Let’s Talk AI on YouTube - https://www.youtube.com/@lets-talk-ai 

Let’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAP

Hosted on Ausha. See ausha.co/privacy-policy for more information.