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 Stack06:07 Deep Work and Project Management08:55 Iterating on User Experience12:12 Final Thoughts on Engineering ToolsConnect with Thomas BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted 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 Pillars06:03 Understanding AI Adoption and Its Impact09:10 Strategies for Effective Implementation11:58 Metrics for AI Success14:51 Navigating AI Tools and Technologies18:07 Building a Learning Organization20:59 Final ThoughtsConnect with Thomas BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted 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 BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted 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 BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted 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 BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted on Ausha. See ausha.co/privacy-policy for more information.

Most early stage founders think their job is to grow. That belief quietly kills more startups than bad ideas ever will.In this episode, Thomas Bustos challenges the default startup narrative and argues that early stage founders should delay optimization, ignore premature scale, and focus obsessively on their first five customers. Not leads. Not users. Paying, real ICP customers.He dismantles the obsession with metrics that don’t matter yet and explains why clarity, learning velocity, and decision quality outperform hustle and headcount in the early days. The discussion explores why premature scaling leads to fragile systems, how visibility enables better decisions, and why early-stage teams must prioritize shared context over efficiency. Thomas also explains how tracking inputs and outputs creates feedback loops that prevent founders from optimizing the wrong things.This episode serves as a systems-level analysis of what actually matters for early stage founders before scale, funding, or hiring accelerates complexity.Top Takeaways:Early stage founders should optimize for learning over growth.Focus on inputs before outputs to ensure effective decision-making.Create a shared map of reality to align the team.User feedback is crucial for refining the mission and ideal customer profile.Metrics should reflect reality, not be treated as goals.High signal users provide valuable insights for progress.Building foundations is essential before scaling a startup.Learning velocity and decision quality are key to success.The early stage is characterized by messiness and uncertainty.Navigating through a dynamic map is vital for achieving product-market fit.Connect with Thomas BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode of Let’s Talk AI, Max Berthelot takes us into the heart of his founder journey. It is one that spans B2B SaaS, Y Combinator’s high-pressure halls, and now the health-tech frontier with Lucis.What starts as a leap from a stable career becomes a story about obsession, discipline, and the uncomfortable truths behind real startup growth. Max shares the moment he realized community was the key to expansion. He talks about the nights spent rethinking distribution, the emotional rollercoaster of working with both technical and non-technical co-founders, and the mindset that transformed Lucis from an idea into a fast-emerging longevity platform.This is a story of a founder recounting the lessons that scarred him, shaped him, and ultimately strengthened his ability to scale a startup in one of the toughest markets.If you want startup growth lessons that go beyond clichés, this is the episode to hear.Top Takeaways:Max is the founder of Lucis, focusing on health and longevity.Building a community around health can drive engagement and growth.Distribution should be prioritized from day one in product development.It's important to start with an idea and iterate rather than waiting for perfection.Y Combinator provided invaluable lessons and a life-changing experience for Max and his co-founders.Relentlessness is key to building a successful startup.Continuous learning is essential for founders as they navigate different stages of growth.The mission of Lucis is to make preventative health accessible in Europe.Max's personal experience with health data drives the mission of LucisConnect with Max BerthelotMax Berthelot on LinkedIn - https://www.linkedin.com/in/maximeberthelot/Instagram: @lucislife_en   Connect with Thomas BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted on Ausha. See ausha.co/privacy-policy for more information.

The IDE has always been the developer’s command center. But as AI systems mature, the IDE is becoming something far more sophisticated — a hybrid of automation, intelligence, and adaptability.In this episode, Thomas Bustos and Richard Wang (Clad Labs) dissect the architectural and strategic implications of building AI-powered IDEs. They discuss how real-time feedback loops, adaptive code assistance, and contextual learning models are reshaping both the developer experience and the business of software.This is a must-listen for engineers, founders, and product builders who want to understand the competitive edge behind AI-driven development tools, and why IDE innovation is the next major battleground for developer productivity.Top Insights:AI is revolutionizing the coding process for developers.Integrating ads into IDEs can subsidize development costs.User feedback is crucial for product iteration and improvement.The future of coding involves AI-native tools that enhance productivity.Democratizing access to AI tools is essential for global developers.Speed of development should focus on user-centric design.Understanding user workflows is key to building effective tools.The competitive landscape of IDEs is evolving with new approaches.Maintaining a balance between speed and quality is vital in development.The mission is to unlock coding opportunities for everyone.Connect with Richard WangRichard Wang on LinkedIn - https://www.linkedin.com/in/richard-wang-39554994/Clad Labs AI - https://www.cladlabs.ai/ Connect with Thomas BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted on Ausha. See ausha.co/privacy-policy for more information.

We often hear stories about engineers leaving Big Tech to chase their startup dreams, but few break down what really happens after the leap.In this episode, Anmol Sood, former Meta engineer and now founder of Alai, opens up about his journey from writing production-grade code to creating a startup that helps people build high-quality presentations in minutes.He shares how speed and quality constantly compete for attention in early-stage product development, why co-founder dynamics can make or break momentum, and the ultimate secret to building products that users love.For anyone navigating the startup ecosystem, Anmol’s story offers a grounded view of what it means to build, iterate, and keep your vision alive amid uncertainty.🎧 Listen to the full episode and discover how startup success is built one decision at a time.Top Insights:Anmol emphasizes the importance of working on problems that matter to you.Building skills is not just about technical ability but also about caring for the problem.Transitioning from a large company to a startup requires unlearning certain practices.Identifying a clear target audience is crucial for product success.Finding early partners can provide valuable feedback and insights.Intuition plays a key role in product development, especially pre-product market fit.Understanding customer incentives is essential for effective partnerships.Design quality is a significant differentiator in presentation tools.Balancing speed and quality is vital in the development process.Strong co-founder relationships are foundational to startup success.Connect with Anmol SoodAnmol Sood on LinkedIn - https://www.linkedin.com/in/anmolsood/Anmol Sood on X - https://x.com/anmolsood21 Alai - https://getalai.com/Hosted on Ausha. See ausha.co/privacy-policy for more information.

When artificial intelligence meets robotics, something remarkable happens: machines stop just following instructions and start understanding purpose.In this episode of Let’s Talk AI, Thomas Bustos sits down with Remi Fabre, a robotics engineer whose work bridges the gap between software intelligence and physical motion. Together, they unpack what it takes to design robots that don’t just move, but adapt, learn, and connect with the world around them.From the elegance of inverse kinematics to the power of open-source collaboration, Rémi shares how robotics has evolved from mechanical systems into intelligent ecosystems. They also explore the emotional dimension of robotics and why people form bonds with machines, and how AI is amplifying that connection.If you’ve ever wondered how intelligence takes shape in the real world, this episode reveals what’s next at the intersection of AI and robotics, where the digital brain meets the physical body.Top Insights:It's important for the human brain to do stuff that is hard.Open source has perks from the marketing side.You need to be comfortable with failure and iteration.The key word of robotics is robust.AI has access only to the high-level decision for now.I think it's fine that the robot has a personality.You can create a new language for movement.The future of robotics is incredibly exciting.You should always try to improve on what you're doing.Connect with Remi FabreRemi Fabre on LinkedIn - https://fr.linkedin.com/in/remifabreRemi Fabre on YouTube - https://www.youtube.com/channel/UCDuIkx0YEzUMgzpVUu3aatw Remi Fabre on X - https://x.com/RemiFabreRobotHosted on Ausha. See ausha.co/privacy-policy for more information.

Behind every breakthrough is a builder with a playbook - one that’s forged through trial, mentorship, and iteration.In this episode, Thomas Bustos dissects the AI Engineer Playbook, a systems-level look at how great builders think, learn, and execute.He unpacks how to design your own playbook, why copying others will never scale, and how the future belongs to engineers who combine speed, control, and empathy for users.You’ll walk away challenged to rethink your process, reconnect with your community, and commit to growth that’s as intentional as it is technical.Listen to this solo deep dive into what it truly takes to create, learn, and lead as an AI engineer.Top Insights:Understand the value of solving problems and time saved.Minimize human involvement in engineering processes.Focus on user experience to solve the right problems.Building is a long-term game requiring curiosity and iteration.Define your unique playbook based on personal interests.Speed and control are essential in engineering excellence.Engage with communities and share your work publicly.Mentorship is crucial for growth and direction.Iterate on your goals weekly for continuous improvement.Be curious and explore new tools and technologies.Connect with Thomas BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted on Ausha. See ausha.co/privacy-policy for more information.

The path from zero to one is an equation that involves technology, psychology, and culture. Adam Haney of Infinity Constellation breaks down how modern teams can achieve product-market fit while preserving a human-centric mindset. The discussion examines the convergence of AI tools, leadership communication, and strategic hiring, offering a lens into how effective collaboration fuels innovation. It’s a grounded conversation on what tech leadership looks like in 2025 and beyond.Top Insights:Technology must harmonize with human-centric services.The zero to one phase is crucial for startups.AI tools are changing the landscape of product development.Unified vision enhances team velocity and effectiveness.Building relationships between tech and operations is vital.Iterative frameworks help in strategic planning.Experience teaches the importance of adaptability in leadership. The best AI services businesses need to be tightly coupled with operations.Hands-on experience in engineering fosters a well-rounded skill set.AI services are becoming viable for hyperscale companies.Understanding customer needs is essential for service success.Intuition and data should reinforce each other in decision-making.Surrounding yourself with ambitious people raises your own standards.Connect with Adam HaneyAdam Haney on LinkedIn - https://www.linkedin.com/in/adamjhaney/ Infinity Constellation - https://www.infinityconstellation.com/ Open Story Podcast - https://openstorypodcast.com/ Connect with Thomas BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted on Ausha. See ausha.co/privacy-policy for more information.

Everyone says podcasting is already saturated, but Kevin Smith would disagree. While most platforms focus on distribution, he’s betting on AI as the core experience. As co-founder of Snipd, Kevin argues that the future of podcasting isn’t about more shows, but about smarter listening: capturing key ideas, surfacing highlights, and creating shareable knowledge from endless hours of audio. In this conversation, Thomas Bustos pushes on the big questions: is AI enhancing the podcast experience, or is it reshaping it entirely? What happens when AI curates your takeaways better than you can? For anyone who thinks AI is just a background tool, this episode is a wake-up call. The podcast player itself is becoming intelligent.Top Insights:Decide what to focus on and forget everything else.Podcasts are the largest knowledge library in the world.You have to be relentlessly resourceful as a founder.Talk to people to understand their problems before building.Give it to five people, and you'll see the problems immediately.Snipd is a fully fledged podcast player with unique features.Put small building blocks on top of each other for success.The world needs more entrepreneurial energy and spirit.AI is changing how we interact with audio content.Exclusive Listener Offer!One month free premium access on Snipd - https://link.snipd.com/Cx7S/thomas Connect with Kevin SmithKevin Smith on LinkedIn - https://ch.linkedin.com/in/kevin-smith-673714b4 Snipd - https://www.snipd.com/ Hosted on Ausha. See ausha.co/privacy-policy for more information.

The conversation around AI engineering is shifting, and Alessandro Romano is at the center of it.In this episode of Let’s Talk AI, we explore why the future of AI is no longer just about tools. It’s now about people who can think critically, solve problems, and apply frameworks with purpose.Alessandro breaks down the evolution of roles in data science, the rise of hands-on workshops for real-world learning, and the practical use of frameworks like Crew AI and LangGraph.He also explains why observability isn’t a buzzword but a necessity for responsible AI development.If you’ve ever wondered how AI engineers can deliver business value without drowning in hype, this episode offers a grounded, professional perspective.Top Insights:A professional perspective in data science requires domain specificity.Experiences at conferences can lead to valuable networking opportunities.The role of a data scientist is evolving and often overlaps with AI engineering.AI engineering is about building solutions, not just maintaining infrastructure.Workshops can be effective for hands-on learning and engagement.Choosing the right framework depends on the specific problem being solved.Observability is crucial for understanding AI systems' decision-making processes.Problem-solving should be prioritized over tool selection in AI development.Experimentation with tools is essential for effective AI engineering.A strong foundation in software engineering enhances problem-solving capabilities.Connect with Alessandro RomanoAlessandro Romano’s Website - https://www.aromano.dev/Connect with Thomas BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted on Ausha. See ausha.co/privacy-policy for more information.

The world of AI engineering is exploding. Every week, there’s a new framework, a new tool, a new breakthrough. For beginners and even mid-career engineers, it can feel overwhelming. Where do you even start?In this episode of Let’s Talk AI, Miguel explains why hands-on learning beats passive theory, why systems thinking is the real skill every AI engineer needs, and why observability—understanding what’s happening inside your models and systems—is non-negotiable if you want to build responsibly.For anyone looking to break into AI engineering, this episode offers a roadmap: stop chasing every trend, start focusing on systems, and above all…Build, learn, and grow.Top Insights:Neural Maze aims to help aspiring engineers navigate the overwhelming AI landscape.Building AI systems requires thinking in terms of systems, not just code.Observability is crucial for deploying AI systems in production.Learning by building projects is the most effective way to gain skills.Filo Agents project connects philosophy with gaming, showcasing innovative applications of AI.The value of code has shifted; engineers must adapt to new technologies.Miguel encourages staying out of the hype and focusing on building meaningful projects.He plans to expand Neural Maze into a community for self-contained courses.Connect with Miguel Otero PedridoMiguel Otero Pedirdo on LinkedInMiguel Otero Pedrido on SubstackMiguel Otero Pedrido on Medium.com Miguel Otero Pedrido on GitHubMiguel Otero Pedrido on XThe Neural Maze on SubstackThe Neural Maze on YouTubeConnect with Thomas BustosThomas Bustos on LinkedInLet’s Talk AILet’s Talk AI on YouTubeLet’s Talk AI on SpotifyHosted on Ausha. See ausha.co/privacy-policy for more information.

We’re standing at the edge of an AI-driven future, and most people don’t realize how fast we’re getting there.In this episode of Let’s Talk AI, Nathan Labenz joins us to share insights from years at the forefront of AI development. We cover everything from the disruptive potential of AI-powered content creation to the deep ethical debates around autonomy, alignment, and safety.This is an urgent conversation because the decisions we make now—about adoption, guardrails, and governance—will define the next decade.Top Insights:There is radical uncertainty about AI's future.Nathan Labenz's mission is to understand AI's current state.AI is transforming content creation and marketing.Hands-on experience with AI is crucial for understanding its capabilities.AI can provide services that were previously expensive and inaccessible.The ideal user experience is for AI to automate tasks seamlessly.AI's rapid adoption is unprecedented in history.Ethical considerations around AI consciousness are complex.AI can outperform humans in specific tasks, but has weaknesses.The future of AI holds both exciting opportunities and significant risks.Connect with Nathan LabenzNathan Labenz’ Website - https://www.nathanlabenz.com/Nathan Labenz on LinkedIn - https://www.linkedin.com/in/nathanlabenzNathan Labenz on X - https://x.com/labenzThe Cognitive Revolution - https://www.cognitiverevolution.ai/Connect with Thomas BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted on Ausha. See ausha.co/privacy-policy for more information.

Most people think entrepreneurship is about having a groundbreaking idea. Pedro Goes disagrees.In this episode, the CEO of InEvent reveals why ideas are overrated, and why execution, adaptability, and timing matter far more. He shares unfiltered insights into securing funding, scaling globally, and leveraging AI-driven innovation to transform the events industry.We also dive into remote leadership, the brutal realities of startup life, and the surprising role luck plays in success. If you’ve been fed the “follow your passion” narrative, this episode will challenge everything you thought you knew about entrepreneurship.Top Insights:An idea's worth is tied to its revenue potential.AI can transform event technology in innovative ways.Starting a company often requires minimal initial investment.Persistence is key in securing funding and acceptance into accelerators.Founders must maintain a clear vision for their company.Effective decision-making involves prioritizing resources wisely.Market validation is crucial for product success.Iterative growth allows for focus and adaptation in business.Building an end-to-end platform enhances value propositions.Leadership in remote teams requires fairness and clear communication.Connect with Pedro GoesPedro Goes on LinkedIn - https://www.linkedin.com/in/pedrogoes/InEvent - https://inevent.com/Pedro Goes on X - https://x.com/goesineventConnect with Thomas BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted on Ausha. See ausha.co/privacy-policy for more information.

Data science freelancing is a test of both technical skill and strategic adaptability. In our conversation with Jérémy Arancio, we dissect the frameworks he uses to thrive: relentless upskilling, leveraging platforms like LinkedIn for visibility, and distinguishing between hype and practical tools in AI. From the nuanced differences of GenAI vs. NLP to the tactical mindset shift freelancing requires, this episode breaks down what separates sustainable data science careers from those that fizzle out.Top Insights:Freelancing is a means to learn and grow.You will never feel fully prepared to start freelancing.The journey of freelancing can be both rewarding and challenging.AI should be used to solve specific business problems.Content creation can help build connections and opportunities.Choosing the right vehicle for your career is crucial.Growth often comes from discomfort and challenges.Networking on platforms like LinkedIn is valuable for career advancement.Understanding the difference between Gen.AI and NLP is important.Continuous learning is essential in the fast-paced tech industry.Connect with Jeremy ArancioJeremy Arancio on LinkedIn - https://cz.linkedin.com/in/jeremy-arancioRead his articles - https://medium.com/@jeremyarancioJeremy Arancio on GitHub - https://github.com/JeremyArancioEmail him - jeremyarancio.freelance@gmail.comConnect with Thomas BustosThomas 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/6mVjFvdEkZDCTXpIuuSLAPHosted on Ausha. See ausha.co/privacy-policy for more information.

AI is evolving fast, but are our systems, ethics, and infrastructure keeping up?In this episode, Thomas Bustos and Marijn Markus break down the complex interplay between innovation and responsibility in artificial intelligence and machine learning. They examine current limitations in explainability and bias, and unpack what it will take to build scalable, transparent, and human-centric systems.With examples from healthcare and finance, and a strong focus on aligning AI with social good, this episode offers both technical depth and strategic perspective. Whether you’re designing ML pipelines or debating LLM regulation, this conversation delivers insight you won’t want to miss.Top Insights:Ethical and Data Challenges: AI systems must navigate ethical considerations and data privacy concerns, ensuring transparency and accountability.Innovation and Impact: AI has the potential to revolutionize industries, from healthcare to finance, by driving advancements and creating new opportunities.Regulatory and Legacy Issues: Balancing innovation with necessary regulations and overcoming legacy systems are key challenges for AI adoption.Bias and Misinformation: Addressing bias in AI models and combating misinformation are critical for ensuring AI's positive impact on society.Job Market Evolution: While AI may automate some jobs, it also creates new opportunities, emphasizing the need for adaptability and skill diversification.Human Responsibility: The importance of human oversight in AI systems is crucial, as technology should serve humanity's best interests.Connect with Marijn MarkusMarijn Markus on LinkedIn - https://www.linkedin.com/in/marijnmarkusCapgemini - https://www.capgemini.com/ Connect with Thomas Bustos & Let's Talk AIThomas Bustos on LinkedIn - https://www.linkedin.com/in/thomasbustos/ Substack - 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/6mVjFvdEkZDCTXpIuuSLAPHosted on Ausha. See ausha.co/privacy-policy for more information.

What do you really need to thrive as a data engineer today?Ananth Packkildurai joins us to cut through the noise. From his experience building systems at Slack to his current role in data engineering, Ananth reveals what skills truly stand the test of time, like SQL, data modeling, and a deep understanding of user needs. We also explore how the rise of Generative AI is changing the game, what observability means in practice, and why chasing the latest trend might not be your best move. Clear, practical, and refreshingly honest. This episode is for anyone who wants to grow with intention.Top Insights:Data engineering is at a pivotal moment, similar to the industrial revolution.The demand for data engineering skills is rapidly increasing.Understanding SQL is crucial as it constitutes the majority of data workloads.Feature prioritization should focus on high yield, low effort projects.Observability is essential for building reliable data systems.Scaling challenges often arise from unexpected user demand spikes.Product thinking is important in data engineering to meet user needs.GenAI has the potential to revolutionize data engineering practices.Exploring various aspects of data engineering is beneficial before specializing.Real-world observation can enhance understanding of data engineering concepts.Connect with Ananth PackkilduraiAnanth Packkildurai on LinkedIn - https://www.linkedin.com/in/ananthduraiData Engineering Weekly by Ananth Packkildurai - https://www.dataengineeringweekly.com/ Connect with Thomas BustosThomas 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-aiLet’s Talk AI on Spotify - https://open.spotify.com/show/6mVjFvdEkZDCTXpIuuSLAPHosted on Ausha. See ausha.co/privacy-policy for more information.