Builders by Proxify

Are we losing control of AI? As intelligent systems become increasingly autonomous, who takes responsibility for the decisions they make?In Part 2 of Builders, our host Lee Haynes continues his conversation with Ingo Paas, former CIO of Green Cargo, now board member at Svenska kraftnät, and author of A Billion Times Smarter and Digital Composable Enterprises.Drawing on his experience leading digital transformation and researching the implications of superintelligence, Ingo explores what happens when decision-making authority shifts from humans to intelligent systems.They discuss what it means to preserve human agency in an AI-driven world, whether we can maintain control over increasingly autonomous systems, and why traditional approaches to AI governance may no longer be enough.The conversation also explores how AI is changing leadership, why five-year business strategies are becoming harder to defend, and what skills will matter as intelligent systems become more capable.In this episode:• Why human agency matters in the age of AI• What happens when machines start making decisions for us• How AI twins could help protect human autonomy• Why AI’s impact matters more than its intelligence• Whether losing control of AI is inevitable• Who takes responsibility when autonomous systems make decisions• The rise of superintelligence and intelligent infrastructure• What skills will matter in an AI-driven world• Why traditional business strategies need to change• How leaders can prepare for the future of AIChapters:00:00 Introduction: The rise of human agency03:31 How do we preserve human autonomy?05:08 The concept of an AI twin06:34 Can we measure AI's impact on society?07:48 Are we asking the wrong questions about AI?10:58 Can we avoid losing control of AI?13:21 Who is responsible for autonomous AI?14:46 Superintelligence and the future of humanity17:43 What skills will matter in an AI-driven world?19:47 Knowledge vs knowing how to apply it21:12 How businesses need to change21:44 Why long-term strategy is becoming obsolete23:28 Strategy inside intelligent infrastructure25:17 What leaders should do in the next 12 months26:00 Final thoughtsAbout BuildersBuilders is a podcast by Proxify exploring the people, ideas, and technologies shaping the future of business and technology. Hosted by Lee Haynes, each episode features conversations with founders, executives, and technology leaders about building, leading, and adapting in a changing world.Subscribe for more conversations about AI, technology, leadership, and the future of business.

What if the future of AI in business isn’t about adding AI to your existing software, but replacing the software altogether?

In Part 1 of this conversation, our host Lee Haynes sits down with technology executive, board member, and author Ingo Paas to examine why most companies may be approaching AI transformation from the wrong direction.

Organizations are adding AI to existing processes, SaaS applications, and fragmented technology stacks. Ingo argues that this misses the bigger shift. If AI can take over the processes those applications were built to support, much of today’s SaaS stack could eventually disappear.They also discuss why traditional governance struggles to keep pace with AI, why boards are often not asking the right questions, and why three and five-year strategies become increasingly difficult when the underlying technology can change dramatically within months.

In this episode:

• Why traditional governance struggles with AI

• The growing transfer of decision-making authority to machines

• What boards are missing about AI transformation

• Why AI could make organizational fragmentation worse

• How leaders should balance experimentation with running the existing business

• Why adding AI to existing SaaS may be the wrong approach

• Ingo’s case for replacing SaaS applications with AI

• Why companies need to start redesigning from the core

Chapters:

00:00 Introduction

01:42 Ingo Paas’ background

03:06 Why AI transformation is only beginning

04:38 Why traditional governance fails with AI

06:45 What AI governance should look like

09:10 Governing AI in real time

11:28 The data problem companies already have

13:50 When machines start making decisions

14:40 What surveillance and warfare teach us about AI

19:10 What boards are getting wrong about AI

22:27 Why AI requires a different kind of leadership

24:32 The problem of fragmented organizations

25:54 Is AI the end of SaaS?

28:52 Experimentation vs paralysis

What does it take to build a career in AI when the technology changes faster than any university curriculum can keep up?

Joana Fonseca is an AI engineer at TRATON with a PhD in machine learning and robotics from KTH Royal Institute of Technology. Yet even after a bachelor’s, master’s, and PhD, she had never learned or used generative AI. When she finished her PhD, she had to learn transformers and generative AI herself. In this episode of Builders, Joana talks about the foundations AI engineers need, why continuous learning is unavoidable, and what separates someone who can build an impressive prototype from someone who can build reliable, valuable AI systems.

She also discusses the importance of domain collaboration and data engineering, what makes a company truly AI-native, where vision language models (VLMs) and vision language action models (VLAs) are heading, and what AI could look like by 2030.

Joana also shares what she is seeing through Stockholm AI and why the conversation around AI increasingly extends beyond models into infrastructure, governance, policy, and societal impact.

Chapters

00:00 VLMs and autonomous driving

00:34 Introduction

00:48 What does it take to become an AI engineer?

01:38 The foundations every AI engineer needs

02:03 Why continuous learning is unavoidable

02:37 Even PhDs had to learn generative AI

03:05 Learning AI after a PhD

04:39 How important is domain expertise?

05:04 Why collaboration matters in AI

06:15 What separates strong AI engineers?

06:31 Building AI beyond the prototype

07:04 The importance of data engineering

07:17 What the AI community is talking about

09:30 AI's impact beyond technology

10:50 What makes a company AI-native?

12:30 What's next for VLMs and VLAs?

13:30 The causality problem in autonomous driving

16:00 How researchers are improving VLAs

17:00 What will AI look like in 2030?

19:00 Advice for the next generation of AI engineers

19:22 Why lifelong learning matters

Joana Fonseca is an AI engineer at TRATON, working on AI for autonomous driving.

In this episode of Builders, Joana explains how vision language models (VLMs) and vision language action models (VLAs) could change autonomous driving by helping vehicles understand entire scenarios rather than simply detecting individual objects.

She also breaks down what makes deploying AI in the physical world so difficult, from latency and model size to safety, edge cases, testing, and the huge amounts of diverse real-world data required.

Joana holds a PhD in machine learning and robotics from KTH Royal Institute of Technology. Her work has taken her from developing algorithms for autonomous submarines to building AI systems for autonomous trucks at TRATON.In this episode:

• How VLMs differ from traditional computer vision
• How AI can understand an entire driving scenario
• The role of VLAs in autonomous vehicles
• Why 99.99% reliability may still not be enough
• Why latency becomes critical when AI operates in the physical world
• The data problem behind autonomous driving
• Why moving from prototype to production remains so difficult
• What production-ready AI looks like for autonomous systems

Subscribe to Builders for more conversations with the people building the future of technology, AI, and engineering.

Chapters

(00:00) Introduction

(01:41)⁠ From robotics to autonomous driving

(03:49)⁠ Building AI for autonomous trucks

(⁠04:47)⁠ What are vision language models?

(⁠05:48)⁠ VLMs vs traditional computer vision

(⁠07:34)⁠ From VLMs to vision language action models

(⁠09:51)⁠ The black box problem

(⁠12:38)⁠ Why 99.99% safety isn’t enough

(⁠13:57)⁠ When AI latency becomes dangerous

(⁠15:01)⁠ Testing AI in the physical world

(⁠16:12)⁠ The autonomous driving data problem

(⁠17:28)⁠ Where current AI models fall short

(⁠18:49)⁠ Why prototypes struggle to reach production

(⁠19:52)⁠ Why robotaxis haven’t scaled everywhere

(⁠21:36)⁠ What makes AI production-ready

AI is rapidly evolving from a powerful tool into a transformative force for organizations and society.In this keynote, Jesper Fredriksson explores the current state of AI, the accelerating pace of technological advancement, and the challenges organizations face when moving from experimentation to large-scale adoption.A central theme is the "last mile" problem—the gap between impressive AI capabilities and fully automated, real-world outcomes. Jesper discusses how AI is reshaping software engineering, productivity, and decision-making, while highlighting the importance of human oversight, guardrails, and responsible implementation.In this episode:- AI's evolving capabilities and future potential- The last mile challenge in automation- AI's impact on software engineering and productivity- Technical and organizational barriers to adoption- Human oversight and guardrails in AI systems- Risks and ethical considerations of advanced AI- Moving from AI experimentation to measurable outcomes#AI #Automation #ArtificialIntelligence #SoftwareEngineering #Productivity #FutureOfAI #AIInnovation

I agents are becoming more capable and more autonomous. But as they gain access to tools, memory, and company data, they also introduce new security risks.

In this Proxify Talk, Ali Leylani, Co-founder & Chief AI Officer at Echo Alpha and Chairman of Stockholm AI, explains how AI agent systems work, where they’re vulnerable, and what engineering teams should think about before deploying them at scale. Drawing on his background in AI, cybersecurity, and defense technology, Ali explores the emerging attack surface created by autonomous and multi-agent systems.

In this episode, you’ll learn:

• How AI agents combine language models, memory, and external tools to perform complex tasks
• Why multi-agent systems introduce new security challenges
• How prompt injection, jailbreaks, and other attack techniques target AI systems
• Why a single compromised agent or data source can affect an entire workflow
• How malicious instructions can be hidden inside images and other multimodal inputs

• What organizations should consider before giving AI agents greater autonomy.

Whether you’re building AI products, leading an engineering team, or exploring agentic workflows, this talk offers a practical look at one of the biggest challenges facing AI adoption today.

#AISecurity #ArtificialIntelligence #CyberSecurity #AIAgents #ProxifyTalks

AI implementation is easy. AI adoption is hard.In this episode, Louise Vanerell and Carl Carlheim-Gyllensköld explore why so many AI initiatives fail to create lasting organizational value despite strong technology investments. The conversation focuses on a critical but often overlooked element of successful AI transformation: the human layer.Together, they discuss how organizations can move beyond technical deployment and drive real behavioral change through effective communication, psychological safety, change management, and internal champions.Whether you’re leading an AI transformation, building an AI strategy, or helping teams adapt to new technologies, this episode offers practical insights for turning AI adoption into measurable business outcomes.In this episode:

• Why AI adoption often fails in organizations
• The importance of engineering the human layer
• Building trust and psychological safety around AI
• Effective communication and change management strategies
• The role of champions in technology diffusion
• Driving behavioral change across teams
• Moving from AI usage to organizational value
• Practical lessons for leaders navigating AI transformation

If you enjoyed this conversation, subscribe for more discussions on AI strategy, organizational transformation, leadership, and the future of work.#AIAdoption #ArtificialIntelligence #ChangeManagement #AIStrategy #DigitalTransformation #Leadership #FutureOfWork #Innovation #BusinessTransformation #AI

How do companies move beyond AI pilots and actually scale AI across their organization?In this keynote from Proxify HQ, AI strategist and transformation expert Rocío Bachmaier shares practical lessons from helping organizations adopt, implement, and scale AI successfully. Drawing from real-world experience working with companies across industries, she explores the most common mistakes that prevent AI initiatives from delivering meaningful ROI, and what forward-thinking organizations are doing differently.You’ll learn:• The 3 biggest mistakes companies make when scaling AI• Why leadership involvement is critical for AI transformation• How to identify high-impact AI workflows• The difference between AI experimentation and AI adoption• Why AI governance shouldn’t be an afterthought• How AI-native companies are redesigning workflows• What agentic AI means for the future of work• The concept of compound learning in AI systems• How organizations can balance human expertise and AI capabilities• Why AI literacy and training are becoming essential business skillsWhether you’re a founder, technology leader, product manager, developer, or business executive, this keynote offers actionable insights for building AI strategies that create lasting value.This keynote is part of a new series from Proxify, where we share conversations, talks, and expert insights from our headquarters with the wider technology community. Our goal is simple: foster curiosity, share knowledge, and help professionals navigate the future of technology together.

Subscribe for more keynotes, expert discussions, the Builders podcast, and insights on AI, software development, leadership, and the future of work.About Proxify:Proxify connects businesses with the world’s top remote software developers, helping companies scale engineering teams quickly and effectively.Learn more: https://proxify.io

AI is changing technical roles faster than ever. But does that mean customer-facing engineers are becoming obsolete?In this episode of Builders, Lee Haynes sits down with Liliana Lindberg, Lead Solutions Engineer at Slack, who has also worked at Google and startups throughout her career. They discuss what solutions engineers actually do, why technical expertise still matters in the age of AI, and why blindly trusting AI can create bigger problems than it solves.Liliana also shares her unconventional journey into tech, why she thought programming wasn’t for her, and how she built a successful career without following the traditional management path.In this episode, you’ll learn:• What a Solutions Engineer actually does• Why AI won’t replace customer-facing technical roles• How Slack uses AI to improve productivity• The biggest mistakes people make when using AI• Why technical knowledge is still essential in the AI era• Startup vs. big tech, what each environment teaches you• Why becoming a manager isn’t the only path to career growth• The skills every modern Solutions Engineer needs• How curiosity became Liliana’s biggest career advantage• Practical advice for anyone building a career in technologyWhether you’re a software engineer, solutions engineer, engineering leader, founder, or simply curious about how AI is reshaping work, this conversation offers practical insights you can apply today.Subscribe for more conversations with engineering leaders, technology executives, and innovators building the future of software.Chapters00:00 Introduction01:09 From psychology to engineering03:13 Thinking she chose the wrong career04:37 What a Solutions Engineer actually does07:00 The biggest misconceptions about the role09:23 The skills that matter most12:41 Startups vs. big tech18:31 Why she chose the individual contributor path22:46 How AI is changing technical work26:32 Why you shouldn’t trust AI blindly28:54 Why Slack changed how she works31:18 AI agents and the future of collaboration34:57 Avoiding AI tool overload36:36 The best workflow she’s seen in Slack37:46 What she looks for when hiring41:58 Building trust in remote teams42:57 Career advice she wishes she’d received sooner45:15 What’s next for Slack46:42 Advice for aspiring Solutions Engineers

AI, organizational transformation, leadership, hiring, AI native companies, the future of work, organizational design, and AI adoption are changing how businesses operate.

In this episode of Builders, Armin Catovic, Director of Data & AI at Funnel, shares a fascinating perspective on how AI is fundamentally transforming organizations—from leadership structures and hiring practices to decision-making, workflows, and team design.

We dive into the critical difference between AI-enabled and AI-native companies, why many organizations are underestimating the scale of change ahead, and how AI could reshape competition across entire industries. Armin also explores the future of talent, the evolving role of managers, and why smaller, more agile teams may become the new standard in the AI era.

If you're a founder, executive, manager, or technology leader trying to understand what AI means for the future of work, this conversation is packed with practical insights and forward-looking ideas.

Topics covered

:• AI-enabled vs. AI-native organizations

• Organizational transformation through AI

• Leadership and decision-making in the AI era

• The future of hiring and talent development

• AI-driven workflows and collaboration

• Attention economics and competitive advantage

• Smaller, more effective teams

• Operationalizing AI across the business

• The future of work and organizational design

#AI #FutureOfWork #Leadership #ArtificialIntelligence #AINative #AIAdoption #OrganizationalTransformation #Hiring #Management #BuildersPodcast

Chapters

(00:00) Why AI Is Forcing Companies to Rethink Everything

(01:12) Armin Catovic's Journey into Data, AI & Leadership

(03:02) How AI Is Changing Organizations Faster Than Expected

(04:42) AI-Enabled vs. AI-Native Companies: The Critical Difference

(06:49) Why Traditional Organizational Structures May Not Survive AI

(09:07) AI's Growing Role in Workflows, Decisions & Execution

(12:17) Addressing Fear, Uncertainty & Workforce Concerns Around AI

(16:21) The Surprising Relationship Between AI and Software Demand

(18:13) Winning the Battle for Attention in the AI Era

(20:53) How AI Is Reshaping Competition Across Industries

(22:02) The Future of Talent and the Rise of Junior Inversion

(24:39) Developing Talent in an AI-Driven Workplace

(25:06) Why Investing in Future Talent Matters More Than Ever(27:09) Essential Skills for Success in the Age of AI

(30:22) How Collaboration Is Evolving Across Modern Tech Teams

(32:49) Leadership in the Age of AI: What Changes and What Doesn't

(36:03) Why Smaller, More Agile Teams Are Winning

(37:44) Moving AI from Experiments to Real Business Impact

(43:31) Becoming an AI-Native Company: Practical Steps Forward

(45:44) The Biggest AI Surprises Still Ahead