Over the last couple of years, most of my conversations around AI have been about capability.

How fast models are improving.

How agents are becoming more autonomous.

How enterprises can adopt GenAI safely.

How teams can redesign workflows around intelligence.

But this week, I found myself thinking about something deeper.

Not what AI can do.

But what does AI cost?

And I don't just mean money.

I mean energy.

I mean infrastructure.

I mean the hidden assumptions underneath the current AI boom.

Because when we talk about the future of AI, most people immediately jump to models, chips, data centers, agents, and software stacks.

But as someone who works closely with organizations trying to operationalize AI in the real world, I keep coming back to a harder question:

What happens when the current compute model itself becomes the bottleneck?

This is not a question most teams are asking yet.

But it is a question serious builders should start paying attention to.

This week, while reviewing different enterprise AI patterns and thinking through long-term architecture choices, I realized that much of the current AI conversation still happens within the assumptions of silicon, scale, and software abstraction.

But what if the next major shift is not a better model?

What if it is a different computing substrate altogether?

That's exactly why today's conversation is important.

Because this episode is not about another AI app.

It is not about another wrapper.

It is not about another productivity layer.

It is about something much more fundamental:

What might come after silicon, and how should we think about it today?

Chapters:

00:00 Introduction to Ewelina Kurtis and Final Spark
00:52 Understanding Living Neurons and Their Potential
02:44 The Vision Behind Final Spark
05:34 Current Progress and Future Goals
08:27 Collaborations and Research Opportunities
11:17 Programming Living Neurons
14:02 Ethical Considerations in Biocomputing
16:59 Benefits of Biocomputing for Society
19:39 Advice for Aspiring Bioengineers
22:30 Commercial Aspects of Final Spark
24:24 Investor Insights and Future Directions

Episode # 184

• Website: FinalSpark

What Listeners Will Learn:

• Why the future of AI may require rethinking computation itself, not just models
• How energy efficiency is becoming a core strategic issue in AI
• What biocomputing means in simple terms
• How living-neuron-based computing differs from traditional silicon-based systems
• Why future AI progress may depend on alternative hardware paradigms
• How emerging scientific computing trends should matter to enterprise AI leaders today
• Why staying ahead in AI means looking beyond current tools and architectures

• FinalSpark

Episode # 183

Adriel is a leader in cybersecurity with over 20 years of experience. Adriel founded Secure Network Operations and the SNOsoft Research Team, whose vulnerability research helped shape modern responsible disclosure practices. He later launched Netragard, pioneering Realistic Threat Penetration Testing, which he now call Red Teaming, and expanding into a broad range of security services.

• Website: Netregard
• X/Twitter: Netregard

What Listeners Will Learn:

• Why "AI penetration testing" is often closer to automated scanning than real offensive testing
• How AI changes security risk mainly through volume and speed, not necessarily sophistication
• Where organizations get misled into a false sense of security
• Why "preventing breach" is unrealistic and why limiting damage paths matters more
• What cybersecurity professionals should focus on to stay relevant in the LLM era
• How AI may influence vulnerability research, but still struggles with novel exploitation thinking

• Netregard

Welcome to Open Tech Talks.

Quick note before we start, thank you.

The messages, the feedback, the "keep this practical" reminders… they've been incredibly helpful. Open Tech Talks has always been a weekly sandbox for technology insights, experimentation, and inspiration—with one objective: learn, test, and share what's real.

Now, a personal moment from this week.

A few days ago, I sat with a business owner who said something that stuck with me:

"AI is everywhere… but I don't know where to start without breaking my business."

And that's the truth for most companies, especially small businesses.

Because "start with AI" sounds simple… until it touches real operations:

• leads that go cold,

• follow-ups that don't happen,

• teams that feel overwhelmed,

• tools that multiply,

• processes that nobody can explain clearly.

Most AI projects don't fail because the model is weak.

They fail because the process is unclear, the team is overloaded, and the strategy is missing.

Let's begin.

Episode # 182

He works with CEOs, COOs, and operating partners in the $20M–$250M range who are ready to go beyond pilots and turn AI into real EBITDA growth. His proven 90-day sprint framework, AImpact OS, delivers measurable lifts across productivity, customer service, and sales.

• Website: Strategic Advisors

What Listeners Will Learn:

• Identify the best "starting point" for AI using business pain, not hype
• Understand why AI pilots fail mostly due to adoption (not technology)
• Learn a practical approach to simplify workflows before adding automation
• See how SMBs can move faster than enterprises in the AI era
• Understand the difference between augmentation and transformation with AI
• Learn how to avoid tool overload and focus on measurable outcomes

• Strategic Advisors