OPEN Tech Talks: AI worth Talking| Artificial Intelligence |Tools & Tips

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
Today's Guest:
Dr. Ewelina Kurtys, Scientist from FinalSpark
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
Resources:
FinalSpark

Episode # 183
Today's Guest:
Adriel Desautels, Founder & CEO, Netragard
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
 
Resources:
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
Today's Guest:
Mindaugas (Min) Maciulis, Founder & CEO of Strategic AI Advisors
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
Resources:
Strategic Advisors

This week, I've been thinking about something slightly uncomfortable.
Last weekend, I was reviewing one of my older architecture diagrams from five years ago. A cloud-native migration plan I was deeply proud of at the time. It was clean. Structured. Scalable.
And then I asked myself:
If I were to rebuild this today in the era of generative AI…
Would I build it the same way?
The honest answer?
No.
Not because it was wrong.
But because our assumptions have changed.
Two years ago, AI was a feature.
Today, AI is shaping architecture decisions.
We're not just designing systems anymore.
We're designing systems that design, generate, predict, and automate.
And here's the tension I keep seeing in enterprise conversations:
Everyone wants AI.
But very few are asking:
"What technical debt are we creating while chasing it?"
That's why today's conversation matters.
Today, I'm joined by Maxim Salav, based in Australia, someone who works deeply in enterprise architecture and technical debt remediation.
And this episode is not about hype.
It's about responsibility.
Because AI doesn't remove architectural complexity.
In many cases, it amplifies it.
Let's get into it.
Chapters
00:00 Introduction to Technical Debt and Architecture
01:34 The Impact of AI on Technical Debt
04:12 Generative AI and Architectural Challenges
08:40 Adopting AI in Organizations
12:26 Building AI Strategies and Governance
17:33 Data Quality and AI Integration
22:43 Guardrails for AI Adoption
Episode # 181
Today's Guest:
Maxim Silaev, Technology Advisor and Enterprise Architect
He is a technology advisor and enterprise architect with more than two decades of experience working with high-growth companies, complex systems, and business-critical platforms.
Website: Arch-Experts
What Listeners Will Learn:
What technical debt really means in the AI era
How generative AI can unintentionally increase hidden system risk
Why architecture remains critical despite AI coding tools
The importance of governance and verification layers in AI systems
How large enterprises are cautiously integrating AI
Why strategy must precede AI deployment
The evolving role of enterprise architects in AI-native environments
Resources:
Arch-Experts

Chapters
00:00 Introduction to Kara Williams
01:53 Kara's Coaching Journey and Entrepreneurial Background
03:20 The Importance of a Simplified Tech Stack
05:51 Common Mistakes in Tech Selection
07:09 Exploring AI in Business
08:16 Creating the Proof First GPT
10:47 Learning and Executing with AI
12:04 Common Challenges Faced by Entrepreneurs
13:50 Guiding New Entrepreneurs
14:59 Misconceptions About Low Ticket Offers
16:18 Refining Messaging and Offers
17:29 The Role of Automation in Business
18:34 Understanding Automation Needs
19:36 Testing Freebies and Building Relationships
20:29 Lessons Learned in Business
21:20 Future Plans and Refinements
22:31 Final Tips for Entrepreneurs
Episode # 180
Today's Guest:
Kara Williams, Founder, GHL Mastery Academy
She is the founder of GHL Mastery Academy, where she helps CEOs stop being the bottleneck in their business by turning their VA, OBM, or EA into a trained backend powerhouse.
Website: Kara Williams
Youtube: GHL Mastery Academy
What Listeners Will Learn:
Why "cheap tool stacking" quietly becomes expensive (money + time + broken trust)
How to think about systems like a real business owner (not a hobbyist)
Why reliability matters more than feature-count in early-stage tech stacks
How entrepreneurs can use AI to validate offers before building full courses or funnels
What automation is actually for: visibility, testing, and removing blind spots
How to simplify business operations without losing flexibility or creativity
Resources:
Website: Kara Williams