customizr domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home8/wotech/public_html/wp-includes/functions.php on line 6170AI has changed the conversation.
Not because machines suddenly became intelligent, but because millions of ordinary people suddenly gained access to something that felt extraordinary.
I still remember when ChatGPT first became available. Like many people, I opened it with curiosity. I wasn’t looking for shortcuts. I wanted to understand what was actually happening.
Every week since then, I have spoken with founders, researchers, engineers, and business leaders from around the world. One thing became very clear.
Technology changes quickly.
People don’t.
Many businesses are still asking the wrong question.
“How can AI do my work?”
Instead, we should ask,
“How can AI help me become better at the work only I can do?”
That’s a very different conversation.
I’ve learned that AI can generate content.
It can analyze data.
It can write code.
But it still cannot replace purpose.
It cannot replace your experiences.
It cannot replace your story.
The companies that will succeed over the next few years won’t simply use more AI.
They will know how to combine technology with authenticity.
Today’s conversation is exactly about that.
Not just where AI is going…
…but where we, as humans, need to grow alongside it.
Let’s begin.
Episode # 192
She is a cultural innovator blending ancient wisdom with cutting-edge AI to transform how businesses tell their stories and shape the future.
What Listeners Will Learn:
For the last two years, almost every conversation in technology has been about AI.
New models.
New tools.
New agents.
New automation.
And honestly, I understand the excitement.
I’ve spent more than 20 years working in technology, cloud, AI, and enterprise transformation. I’ve seen many waves of innovation. But this one feels different.
Yet something has been bothering me.
Every conference I attend, every LinkedIn post I read, every discussion I have with business leaders seems focused on one question:
“What can AI do?”
Very few people are asking:
“What should I become?”
A few years ago, if someone wanted career growth, the advice was simple.
Work hard.
Gain experience.
Move to the next role.
Today, that roadmap is disappearing.
The world is changing faster than job descriptions can keep up.
And that’s why this conversation matters.
Because the future may not belong to the people with the best title.
It may belong to the people who remain curious, adaptable, connected, and willing to keep learning.
In this episode, we’re not talking about prompts or models.
We’re talking about people.
Because while AI is changing work, humans are still responsible for creating meaning.
Episode # 191
She is a strategist, connector, and coach helping people and organizations thrive through change. With 20+ years of leading talent, culture, and transformation across global brands like Under Armour, Hyatt Hotels, Apollo Education Group, and the U.S. Olympic & Paralympic Committee
What Listeners Will Learn:
For most of my career, technology felt predictable.
A new software platform arrived.
A new programming language appeared.
A new cloud service changed how we deploy applications.
Every wave of technology helped people work faster.
But AI feels different.
Over the last two years, I have watched professionals across industries experience something I have never seen before.
People are not simply using a new tool.
They are having conversations with technology.
A marketer can generate campaigns.
A consultant can build frameworks.
A developer can create applications in hours instead of weeks.
And every week, the systems become smarter.
Personally, I have experienced this while building AI frameworks, experimenting with coding agents, and working with organizations trying to adopt Generative AI.
Many times I have found myself staring at a screen thinking:
“How did it do that?”
Not because the output was perfect.
But because the pace of improvement was faster than expected.
This raises an important question.
If AI is becoming more capable every month, how do we ensure we build systems that remain useful, trustworthy, and safe?
That is exactly what we explore in today’s Open Tech Talks conversation with Dr. Craig Kaplan.
Episode # 190
He is a pioneer in artificial intelligence and the inventor behind technologies designed for safe Superintelligence. For more than four decades, he has worked at the intersection of intelligent systems, ethics, and innovation, developing architectures that help AI evolve safely and remain aligned with human values.
What Listeners Will Learn:
For many years, technology projects were relatively predictable. A new system was implemented, a process was automated, or an application was modernized. The challenges were technical, but the path was usually clear.
Then Generative AI arrived.
I still remember some of the early conversations with technology leaders. Almost every discussion had the same underlying question: “How quickly can we adopt AI?” Yet very few people were asking a more important question: “Why are we adopting AI?”
Throughout my career in enterprise technology, ERP, cloud, and AI transformation, I’ve seen organizations succeed when they focus on solving real business problems. I’ve also seen companies chase trends because everyone else was doing it.
Today’s conversation reminded me that technology leadership is no longer about buying the latest tool. It’s about balancing innovation, security, business value, and human judgment.
As AI becomes part of every organization, the challenge is not whether to adopt it. The challenge is adopting it thoughtfully.
Episode # 188
Kevin Carlson is a seasoned tech exec and a go-to expert on AI’s real-world impact within businesses. He’s been a CTO or CISO four times over, working across different industries in both North America and Europe, so he brings a genuinely practical viewpoint to how AI is changing business and the world.
What Listeners Will Learn:
One thing I have realized after years of working in AI, enterprise systems, ERP, and now Generative AI, is that technology alone never changes industries.
What changes industries is understanding people.
The problem today is not a shortage of content.
There is no shortage of tools.
It is not even a shortage of AI models.
The real problem is relevance.
Why do people ignore most advertisements?
Why do customers disconnect from brands?
Why do organizations create more AI-generated content but still fail to create engagement?
Because human decision-making is emotional, contextual, irrational, and deeply personal. And that is why today’s conversation is important.
For years, the world focused on machine learning models, automation, and now Generative AI. But very few people are asking a deeper question:
Can AI actually understand human intent, context, and decision-making?
Today’s guest, Martin Lucas, has spent years exploring exactly that through deterministic AI and decision science.
And personally, this topic resonates with me deeply.
Because while building AI adoption frameworks and helping organizations modernize, I constantly see one challenge repeated everywhere:
Companies are automating communication…but not improving understanding.
They are generating more…but connecting less.
This episode is not just about AI technology.
It is about human behavior, trust, context, branding, creativity, and the future relationship between humans and intelligent systems.
Let’s dive in.
Episode # 188
He is the inventor of deterministic AI and decision science, proven across more than 100 global brands with results up to 76% above market performance.
What Listeners Will Learn:
One of the biggest shifts I’m seeing right now is not only how AI is changing work, but how it is changing the way we test ideas.
In the past, if a founder, researcher, product manager, or strategist wanted to validate an idea, the process was slow. Build a hypothesis. Run surveys. Wait for responses. Clean the data. Analyze it. Then maybe discover the question itself was not strong enough.
Now, with GenAI, that whole cycle is being challenged.
And this connects directly with my own work as well. When I work on AI strategy, GenAI maturity, or enterprise adoption roadmaps, the hardest part is often not the technology. The hardest part is asking the right question before building the solution.
That is why today’s conversation is important.
Because we are moving from AI as a content generator to AI as a thinking partner.
A system that can help researchers, founders, and teams test assumptions, explore user behavior, and sharpen decisions before spending time and money in the wrong direction.
Today, I’m joined by Sharif Amlani, who brings together political science, research methods, data analysis, and generative AI to build tools for synthetic respondents and AI-powered research analysis.
This is a conversation about research, validation, synthetic data, agents, and what happens when GenAI becomes part of the thinking process itself.
Let’s get into it.
Episode # 187
Sharif Amlani is the Founder and CEO of HumanAI, a UC Berkeley startup using generative AI to transform how we do research, analyze data, and expand what we know about the world around us.
What Listeners Will Learn:
Over the past year, something has become very clear.
AI is not just a technology shift.
It is a leadership test.
Across enterprises, startups, and even governments, the same pattern keeps repeating:
From the outside, it looks like a technology race.
But from inside organizations, it feels very different.
It feels like:
In conversations with CIOs, architects, and business leaders, one thing stands out:
The real challenge is not adopting AI.
The real challenge is leading through it.
That’s why this episode matters.
Chapter List:
00:00 Introduction to Silicon Valley Executive Academy
01:37 Understanding the Silicon Valley Playbook
03:20 The Impact of AI on Leadership
05:25 Leading Through AI Transformation
09:45 Managing Pressure as a Leader
11:21 Driving Growth with a Healthy Culture
13:39 Common Challenges for Executives
16:00 The Role of Emotional Intelligence in Leadership
17:20 Micro Joy Method for Leaders
18:58 Building Trust as a Leader
19:54 Identifying Red Flags in Leadership
21:20 Evolving Leadership Models
23:53 Advice for Emerging Leaders
Episode # 186
An executive leadership coach and strategist with over 25 years of experience in Silicon Valley’s high-tech sector. With a PhD in Psychology and an MBA from UC Berkeley.
What Listeners Will Learn:
80% of enterprise AI projects never reach production. After two decades helping enterprises adopt new technology, Kashif Manzoor breaks down the five failure modes killing enterprise AI initiatives, introduces the GenAI Maturity Framework, and shares three questions every CTO should ask before approving their next AI project.
Episode #: 185
In this episode, you’ll learn:
TIMESTAMPS:
0:00 – The POC graveyard (a real conversation)
1:30 – Welcome + Why this episode exists
3:30 – My journey: Oracle → Cloud → GenAI
7:00 – The 80% problem: Why enterprise AI fails
10:00 – Failure Mode 1: The Strategy Gap
12:30 – Failure Mode 2: The Architecture Gap
15:00 – Failure Mode 3: The Governance Gap
17:00 – Failure Mode 4: The Talent Gap
19:00 – Failure Mode 5: The Measurement Gap
21:00 – The GenAI Maturity Framework (6 levels explained)
24:00 – 3 Questions Every CTO Should Ask
26:30 – What’s coming next
28:00 – Subscribe + Connect
]]>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
What Listeners Will Learn:
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.
What Listeners Will Learn: