Topic of AI Bubble started to surface already last year and comes back in (ever increasing) frequent hops. Studies like infamous MIT study of 95% AI Failing are loading the discussion even further. So we settled down with David for discussion on where the truth might be on this Bubble issue.
To avoid being just shallow, we decomposed the total Revenue (and Profit) profiles of companies and discussed in deep detail the development around AI companies' User Bases, Intensity of Use and Frontier Labs' Pricing power.
The development in the very topic is so dynamic that next events my render these kind of talks soon irrelevant. Therefore, we invite you watch through quickly :) and to extract the measuring logic rather than just one snapshot conclusion. Enjoy!
Chapters:
01:40 Do We Have, AI Bubble Or Just Mind Games ?
04:10 M7 Taking Lion Share of Stock Market
06:25 It's Complicated, Because
08:40 AI Is Surely Getting To Big For Traditional Investors
10:10 Most Investors Are Locked Out Of Investment In AI
12:34 Reading The Recent Quarterly Results, We See ...
15:40 Profits, Not Reported, But Once Can Assume
19:54 When The Tide Goes Down, It Is Clear Whole Had Swimsuit And Who Didn't
21:02 Introducing 3 Factors: User Base + Intensity + Pricing
25:22 Positive And Negative Factors Of User Base Dynamics
28:07 Intensity of Use Is Tricky to Estimate
31:40 Wow Effects Try To Push Usage, But Stumbling On This
35:03 Free Tier Is Still Bloating Impression Of Demand
38:04 Quite Bluntly : AI Is Still Too Cheap
41:00 30USD+ Would Be Psychological Limit For Me
44:04 Bring Your Own AI To Work Might Play A Role Here
47:51 After Here All The Facts David Changes His Opinion
Having distinguished Prof. Amina A. QUTUB for interview is a dream of many Tech podcasters (or it certainly should be). As only few people have both the vision to see where AI is heading and the hands-on expertise to push those breakthroughs into the real life of clinics and hospitals.
Her work is on intersection of high-end, expert Biology and Computational Systems and even both ways: With her team she not only builds models that replicate human brain BUT ALSO uses these insights to gear "regular" AI (as we all know it from LLMs) to save lives in Emergency Rooms and help Clinicians to stay On-Top-Of-The-Game even in long shifts.
Thus, talking to someone, who's inventions can literally save your own life in future made this discussion so eye-opening and awesome at the same time. With David, we definitely took a chance to address the AI + Medicine combo from several angles and got from Dr. Qutub handful of insights nuggets.
So, go ahead & explore all chapters of this great interview:
01:50 Patterns of Communication, From Molecules to Humans
02:40 Where To Look For AI Start-Up Inspiration In Medicine
04:55 Turing Test in Medical AI Would Need to Include Smell and Taste
06:56 Combining Many Clinicians On Top Of Their Game
09:15 Accessibility Paves The AI Adoption (and hence hinders Medical AI)
11:24 In Full Emergency Room AI Split-second Decision Really Helps
16:35 Originally Deep Learning Inspired By Brain, But Where Do We Go Now
20:35 Is There Hope For Humanity To Merge Bio Brain Efficiency With AI
26:08 Building AI That Does Not Hurt Or Kill
29:34 How Do AI Scientists Choose (from ever changing) Top AI Models
32:05 Where Can/Should We Augment Humans With AI (and where to stop in it)
35:50 Launching AI Is Not Like Introducing Penicillin
38:19 Humble Self-Regulation (In Times Where Regulators Not Fully Grasp AI Principles)
41:30 AI Is Useful Even In My Private Life, But I Miss ...
44:16 What If We Invest Into Creating Super-Humans ?
Amina Ann Qutub, PhD is the Burzik Professor of Engineering Design and Associate Professor of Biomedical Engineering at the University of Texas, San Antonio. She serves as the Assistant Director of Strategic Alliances for the MATRIX AI Consortium and a research thrust lead for the Augmenting Human Performance thrust. Dr. Qutub is also the Director of the UTSA – UT Health San Antonio Graduate Group in Biomedical Engineering and co-lead of the Center for Precision Medicine. Dr. Qutub is pioneering methods at the interface of computer science, biology and engineering to study the design of human cells, and help eradicate diseases affecting cells of the brain and vasculature. In new translational work, Dr. Qutub is co-lead (with Drs. Brian Eastridge, MD, UTHSCSA and Alan Cook, MD, UTTyler) of the iRemedyACT project to develop AI tools that can minimize time to treatment and optimize care for trauma patients.
After years of consuming the AI mainly through chatbots and custom applications, finally we AI makes itself available also in form of the Web/AI Browsers. But that is where the story only starts, as it opens plethora of new questions:
All those topics and more you can hear from the recent episode of the www.maindsetpodcast.com
We started 2025 year with mAIndset podcast episode "2025 AI Surprises You Are Not Ready For" outlining prediction about where AI will move in next 12 months. As we are after H1 results of all key players, let's look back on what predictions are turning true and where trends go differently as originally anticipated.
This time a special treat for our #mAIndset podcast where David and Filip debate together the ideas from thought-provoking article "AI Will Hit Me Before I Turn 50". And, boy, exchanged the ideas are.
From Future of Universities to Why buy your own GPU all the way to How to Improve your work chances in post-AI era. Highly recommend to all those wanting to have nuanced understanding what to expect. If you are 40 year (or younger) don\t miss this out. Original article "AI Will Hit Me Before I Turn 50" can be read in full here:https://mocnedata.sk/en/artificial-intelligence-will-hit-me-before-i-turn-50/Chapters of Video:01:47 Form The 43rd to 50th Birthday03:25 Will David Amass Enough Funds For Early Retirement?05:35 Airplane analogy08:37 How People React on "AI will hit me ... " Text?12:16 Want To Make It Personal, It's Going To Hit Me15:25 Sam Altman's Children Not The Smartest Anymore17:45 Human 1st Task: Swallow AI Dominance20:45 Getting Yourself Ready for AI JOB Replacement24:35 Craftsmen Jobs - Not Secure Plan B, Really27:05 Future of Universities and Education32:10 So Are We In Flood Prevention Scenario?37:44 The only Remaining Barrier to AI Is ....38:50 I Was Also Guilty Of The This Issue40:20 Skills To Learn To Improve Your Post-AI Chances42:41 GPU What is Real Estate Of AI Aera ?!
We have seen Mark Zuckerberg open his AI wallet wide. Scale AI acquisition, hiring through sign-up bonuses surpassing Tim Cook's annual salary, struggle to get the Llama 4.0 out and reclaim the Open Source trajectory.
What are the real motives behind Zuck's rush to throw billions on Meta's AI progress?
Chapters
00:00 Introduction to AI Trends
01:23 Meta's AI Ambitions and Challenges
10:30 Zuckerberg's Strategic Moves in AI
18:13 Acquisition of Scale AI: Implications
27:10 The $100 Million Sign-Up Bonuses
38:35 Legacy and Future of Meta's AI Strategy
In this episode of the mAIndset podcast, David and Filip delve into the Model Communication Protocol (MCP), exploring its significance in the AI landscape, its comparison to APIs, and the future implications for AI agents. They discuss the need for MCP, its role in facilitating communication between different AI models, and the potential alternatives that may emerge. The conversation also touches on the challenges of value exchange within the MCP framework and the broader implications for the development of agent AI.
Takeaways
Chapters:
00:00 Why MCP is The Thing02:51 Understanding the Use-cases for MCP05:57 MCP vs. API: A Deeper Look09:07 Likely Future of MCP and Value Exchange12:09 What If You Don't Want MCP: Landscape of MCP's Alternatives15:00 How MCP Is Forming The Agentic AI Progress17:54 Conclusion and Two Important Signals This MCP Craze Yields
In recent episode, David and Filip discuss the implications of using AI in the workplace without informing superiors. They explore reasons employees choose to use AI secretly first place, the ethical considerations and the impact of company policies on AI usage.
The conversation also delves into the aftermath of employees' decisions to use AI, the potential risks involved and provide guide on if you choose to use secretly Ai in your work, which use-cases are Ok and which to avoid.
Properly understanding the risks associated with individual AI use-cases (across summarizing documents, drafting emails, coding and handling sensitive data) arms you better to avoid the biggest back-clash or even retaliation of your company. Key Takeaways:Using AI without consent raises ethical concerns.Many companies lack clear policies on AI usage.Employees often use AI to enhance productivity.Cost is a significant factor in AI adoption.Legal and security issues are prevalent in AI use.Employees may use AI to manage multiple jobs.AI can help bridge knowledge gaps for employees.Transparency about AI use can build trust.The trend of 'bring your own AI' is growing.AI tools can significantly improve work efficiency. Summarizing internal documents can be risky due to sensitive data.Using AI for drafting emails is generally safe but requires careful review.Medium risk use cases include creating spreadsheets and translations.High risk scenarios involve summarizing sensitive data and coding.AI-generated content can misrepresent the user's capabilities.Employee evaluations may become unfair if AI usage is not disclosed.Companies may face liability for AI-generated errors.Sensitive information should not be processed by external AI tools.AI can streamline tasks but requires human oversight.Communication about AI usage is crucial for transparency.Notable Sound Bites:"I'm telling ChatGPT, but I'm not telling my boss""Using AI without the consent is a shady area""78% of users bring their own AI tools to work""More than half of companies do not ha AI policy""Two thirds of candidates use AI already during interviews""Most of the Workplace AI use-cases are kind of okayish.""The risks of using AI (even secretly) are manageable""Best Advice : Consult before using AI at work"
Chapters
00:00 Introduction to AI in the Workplace
03:05 The Ethics of Using AI Secretly
05:56 Reasons Employees Use AI Without Consent
09:07 The Impact of Company Policies on AI Usage
12:10 The Cost of AI and Employee Choices
15:00 Legal and Security Concerns in AI Usage
17:56 Employee Motivations for Using AI
21:04 The Dangers of Misusing AI
23:54 Evaluating AI Use Cases in the Workplace
28:28 Summarizing Internal Documents and Meeting Notes
34:31 Medium Risk AI Use Cases
44:10 High Risk AI Use Cases
51:13 Understanding the Risks of AI Usage
In this episode of mAIndset podcast, we dive into Apple’s recent stumble in the AI race — a company known for polished products now facing stark criticism for what some call a glaring intelligence gap. Is this just a minor, though awkward, misstep on their path to AI relevance? Or has Apple committed a deadly sin that could cost them their innovation halo? Shall the Siri Brand survive and what would be alternative approaches? What is the underlying reason for where apple is now with its AI ?David and Filip unpack it all in debate of blunder, the backlash, and what it signals for Apple’s future in the intelligence era.00:45 What Went Wrong 02:00 WWDS - False Hope Only ?!04:10 Is Umbrella The Problem Here06:12 Language Limitations On Top09:05 Hard Dilemma: Shall Apple Build or License11:50 May The Fish-head Stink As Well? (Tim Cook's part of blame)13:05 Apple Car First Warning19:12 Sorry, This Is Going To Get Even Worse (before it get's better)23:05 What Use-cases Will Really Happen In Apple AI26:10 Is The Company In Big trouble ?!28:02 Too Much Apple, Too Little Intelligence29:28 Siri Is Dead Brand To Be Kept Alive32:30 What Would You Do, Filip, To Solve Apple Intelligence ?34:15 Too Late For A1 Model36:10 What To Expect In 2025 and 2026 from Apple AI37:25 Surviving As Black Passenger Is The Next Apple's Move40:31 What Next mAindset Will Be About
The 6th EPISODE of #maindsetpodcast podcast is dedicated to role of AI In Hiring Process. Davis was lately in between jobs, I am hiring continously new colleagues, so this discussion on where AI can, should and should not be used by either side of the hiring table, comes really handy not just to two of us, but to anybody on job market. In the episode you will hear on which tools are most useful for candidates, where you as candidate should still hold your horses to engage AI in your job seeking. We feature also which tools are popping on the Employer side and (for some) surprising result of who is winning the "AI Battle in hiring process". Enjoy the full episode for free and leave us a comment if you had similar (or vastly different) experience with Ai in the job seeking.Topic was picked for recording as upvoted theme from our suggestion list. Go and suggest your own topic here as well:https://poll.ly/lifUgkaHpwrX9128yJlW
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Send us your questions, feedback and topic suggestions at ideas@maindsetpodcast.com
Find more info at https://maindsetpodcast.com
-Detail content of today's episode; 00:01:30 How Would David Use AI in his Job Hunt [before getting clever today]00:03:45 The 2 Biggest Myths of AI in Hiring00:07:15 How Do You Validate Potential Employee in 21st century00:08:50 AI In Early Steps of Job Seeking00:13:15 Underutilized AI In Profiling Yourself As Candidate00:16:20 LLMs Helping to Be Relevant00:20:10 Surprising, New Role Of Cover Letter00:22:50 Polishing the CV to Stand Out00:26:45 Using Voice Assistant To Cheat On Interview00:30:08 Don't Use AI For The Number Game for CVs00:33:55 Getting Self Ready For Interview With AI Help00:36:05 Turning To Employer Side, How AI Helps Here00:41:00 Can AI Lead Interview00:42:07 Filip's Own Experience With AI leading His Job Interview
You are watching (or listening to) the next episode of the #mAIndsetpodcast: Shaping What You Know And Think About AI.
In this episode of the Mindset AI podcast, we discuss the hype and reality of AI agents. Are they truly the next big thing in AI, or are they simply overpromised and underdelivered?
We explore the challenges of developing AI agents, including the chicken-and-egg problem of creating a market for specialized AI tools.
We also look ahead to the future of AI agents, discussing their potential integration into robotics and ambient computing. Finally, we consider the regulatory landscape and what it means for the future of AI agents.
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Send us your questions, feedback and topic suggestions at ideas@maindsetpodcast.com
Find more info at https://maindsetpodcast.com
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In this episode (chapters):
00:00 Introduction
00:35 The Hype
01:12 The Reality
10:52 The Chicken and Egg Problem
13:28 Overpromising and Underdelivering
20:21 The Importance of Integration
25:52 The Apple Vision
31:56 The Cost of AI Agents
38:09 The Future of AI Agents
46:25 Conclusion
You are watching (or listening to) the next episode of the#mAIndsetpodcast: Shaping What You Know And Think About AI.
In this episode, David and Filip discussed the current state of the AI market and especially looked at OpenAI, its current announcements like Deep Research or the latest reasoning models and tried to explained whether the company is still the market leader everyone seems to believe it is.
The mAIndset podcast is hosted by David Tvrdon, a technology journalist, entrepreneur, and media strategist, and Filip Vitek, an AI executive. Authors were looking for a practical podcast on AI and after missing one for a long ended up creating one instead.
Send us your questions, feedback and topic suggestions atideas@maindsetpodcast.com
Find more info athttps://maindsetpodcast.com
In this episode (chapters):
00:00 Introduction
00:43 Is OpenAI Still the Leader?
03:21 SWOT Analysis of OpenAI
09:37 Deep Research
14:21 Reasoning Models
25:35 OpenAI's Strategy for the Future
27:42 Will OpenAI be Profitable?
31:44 AGI
36:27 OpenAI's Strategy for the Future
39:54 Other Players
42:53 Apple and Amazon Have the Biggest Potential
44:57 Conclusion
You are watching (or listening to) the next episode of the #mAIndsetpodcast: Shaping What You Know And Think About AI.
In this episode, your hosts David and Filip take open-source in AI for a spin. We all know Open source is used in Software or application development. But what does it really mean to be open-source in AI? Is Open-source at par with or inferior to commercial alternatives? What will Meta’s future strategy be with Llama?
What is the market share of Open source on total AI development? For which developers does it payoff to be open-source heavy? How will be Open-source used in military and other touchy areas? The mAIndset podcast is hosted by David Tvrdon, a technology journalist, entrepreneur, and media strategist, and Filip Vitek, an AI executive. Authors were looking for a practical podcast on AI and after missing one for a long ended up creating one instead.
Send us your questions, feedback and topic suggestions at ideas@maindsetpodcast.com
Find more info at https://maindsetpodcast.com
You are listening to the first episode of the mAIndset podcast.
Send us your questions, feedback and topic suggestions at ideas@maindsetpodcast.com
Find more info at https://maindsetpodcast.com
