In a world where AI tools can write code, design products, and debug at scale, the bigger story is human. Ari Block opens up about growing up with dyslexia and never seeing himself as a writer until AI removed that barrier. From that very personal starting point, this conversation explores what happens when anyone can build software, how skills and careers will shift, and why creativity, math literacy, and ethics matter more than ever.

 

Is SaaS about to collapse or simply evolve. Will engineering jobs vanish or multiply into new kinds of problem solvers. How should schools and parents teach the next generation to use AI responsibly while still developing critical thinking and real world grit. Ari brings a candid, practical lens from the front lines of product and AI, turning fear into a plan.

 

What you will learn:

 

– How AI removes barriers like dyslexia and unlocks creativity

– Why speed matters and how tech timelines are compressing

– The real risks in security and governance and how to mitigate them

– Will SaaS collapse or morph and how to spot each scenario

– Why non technical builders are thriving with AI and no code tools

– What skills still matter most math, systems thinking, ethics, customer focus

– How education can adapt without losing writing and critical thinking

– A simple path to stay valuable as an engineer or product leader in the AI era

 

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Spotify: https://open.spotify.com/show/1zggDnblvD9Pa0Nn37o1TD?si=JVvZfm5rTryxJZh6nlO6yQ

 

CONNECT WITH ARI BLOCK:

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https://www.youtube.com/ ⁨@storysamurai⁩  

 

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Chapters:

00:00 – How AI removed Ari’s dyslexia barrier

00:36 – Are engineers about to get fired

01:04 – Why engineering felt like magic and fear pushed Ari into AI

02:03 – The job risk dilemma for today’s engineers and parents

03:34 – Math as the secret language of problem solving

04:02 – Fanatics vs resistors: two camps in AI adoption

04:28 – Tech acceleration: why predictions keep arriving early

05:24 – What if it works in three months reframing the future

05:44 – Will SaaS collapse or simply evolve

06:48 – Instant software creation with no-code tools

07:29 – Barriers to entry are shifting for business

08:20 – Startups scaling in months and billion-dollar exits

09:07 – Security and compliance challenges in AI software

10:10 – From science to engineering: solving hallucinations

11:28 – Oversupply of engineers and the job market shock

12:04 – Why universities are failing to prepare students

13:04 – Non-technical founders thriving with AI products

14:00 – A second renaissance for creativity and the arts

16:19 – Removing personal barriers with AI voice and text

18:24 – Writing style debates, plagiarism, and AI ethics

21:04 – Who wins in the new world: mindset over model

22:51 – Giants, antitrust, and the role of regulation

24:00 – Product trust issues and hallucinations in support

25:37 – AI passes the Turing test and what that means

27:51 – Don’t automate yourself out of growth

29:07 – Education’s future: beyond degrees and into skills

30:11 – AI for firefighters and local community training

31:15 – Teaching kids law, commerce, and durable skills

33:55 – Why joy, creativity, and human experience still matter

36:37 – Final advice: study math, learn neural nets, stay curious

37:53 – Closing thoughts and where to connect with Ari

 

Melissa

(0:00) Are all engineers about to get fired? (0:05) AI tools like Cursor and no-code platforms are putting serious power into the hands of anybody with an idea. (0:13) So what happens when anyone can build software?

(0:17) Will SaaS collapse? (0:18) Will IT departments disappear or multiply? (0:22) Today on the Executive Connect podcast, we’re diving into the big questions with Ari Block, a product strategist and engineer who’s not just watching the tech shift, he’s engineering it.

(0:35) Welcome, Ari.

Ari

(0:37) Thank you so much for having me on your show today, Melissa. (0:40) Great to be here.

Melissa

(0:42) Now you’ve been on the front lines of some of the biggest game-changing innovations in tech, from manufacturing to product pivots, that’s all reshaping our future. (0:55) Talk to me a little bit about what drove you to jump into this industry and work in engineering and strategy?

Ari

(1:04) Well, the original thing is just this fascination with what I in essence saw from a very early age as magic. (1:14) Engineering software, to me, it was just magic. (1:18) And what a most wonderful thing to understand how magic works.

(1:22) So that’s what drove me. (1:23) What drove me into AI is pure fear. (1:30) 100%.

Melissa

(1:31) I love it. (1:33) That’s such a good answer. (1:35) Fear.

(1:35) Yeah, me too. (1:36) That seems to be the through line these days with everybody. (1:41) Is my job going away?

(1:44) Should I be using AI? (1:46) Am I replaced with my job? (1:48) Talk to me.

(1:49) Let’s jump right into it. (1:50) AI tools, writing code, debugging, building apps. (1:55) Who actually needs engineers anymore these days as it relates to AI?

Ari

(2:03) Your audience must know that both of us are engineers. (2:07) So we here are on the front line of basically losing our jobs, and that’s the fear that was driving me. (2:14) I’ve been trying to untangle, is my job going to be in risk?

(2:19) And if I’m pushing my son to learn engineering, is that a stupid decision on my part? (2:24) So there’s this idea that I fear for myself. (2:27) There’s this idea that I fear for my children.

(2:29) What’s the future going to look like? (2:31) And what that’s done, it’s driven me to be like, okay, what does the future look like?

Melissa

(2:37) Yeah. (2:37) And it’s an interesting conundrum. (2:40) Let’s just unpack that a little bit.

(2:42) I think for me, one of the biggest blessings of getting an engineering degree, despite the degree, was learning how to think. (2:50) I think that’s my favorite part of the degree. (2:54) I feel like math is a language in and of itself and being able to solve problems.

(3:00) I know for me, I’m not an electrical engineer, but during the pandemic, when we had our microwave, our stove, our dishwasher all go out because of a power surge, I was able to figure it out myself. (3:17) And I don’t know, had I not got that engineering degree, if I would have A, had the confidence to do it, or B, even wanted to do it. (3:26) So I think the beautiful thing about engineering, despite what happens to the engineers, is it teaches you how to solve your own problems.

Ari

(3:34) That’s right. (3:35) And what you said is absolutely brilliant because I think math is the secret to this. (3:40) So if you’re kind of, there’s two camps right now.

(3:43) There’s the AI fanatics that are learning all about it. (3:48) And then there’s the resistors who are basically like, no, I just like coding or I just like my job and I don’t want to do anything and I don’t want to touch AI. (3:58) So we’re seeing these extremes right now in the market.

(4:02) But what there is, is a lot of lack of knowledge. (4:05) And then both sides of the camp are right. (4:08) Now, why is that true?

(4:10) You know, when I talk to engineers, they’re like, oh, I used this, it didn’t work. (4:14) True. (4:15) It doesn’t work.

(4:17) Then you talk to the fanatics and they’re like, oh my God, look how much AI has progressed. (4:22) The speed that this technology is accelerating is faster than any technology ever in the history of mankind. (4:28) Now, that’s a huge flag.

(4:31) So what that means is that all of my predictions, I’ve been predicting tech for 20, 30 years. (4:37) I’ve always been right. (4:38) I’m pretty damn close on the timing aspect.

(4:41) I’m consistently wrong now. (4:43) Not on what’s going to happen, but when. (4:45) So I made a prediction, it was six months ago, oh, this will happen in a year or two.

(4:50) Happened three months later. (4:52) So that fundamentally shocked me that I don’t know to predict when things are going to happen and they’re happening five, 10, 20 times faster. (5:01) So my question to the laggards, right?

(5:04) Sure. (5:05) It doesn’t work now, but what if it worked in three months? (5:09) Like, are you really sure that it’s going to be 10 or 20 years before it works?

(5:13) And what does that world look like once it works? (5:17) And, you know, we’ve been thinking through what that world would look like. (5:20) It’s kind of terrifying.

Melissa

(5:24) Yeah. (5:24) And I think it poses a question, will SaaS collapse or will it evolve? (5:29) There’s a lot of very expensive SaaS software that people are using.

(5:33) So that’s the big, bold question is if teams can build their own tools, will they pay for SaaS? (5:41) So let’s get, you know, let’s unpack that. (5:44) Is this a beginning or an end to the B2B software world as we know it?

Ari

(5:49) This is a tough one because my key thinker, my partner in crime for all technological futurism, which nowadays is not 10 years or 20 years, it’s three months out. (5:59) So my key technological thinker and I, Max, we are at odds. (6:04) We don’t agree on what’s going to happen.

(6:06) I have said that SaaS, specifically the B2B model, where you’re selling into a platform of services, right? (6:15) So the Mondays of the world, right? (6:17) I said those are going to collapse.

(6:19) He’s like, well, Ari, no, I think they’re going to morph into something else. (6:23) And I think to a certain degree, he’s right. (6:25) So let’s walk through both of these scenarios, because I think the key thing in helping the audience try to predict the future, which is going to be critical for their own success, each and every one of them, they need to understand not what is the right answer, but what are the possible scenarios and how to kind of judge at each moment, which one of those scenarios is about to happen.

(6:44) So here’s the scenario, which I believed in, which I think I’m wrong to a large degree. (6:48) SaaS companies are going to collapse. (6:50) Now, why is that?

(6:52) We see software like Lovable, Cursor, and many others, basically building software instantly. (6:59) So I could literally write a prompt and create a risk repository, which would replace what Drata and other Vanta and other security companies have worked on for years, right? (7:10) So I could literally create a piece of software that Drata created in years.

(7:15) I can do that in minutes, right now, I could do a live demo. (7:19) Now, that’s concerning, because if that’s true, there is no barrier to entry on software. (7:26) So if we think about the fundamental barriers for business, what are they, right?

(7:29) You have a base of customers, you have money, capital is a barrier to entry, you have sticky usage of your platform, you have data that nobody else has. (7:39) And oh, it took you three, four, five, 10 years to build your software. (7:42) What if that one thing, it took you 10 years to build your software, Salesforce, Vanta, Drata, what if that suddenly became untrue?

(7:52) And that’s the revolution that’s happening, is that one of the really the founding theories behind how business works is changing.

Melissa

(8:03) Yeah, I agree. (8:04) I think in my industry, so many companies build their own products themselves, and it takes them months, years, multiple years to get the product working and functional and friendly. (8:20) But I think it’s different now.

(8:22) I feel like a lot of the companies that are smaller, that didn’t have the capital that you mentioned, that are starting with a small team, they’re scaling so quick. (8:33) Like you were saying, what we thought was going to take two years is taking three months.

Ari

(8:38) So talk to me- Mind-boggling. (8:45) How do you exit a company for millions of dollars in nine months? (8:49) That’s ridiculous.

Melissa

(8:50) A hundred percent. (8:51) And we’re seeing it, I’m seeing it in the regulated industries I work into. (8:57) And I’m curious to get your perspective on using the security control lens.

(9:07) How is that looking as AI tools are evolving?

Ari

(9:10) Yeah. (9:11) So look, here’s the human thing we do, right? (9:16) We fear something, we try to shut it down.

(9:18) So Italy shut AI down, shut chatGPT down for about two to three weeks. (9:23) And they’re like, whoops, we can’t do that because there was a huge outcry, and they understood that basically they’re shooting the self in the leg, if not in the head. (9:31) So our first human reaction is legislation.

(9:34) It’s hold on, let’s put the brakes on this. (9:36) Now, there is a version of legislation that makes sense. (9:39) And we can talk about that as it pertains to basically creative rights.

(9:45) But the problem is that it takes government a lot of time to get to that. (9:50) So that’s the first fear. (9:51) Oh, let’s shut it down.

(9:52) That won’t work. (9:53) Take government a long time to figure out how to manage it and regulate it. (9:57) That’s going to take a while.

(9:58) A lot of damage is going to happen until governments figure it out. (10:01) Honestly, that’s how governments figure things out, through seeing the damage, right? (10:05) That’s how they react.

(10:06) Now, on the other hand, there’s the security of it, right? (10:10) So we’re talking about, oh, like somebody does something stupid, like putting keys into their prompt, now the keys are available, or the GPT that’s creating the software is putting database keys into the code because it didn’t do the security correctly. (10:24) So it kind of gets really scary.

(10:26) Like, how do we manage AI software to be secure, to be scalable, to be deployable, to be human? (10:33) But what we need to understand is kind of this story about teleportation. (10:37) And one of the famous sayings is that, and I can’t remember who said it, but the saying was, oh, we can now teleport one particle from the end of one lake to the other end of the lake.

(10:46) It’s possible. (10:47) This is possible now. (10:49) So this guy, very famous, I can’t remember his name, said, well, I no longer care about teleportation because now it’s not a scientific challenge.

(10:56) It’s an engineering problem. (10:59) And that exact saying is true for AI now. (11:02) All of these problems that the laggards are saying, oh, it doesn’t work.

(11:05) They’re just an engineering problem. (11:07) In fact, I could talk through architectures to solve each and every one of them. (11:11) And that’s why we are on the forefront of AI, because we’re thinking about these solutions, we’re testing them, we’re implementing them, and that’s what’s going to make the difference.

(11:19) So it’s just an engineering problem. (11:20) Now, once these engineering problems are solved, that means security, scalability, reliability. (11:27) What happens then?

(11:28) Now, one idea is that the supply of engineers will be significantly larger than demand. (11:37) Now, that’s a problem, because what we’re already seeing today is students finishing their CS degree that can’t find a job, because AI is smarter than a student that just finished their degree. (11:49) Now, we can go down two paths.

(11:51) We can say, well, what’s going to happen to the people who are experts? (11:53) That’s one question. (11:54) Another question, which I think is rather interesting, what’s going to happen to the universities if they’re churning out students that can’t do a job?

Speaker 2

(12:04) Yeah, that’s a good point. (12:06) And it’s interesting that you mentioned that, because I know a couple engineering students that are now waiting tables because they could not find top of their class at a very good college, and they’re having a hard time finding a degree. (12:22) I know when I came out of engineering school, it was very easy for me to get an engineering degree.

(12:27) There were multiple job offers. (12:30) I just had to pick one. (12:31) And so I think you’re spot on that even people that are coming out with an AI degree thinking that they’re going to get better jobs, more pay, because now they have a PhD in AI.

(12:46) I didn’t even know that there were that many people doing PhDs in AI. (12:51) But it’s interesting to think about that. (12:54) And I often wonder myself, I see a lot of people creating AI products that have zero technical background.

(13:04) They are non-technical. (13:07) They might even be arts people, but they’re creating very successful AI products. (13:14) Talk to me a little bit about that shift, because usually if you’re a technical person.

Ari

(13:20) So what’s happening is that the engineer’s value is being diminished, right? (13:25) So there’s a huge contraction in demand for engineers. (13:29) The supply for engineers is just churning right now until supply and demand basically adjust, right?

(13:34) That supply and demand laws, they kind of work out in the long run, not the short run. (13:38) My professor, right, the father of efficient markets, that’s his whole thing. (13:43) So Nobel laureate, Professor Fama.

(13:47) Okay. (13:48) So that’s going to happen. (13:49) But will overall demand for innovation change?

(13:56) Now, it’s not going to decrease. (13:58) I don’t think so. (13:59) Will it increase?

(14:00) I don’t know. (14:01) I think it’s at least going to stay the same if not increase. (14:04) Why?

(14:05) Because AI is going to empower us to build these amazing things. (14:09) And it’s going to open up doors and problems that we can solve. (14:11) It’s actually going to open up more problems that can be solved.

(14:15) So I think actually demand for innovation is going to grow. (14:19) However, demand for engineers is going to reduce. (14:22) What’s going to grow is demand for anybody who wants to solve a problem.

(14:26) Now, if supply of engineers doesn’t matter, because now it’s not supply of engineers, it’s supply of anybody. (14:33) So my daughter, she is definitely an artsy fartsy person. (14:37) She’s publishing a book of poems.

(14:39) And she’s publishing another book, which is basically a cartoon about this kiwi bird trying to save the world on the way burning down stuff, right? (14:49) So they’re classical. (14:51) Yeah, she’s brilliant.

(14:52) And she’s only 11. (14:53) So I think that the artsy fartsy of the world are going to go through this basically second Renaissance. (15:00) I think they’re going to be leading the charge.

(15:02) I was talking to a very famous now retired reporter the other day. (15:07) She was on TV a whole lot of time, many, many, many times for years, nine years. (15:11) She’s written six books.

(15:13) And she was depressed. (15:14) And I was like, no, no, no, no. (15:16) We’re looking at a second Renaissance for the arts, and people who are thinkers and people who are creative.

(15:24) So if you’re a creative, and you’re like, oh, Chachapiti can write my copy. (15:29) No, sure. (15:30) If all you can do is write copy, yeah, you’re in trouble.

(15:33) But if you’re truly creative, and you’re truly brilliant, and you’re hardworking, this is going to be the best 10 years of your life, you know, barring the world messing it up.

Melissa

(15:44) It’s so true. (15:46) And I think about, you know, being a math and science kind of gal, I am, I do consider myself a creative. (15:53) But the beautiful thing about these tools, all these AI tools is my creativity level has gone through the roof.

(16:00) And it’s not something that was second nature to me. (16:05) It’s something that I have been developing and getting better. (16:08) And I’m able to leverage both sides of my brain, the math and science and the artsy fartsy through some of these tools, which is so splendid.

Ari

(16:19) Yes, that’s right. (16:20) And that’s why there’s hope. (16:21) Because what’s happening is that each of us have barriers, right?

(16:25) So I’m dyslexic, I can’t spell, that doesn’t matter anymore, because Chachapiti can fix my spelling better than Grammarly could. (16:32) And I paid for Grammarly for years. (16:34) But now I’ve got a free tool.

(16:36) Sure, I’m paying a license, but it costs the same as Grammarly, but it does so much more. (16:41) So, but the concept is that you’re removing people’s barriers. (16:44) Now, if you’re removing their barriers, they can get to that next level.

(16:47) Now, what is that next level? (16:49) So in some way, right, of course, there’s a dark side to this. (16:53) And we started from the dark side.

(16:54) And there’s even a darker side that we didn’t get to. (16:56) But the, you know, the light side of this, and everything’s yin and yang, is that we’re unlocking human potential, because these barriers that we have that doesn’t fucking matter. (17:06) Sorry, I don’t know if we can curse on your show or not.

(17:08) But it doesn’t matter that, you know, I’m dyslexic, it doesn’t matter that I can’t spell. (17:14) But in the past five years ago, that would prevent me from being creative, that would prevent me from being a writer. (17:20) Today, I can be a writer, because I can I literally use this prompt, this is like, probably like 95% of the prompts that I write.

(17:26) Fix this for spelling. (17:27) That’s it.

Melissa

(17:30) I love that you just said that, because similar, I was so excited when I graduated, I could use word profession, like, and now the beautiful thing about that is we can talk into chat GPT. (17:41) So we don’t even have to type of something that we don’t like, we think fast. (17:46) And the tool can keep up with our thinking.

(17:49) So I’ve pivoted Ari from the typing and misspelling and run on sentences and 80 different ideas in one paragraph to talking to GPT. (17:58) And it can output exactly what I’m doing. (18:01) It’s like, like this, it’s a whole, I think back of what held me back before, how much longer I had to work to get my writing where I need it.

(18:12) And now like you were just saying, instantaneously, I can have correct grammar, correct punctuation, you know, remove the em dashes, because I’m not sure how I feel about all these em dashes. (18:24) Maybe you can clue me in on like, is the em dash, like I was just told the other day, if you use an em dash, everybody knows it’s chat GPT. (18:33) What’s the story on the em dashes?

Ari

(18:35) Lord. (18:36) I didn’t know that this is a very tense topic on LinkedIn and probably other places as well. (18:44) We’re on 56 different types of media, but only one of them I actually, we don’t basically publish automatically.

(18:51) So there’s only one that I’m actually on and that’s LinkedIn. (18:54) And LinkedIn, it’s a heated debate. (18:57) You know, the dash people, I think they’re laggards because the dash people like, oh, you use AI, that’s evil.

(19:03) So they’re laggards because they’re basically not seeing the potential of the technology. (19:08) Now there’s the people who are using AI, but don’t want anybody to know like me, right? (19:12) So we’re deleting the dashes, but I’ll say it here and now I’m using AI to fix my spelling.

(19:18) Heck yeah, I’m dyslexic. (19:20) So why wouldn’t I? (19:21) But I don’t want that perception that AI wrote this for me, right?

(19:25) That’s plagiarism. (19:27) That should be illegal, but it’s going to take governments to figure out what is plagiarism with AI. (19:33) And then you’ve got the crazy people that are like, oh, anything AI writes is fine.

(19:37) Like, no, if I write, and I just spoke to an author the other day, and she’s like, people can literally go and say, write a book on this topic in the voice of using ideas of X. (19:49) And we’ll do it. (19:50) And the author can read it, and she’ll be like, holy cow, this is my stuff.

(19:55) This is my tone. (19:56) This is my voice. (19:57) This is my ideas.

(19:58) But legally, that’s not plagiarism right now. (20:01) So I think there’s a problem here, and government will have to catch up. (20:05) Like, what is legal?

(20:06) What is illegal? (20:07) Right now it’s all legal. (20:09) You know, ChatGPT, everyone, Anthropics, they all stole a whole bunch of data.

(20:12) Is that legal? (20:14) When somebody stole a whole bunch of data, they got put in bars when it was music. (20:19) Music industry is pretty good about keeping their data.

(20:22) But when it was basically every website in the world, nothing happened to them. (20:28) It’s fine. (20:29) So is stealing now fine?

(20:30) I don’t know. (20:31) Like, the world is becoming weird.

Melissa

(20:34) It’s very true. (20:35) I had a friend of mine’s daughter use MDASH in her English paper, and they gave her an F because they said it was written by AI. (20:46) But it wasn’t written by AI.

(20:50) Right. (20:51) And so it’s an interesting time indeed. (20:54) So thanks for clearing that up.

(20:56) I think we’re on the same page with the MDASHes.

Ari

(20:59) It’s going to be weird.

Melissa

(21:00) I don’t know what’s going to be weird.

Ari

(21:01) It’s going to be weird.

Melissa

(21:03) It’s going to be weird. (21:04) Okay. (21:04) Let’s talk about the winners in the new world of AI.

(21:09) I think we kind of alluded to some of it. (21:12) So who is going to benefit from all this disruption? (21:17) I know I have so much content thrown in my inbox and online.

(21:23) I’m like content overload right now in the world. (21:28) So do startups get the upper hand to big, expensive SaaS software?

Ari

(21:34) Not necessarily.

Melissa

(21:35) Okay. (21:36) So tell me.

Ari

(21:38) The answer is that… (21:39) Okay. (21:39) So here’s the answer.

(21:42) Do you think you’re dumb? (21:44) If the answer is yes, you think you’re dumb. (21:46) You’re right.

(21:46) You are dumb. (21:47) If the answer is no, you don’t think you’re dumb. (21:49) You’re right.

(21:50) You aren’t dumb. (21:51) And why is that true? (21:52) If you think you’re dumb, you’re not going to embedder yourself.

(21:56) You’re not going to read books. (21:57) You’re not going to be smarter. (21:58) You’re not going to challenge yourself.

(21:59) You’re just going to be like, oh, I’m just dumb. (22:01) If you think you’re smart, then you’re going to seek out your mistakes. (22:05) You’re going to read books.

(22:05) You’re going to be like, oh, I need to maintain my self image of myself that I am in fact smart. (22:11) Right? (22:11) So there’s this ongoing fight with oneself to perpetuate a belief.

(22:16) Now, why is this relevant? (22:17) Why is Ari bringing in Daniel Kahneman and Amos Traversky? (22:20) Why are we talking about social psychology and biases?

(22:24) The simple reason is that the winners are those people who believe that they will be the winners. (22:30) And the losers are those people, large, small companies, B2B, SaaS, whatever, that believe they’re going to lose. (22:37) So why is that?

(22:40) You know what? (22:41) Let’s make this interesting. (22:42) You pick anybody, and I’ll explain how they could win or lose, from SaaS companies to firefighters to anything.

(22:48) You pick a thing, and I will talk through it.

Melissa

(22:51) How about Microsoft and their very expensive SaaS software?

Ari

(22:56) So the only people that I should probably exclude from this is the winners who have won already. (23:04) So it’s kind of clear that the Anthropx, the GPTs, the Microsoft, that’s going to become a problem in the future, where government comes back and is like, these guys are so, so powerful and strong and rich. (23:18) How do we break them apart?

(23:20) So basically, at some stage, we’re going to have a big government agency say, you guys need to split into two, three, four, five, 10 companies, because this is not cool. (23:29) So they’re going to be the winners for a lot of years. (23:32) Absolutely.

(23:33) The moment that they’re going to lose, to a degree, right, is when government breaks them apart. (23:38) So that’s going to happen, I have no doubt in mind. (23:40) I hope I have no doubt in my mind, because if government can’t do that, maybe government doesn’t have the power it needs.

(23:46) So that’s a scary thought. (23:48) So I’m afraid those companies are going to be winners for sure. (23:54) I don’t know who exactly.

(23:56) So for example, Cursor. (24:00) A weird thing happened to me in Cursor. (24:01) It told me that it couldn’t do terminal commands.

(24:05) So I asked Cursor, why can’t you do terminal commands? (24:08) You could do it before. (24:10) And then it said to me, oh, we shut down that functionality and wrote a beautiful explanation on why they did it and what’s possible and what’s not possible.

(24:16) I closed my account with Cursor, because I’m like, you guys just shot yourself in the foot, if not the head. (24:21) Support reaches out to me and says, oh, why did you close your account? (24:26) Politely.

(24:27) And I’m like, well, you turned off terminal. (24:28) So bye-bye. (24:29) And they’re like, we didn’t turn off terminal.

(24:32) And then I send them the information. (24:35) And then their own system told me that it did not have the functionality, would not do the functionality. (24:40) And then support is telling me that’s a hallucination.

(24:44) So their own system was telling me it can’t do something. (24:48) And then support is saying, no, that’s an hallucination. (24:51) Your configuration, something went wrong there.

(24:53) You need to update a configuration. (24:55) So who’s telling me the truth? (24:57) The system I’m using or the support person who might be a chat also?

(25:03) At this stage, I don’t know who’s hallucinating, who’s true, who’s a human being. (25:08) And I don’t even know if Cursor actually has terminal capabilities right now or not. (25:13) So I mean, I don’t know which company is going to win.

(25:18) But all of these companies are going to make a lot of money for sure. (25:23) The thing that scares, I think should scare Cursor and Lovable is that chat GPT, Anthropix, they can shut them down like that. (25:31) They can just decide these companies are gone and they’ll be gone overnight.

Melissa

(25:37) Yeah. (25:37) And you pose a really good point. (25:40) Who are we talking to?

(25:42) Is it a human? (25:44) I know I was just fooled last week for the first time. (25:48) I actually thought I was talking to a human.

(25:52) And then I realized it took me three minutes into the conversation. (25:55) I was with 100% certainty that it was a human, but it was not a human. (26:01) And so let’s talk about- Is this voice or is this text?

(26:05) Voice.

Ari

(26:06) Oh my Lord.

Melissa

(26:07) It was that good. (26:09) It was that good.

Ari

(26:11) So can we just agree that AI passes the Turing test? (26:15) What does that even mean? (26:16) And this isn’t general artificial intelligence.

(26:19) This is the shitty AI, basically neural networks. (26:22) If the old neural networks can pass a Turing test and we don’t yet have what we call general AI, general artificial intelligence, what does that even mean? (26:33) All of our mathematical models are failing us.

(26:37) The person that invented computers that came up, Alan Turing, that basically won World War II, is all failing us. (26:44) What is happening? (26:46) Just history is suddenly meaningless?

(26:49) Is that what’s happening? (26:50) I don’t know how to understand how something like a neural network is passing the Turing test. (26:55) In fact, I would make another argument.

(26:57) Are we even intelligent? (26:59) Because if a neural network can pass the Turing test, are we just a neural network and we have no intelligence? (27:04) Or do these neural networks have intelligence already and we just don’t know that they’re almost general artificial intelligence?

(27:10) So I think these borders are suddenly so fuzzy that…

Melissa

(27:17) Yeah, it’s interesting because I’ve used other ones where I have a unique last name. (27:22) They weren’t even close. (27:24) They didn’t even sniff the test.

(27:27) But I would say in the last few weeks, is it because of GPT-5? (27:33) I don’t know what’s going on. (27:36) But it was the first time I was duped and I would call myself a very savvy, intuitive kind of person.

(27:44) So I’m curious in closing the human side of what’s happening in our world. (27:51) We both have young kids. (27:55) I don’t know about your 11-year-old, but my 12-year-old twins, when they have friends over, they’re all on electronics playing different games.

(28:04) And I have to tell them to put it down and talk to me like a human. (28:08) And they look at me like I have three heads. (28:10) So let’s talk about the human side of using AI and not continuing to develop as a human.

Ari

(28:19) Right. (28:20) So we talked a little bit about how it’s unlocking human potential. (28:23) I think that’s true, but it can also diminish human potential.

(28:27) And I think if you’re just pointing out everything to AI and you’re not going to that next level, and now you’re like, oh, I’ll just drink beers and do TV while AI is doing my job, that probably won’t last long. (28:38) Even if you’re brilliant and nobody else can do what you can do with AI, you’re basically diminishing yourself because you’re no longer challenging yourself. (28:46) So I think that’s a problem that my age, my people are going to have.

(28:50) Like they’re brilliant enough to automate their job, but should they do that? (28:55) And then lay back on the couch, drink beer. (28:57) I think the answer to that is no.

(28:58) The second aspect of this is kids, where I’m a dad, I have three kids. (29:03) And then the question is, well, what should I urge my kids to learn? (29:07) Should they go and do a degree at all?

(29:11) Just this morning, I told my son, the 13-year-old, in the next 10 years, you probably don’t need a degree. (29:18) Why? (29:19) Because the universities have completely lost touch of what needs to be taught in order for it to give value to the job seekers.

(29:28) So the job seekers will do three, four years, they’ll get no value. (29:32) So people are going to be like, oh, universities are useless. (29:34) It will probably take them five to 10 years to figure maybe more to figure it out and to basically rebuild the whole system and structure and syllabus, and then they’ll be useful again.

(29:43) But in this 10 years of period where we don’t know what to teach our youngsters, they’re going to have to figure it out on their own. (29:50) And I think in that interim, new educational organizations, and this is one of my big agendas, will come up, and they will basically help people learn the skills that they need. (30:02) And this is basically what I’m doing.

(30:04) And I mentioned firefighters, because my big thing that is coming out soon is actually AI for firefighters. (30:11) And I think that anybody can be empowered, anybody can be empowered to basically leverage AI for the success of their job. (30:22) And universities, I think, will not get it.

(30:25) They’re going to fail. (30:25) Some might succeed, but mostly they’ll fail. (30:28) And then we’re going to have to figure out how to basically have the new education, which is going to train our future generation.

(30:34) And with government responsible for that in universities, it’s probably going to take a while. (30:38) So I think that once people believe that they can be that, then they’re going to have to do a lot of self-learning and find people like me or others, which are basically like, in order to save my local community from the catastrophe that’s looming, I need to make sure that my community is educated and has the tools to be successful in this new world. (31:00) And that’s what I’m teaching my son.

(31:02) You have to get these tools to be successful in the new world. (31:04) You need to understand what the new world is going to look like, or at least have a few ideas of what it might look like, scenario analysis planning. (31:10) And then you need to gain the tools.

(31:12) So for example, what my son is doing, the few things won’t go away from the world. (31:15) Healthcare is not going to go away from the world. (31:18) Commerce is not going to go away from the world.

(31:19) So my son is now selling stuff on eBay. (31:23) So he’s going around the house. (31:25) I was about to throw away my old routers.

(31:28) It was on the way to the trash, because I’m like, I’m not going to waste my time. (31:31) I’m trying to sell this. (31:32) He saw that and was like, what are you doing there?

(31:35) So he takes this off and I’m like, what? (31:38) And he’s like, oh, can I sell it? (31:40) And I’m like, where?

(31:42) On eBay. (31:43) So I’m like, sure. (31:44) So I didn’t help him with it.

(31:45) He did it all on his own. (31:46) He got banned from eBay. (31:49) Like 48 hours, he’s banned.

(31:51) So he’s like, oh, dad, I need a new email. (31:53) Okay, fine. (31:54) So I gave him a new email.

(31:56) I have a domain, so I can create however many emails I want. (31:59) So I give him a new email. (32:01) And this time he doesn’t get banned.

(32:02) He’s sold basically 10 products. (32:04) He’s made $600 in a few weeks. (32:07) Now he spent maybe three hours on this.

(32:09) Now let’s do the math. (32:11) $600 divided by three, that’s $200 an hour. (32:15) He’s making more money than I am right now.

(32:18) Now, sure, my wife said, well, he can’t scale that. (32:20) He’s not making more money than you are, because he can’t sell 6,000 items a month. (32:24) That’s too much.

(32:25) I’m like, well, there are ways to scale. (32:29) There’s drop shipping, there’s garage sales, there’s increasing the price of items. (32:34) He can go into luxury items.

(32:35) So there is a potential. (32:37) So if he’s learning commerce, like, holy shit. (32:40) And the other thing that he’s learning is I said, hey, if you, and I think legal is going to be a thing.

(32:45) I’m sure that the legals that are just reviewing contracts, sure, that will disappear from the world. (32:49) But legal people who are out in the world suing, prosecuting, defending, that’s going to stay. (32:53) So I told him, and I’m like, what do you love?

(32:55) So he said, lawyer. (32:57) So I was like, okay, that’s a weird thing to love, but fine. (33:01) So I said to him this, you want to do this?

(33:02) You’re going to do it my way. (33:04) And what’s my way? (33:05) So I Googled the youngest person ever to become a lawyer.

(33:08) It’s 17. (33:10) So I said, Liam, you become a lawyer at age 16, you get a new car from me. (33:18) I pay for a new car.

(33:21) Now, this has been going on for about six months. (33:24) He’s 13, he’s studying law. (33:27) And the other day, he starts talking to me and I realized I have no idea what he just said.

(33:33) So at the age of 13, he is now speaking a new language, legalese. (33:37) I have no idea what he just said. (33:39) So will he meet this timeline before the age of 17 or 16?

(33:44) I need to double check, might be 16. (33:46) Will he meet that timeline and get the car? (33:49) I don’t know.

(33:50) But is he going to be incredibly successful in life? (33:53) Hell yeah.

Melissa

(33:55) And kudos to you for that. (33:56) Because I think when we were in engineering school studying, we had to use the thick manuals to study our fundamentals test. (34:04) And now I got rid of all my stuff.

(34:07) I’m like, cause now all of it can be used quizzing AI. (34:11) My oldest did her, you know, studied for her driving test. (34:15) There’s silly manuals that they had paper manuals.

(34:19) She’s like, oh, no problem. (34:20) Easy peasy. (34:21) Got it done.

(34:22) And it took her an hour. (34:24) Right. (34:24) And so I love that you’re saying that.

(34:27) So we’ll have to do this podcast in a couple of years and see, did he get that degree and the vehicle? (34:33) And funny enough, I even think about, you know, driving. (34:38) Will he even be driving in three years?

Ari

(34:41) Probably not.

Melissa

(34:43) Because like, well, he’ll still have the beautiful car.

Ari

(34:46) He loves the car. (34:46) Does he have to drive it or just sit in the back? (34:48) Like who cares, right?

Melissa

(34:50) Yes. (34:50) Well, it’s funny because in Austin, I was just at the Ferrari dealership for a party and the people that were buying the Ferraris, they were buying them, but they had no miles on them. (35:03) They, their opinion is when everybody goes to gets rid of all their vehicles, it’s going to be such a privilege to drive that they’re going to keep and own all these.

Ari

(35:15) So my son, even if the car can drive itself, he’s going to be at the wheel because he’s going to, I think he’s going to love it. (35:21) He gets that from his grandfather. (35:23) He used to service Formula One cars.

(35:25) So I totally think that, you know, he’s going to drive no matter if the car can drive itself. (35:33) And, you know, there is a joy to life. (35:36) Me personally, I want to fall asleep in the back of the car.

(35:40) That’s me. (35:42) But my son, my father-in-law, his grandfather, he, you know, so the techs that would basically service the Formula One, they had to go faster than the drivers and through dirt roads to get to the next stop in time. (35:56) So this is Soviet Union time.

(36:00) And so basically he was driving like crazy a Formula One car because no other car would get there in time. (36:06) So he was just a mechanic driving a Formula One car faster. (36:11) Well, not really faster, but as fast, but through dirt shortcuts.

(36:15) And he told us a story about how there was an issue with the brakes with one of his cars. (36:19) So he had to skid on the ice, this wall of ice on the side of the, just to slow down. (36:24) So he wouldn’t die basically.

(36:25) So, I mean, you know, I don’t think that the joy of living is ever going to go away. (36:30) Um, I think people find joy in things that are joyous and if it’s driving a car, I mean, yeah, right on to you.

Melissa

(36:37) I love it. (36:38) That’s such a great way to, any final thoughts or anything we didn’t cover that you want to share with us?

Ari

(36:45) Just take your head out of the sand. (36:47) Like, you know, what is it? (36:48) Ostriches?

(36:49) I can’t remember the, what’s the bird that buries its head in the sand, if that’s even true or not an urban legend, I don’t know, but don’t bury your head in the sand. (36:56) Don’t be an ostrich. (36:58) Buy a book and learn.

(37:00) Yes, math is still unfortunate. (37:01) We all hate math. (37:02) Well, I don’t, but math is going to be incredibly important in the future because that’s how neural networks work.

(37:08) And if you want to understand how AI works, you need to study math. (37:11) So yeah, have your children study math. (37:14) Take a book and learn about neural networks.

(37:17) Sure, you’re not going to build your own neural network, but it will teach you how to prevent hallucinations in neural networks and why hallucinations are happening. (37:23) And that will teach you one of the most important skills in the world because everybody’s going to be trying to solve that now. (37:28) And that’s what we’re doing too.

(37:30) So, you know, I would say just work hard, enjoy life. (37:35) And as long as you don’t see yourself as a victim who is not capable, you’re going to kick ass. (37:41) It’s the people who give up, you know, and my son did.

(37:45) He’s a black belt. (37:45) He learned karate. (37:46) There was one slogan, and this is probably the last thing I can end with.

(37:50) Winners never quit and quitters never win.

Melissa

(37:53) Ah, so good, Ari. (37:56) Thank you so much for sharing your knowledge and your fantastic personality with our listeners. (38:02) Where can they connect with you?

(38:04) What’s the best way?

Ari

(38:05) There’s 56 different places they can connect with us. (38:08) The easiest place is probably LinkedIn, but we’re everywhere. (38:11) AI has automated our distribution.

(38:14) So the Story Samurai podcast is everywhere. (38:17) What’s special about our podcast is that we believe that everybody has a story to tell. (38:21) So I’ve interviewed people from the first female firefighter in the United States to a person who’s basically the first biohack person.

(38:30) So he’s put chips in his fingertips and magnets on his body. (38:33) So it’s really interesting and it’s really about having anybody tell their story and about really great stories being told. (38:41) So tune into Story Samurai anywhere you can find us, which is almost everywhere.

Melissa

(38:47) Thank you so much, Ari. (38:49) That’s the Executive Connect podcast.

Ari

(38:53) What an absolute delight. (38:54) Thank you so much for having me.

 

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