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Looking ahead to the next year in tech and human impact
0 Playsin 18 hours
2025 was hella weird. The AI revolution is here whether we asked for it or not. This week, George K and George A reflect on the year and what it means for 2026.
At AWS re:Invent, George A watched a machine create a custom fragrance and marketing campaign in real-time from a voice prompt. What does that portend for product prototyping, and scaled manufacturing?
Could voice and natural language finally replacing typing as the primary interface? We're watching the biggest shift in human-computer interaction since the mouse.
Worldwide AI adoption isn't hype anymore—it's happening and doing so unevenly. Some enterprises are getting serious and some are still noodling. The tools are maturing. The question shifted from "if" to "how do we do this responsibly."
There are serious questions to answer. GPU lifecycles. The Magnificent Seven’s circular financing models. The human cost of moving this fast. But that's the work—building technology that serves us instead of the other way around.
The revolution came. Now comes the interesting part: what we actually build with it.
2026 is going to be wild. We remain up to the challenge.
Mentioned:
- Brookings Institution, “New data show no AI jobs apocalypse—for now”
- Discussed in further detail with Ethan Mollick on Your Undivided Attention
- Reid Hoffman’s interview with Wispr Flow founder/CEO Tanay Kothari
- More on Coreweave’s financing model at The Verge
Recommended
Transcript
Reflections on 2025 and AWS reInvent Insights
00:00:08
Speaker
All right, listeners, it's the end of the year. 2025 has been weird, been rough for a lot. And we just wanted to take a moment to look back on it, talk a little bit about what we're thinking through for 2026.
00:00:23
Speaker
Not about the show, but about the trends, about all the stuff that we've been talking about the whole season.
00:00:30
Speaker
Sure. We're just at AWS reInvent. So that's sort of the last of the big conferences related to cloud and infrastructure and no doubt AI, whatever the hell. So why don't you give us a quick recap? Most interesting things you saw, things that like put a bug in your brain about other considerations. And we'll just sort of free form this episode and we'll close out the year strong.
00:00:57
Speaker
Yeah, so it's kind of interesting. um I thought the experience was really cool. First of all, it was my first AWS reInvent. This is not the pure security conference. This is like the everything, all the founders, kinds it's like collision. When Toronto has collision, it's the same kind of thing.
00:01:13
Speaker
um But this is ah it was really fun. All the security shops were there as usual, but it was interesting to see non-security tech playing. It was interesting to see NVIDIA because I saw their most cutting-edge server racks and chips. They had them on display and you know, say what you will about NVIDIA as a business, and we've talked about them before, but to see the technology live in person, to touch it, um and to look at the way modern server server racks are being architected and built now, I thought that was really fascinating. um Fascinating, excuse me, i should say.
AI Innovations at AWS reInvent
00:01:42
Speaker
um I think one thing that was really interesting is actual advancement in AI use case application. And George, I was talking to a little bit about this. So one of the things they had set up because they had a whole, ah like they had a lot of vr stuff, which is really cool. i actually won a set of of meta goggles, like meta VR goggles at a CISO dinner I went to and they're like $500 and I'm gonna go like try them out tonight because i still haven't even opened up the box.
00:02:10
Speaker
Merit Bear, thank you very much for ah pulling me into that random event because that was just random Merit. Like, George, what are you doing? Let's go to dinner there. And I was like, it wasn't even on my books and we won like ah the ticket draw or whatever.
00:02:22
Speaker
um But yeah, so they have this thing called a fragrance lab. and i And I really predict this might be the future of product marketing, actually product manufacturing to marketing, right, as a function.
00:02:34
Speaker
So what they do is you line up, you go in and you explain kind of what fragrance you're looking for. And it goes into the system and the system actually produces a cologne or perfume for you of that fragrance, like personalized to you.
00:02:48
Speaker
And then on top of that, what it will do is it'll actually produce a small marketing campaign for that fragrance. All in the spot. So can you walk me through like what you would say to it?
00:03:01
Speaker
Like what is the input? Like you would say like, obviously like what your preferred gender is, or if it's a unisex fragrance, you would kind of give like, you know, I want something that's more evergreen or I want something that's more like fresh. I want something that has a little more of a musk to it. Right. Like I want something that has like a little bit more of an aftershave, but not as heavy.
00:03:21
Speaker
and You kind of describe in the typical ways that you would describe a fragrance anyone's ever. Voice or text? ah So this is actually voice to text. Okay. Okay. Even better. We're going to touch on that in a second. But yes, I i get sort of, so we're stringing together an LLM attached to some other yeah classifier that's going to like, but this time it's connected to like chemical output.
00:03:43
Speaker
Something is being made as a result. It's connected to a machine that's actually making the thing that you described to the best that it can. But for the most part, like I, the thing I got was really good. Like I just asked for something that was a simple, like,
00:03:56
Speaker
post-workout make me smell not stinky. I forget exactly how worded it on the prop, but basically just like make me smell fresh after a workout. Okay. And just like wanted a light odor. I don't want something that's too heavy. i want something that like, if you were close enough to hug me, you could smell it, but otherwise like you wouldn't smell
AI Integration in Business Processes
00:04:13
Speaker
anything. Okay. By the way, in general, I think is the appropriate use of cologne. Correct. That's whole other conversation. Fuck you, Axe body spray.
00:04:21
Speaker
no
00:04:25
Speaker
Yeah. um But it's, it's, I think what's really cool to me was not only that it produced the product, it was, which was a physical product based on a, on a vocalized or inputted prompt, like whether you typed whether you set it, like the prompt based output of a product was one thing.
00:04:42
Speaker
And actually making really quick turnaround on very small, short, like, like ads and like a web front end for that thing, that product.
00:04:53
Speaker
Interesting. That to me was just like, oh, this, this is a game changer. Because like, I know this just the issue, but. Yeah. Manufacturing prototyping out of that kind of LLM stuff would be very interesting.
00:05:06
Speaker
Right. That would be rather than you just going back and forth with a chat bot. I mean, I think that's way more interesting to me. um Yeah. I honestly think like, you know, there are plenty of applications for it. And, you know, you look at like, you know, even for some like us, like, um,
00:05:24
Speaker
you know, looking at like a store, if you want to produce a thing, like how being able to produce new products. um I do think that once you kind of figure out the cost of actually implementing and running the system for this and the equipment, um you probably in the long run could save a lot of money on, on staffing, on, on distribution channels, right? Like you won't have to go to different shops and different organizations to build a product line and a supply chain that,
00:05:50
Speaker
um to actually get something to market in theory, right? So if you actually can get the raw components, you literally could in-house the entire supply chain and then your distribution channel would really be your retail storefront.
00:06:02
Speaker
And nowadays with everyone having web-based storefronts, even the brick and mortar is kind of an optional, right? Yeah, I think this highlights one of my great hopes on the NLP and LLM um front when it first started, which is taking a long time to get there. But The future that I saw for it wasn't sticking a chatbot in software and you just talking to it versus, you know, running line commands.
00:06:30
Speaker
It was getting even simpler, right? It was trying to strip down the interface so that we can finally get rid of the keyboard, right? I think the gold standard is we all want Jarvis, right? From Iron Man. We just want be like talk at the machine and think with the machine, which brings up one of the more exciting technologies, Whisperflow, which has incredible high fidelity and has solved the problem of taking what you're trying to say and massaging that input. They're not transcribing what you're saying. They're actually doing that extra step of, you know, take the ums and the uhs out and order the thoughts.
00:07:10
Speaker
And Reid Hoffman did a great interview with the founder CEO, whose name escapes me. Sorry, Whisperflow. But he, you know, had this interesting set, you know, the fastest typers, right?
AI Market Growth and Adoption Challenges
00:07:23
Speaker
Maybe 110 words per minute. I think most people type anywhere between 50 to 80 words per minute. but you can speak comfortably at about 400 words per minute, right? So there's a double-edged sword there. I think there's always ah a role for writing when you want to slow your thoughts down. But for most of us in our day-to-day work, if you could just tell the computer what you wanted to do and it could take that and run those functions, like that would be incredible.
00:07:50
Speaker
And to this point, George, earlier this week, I saw a demo from a startup company working in the identity space specifically for mid-market. And they were going through several other their features, but they happened to show that you could build workflows like you see in other tooling, but no drag and drop, no low code.
00:08:12
Speaker
The founder literally just hit a mic button and said, i need this user to have access to Google Drive and provision them for Slack at normal pace. It took in that input and like built built it, built the workflow, like right there in the tooling. And if you can imagine mid-market security teams that have like three and five people where they're currently wasting tons of time pointing and clicking and dragging and dropping, like that is an amazing benefit. Anyway, I'm i'm very excited about us moving beyond typing and chat. was this Was this a security technology or just a standard? Yeah, it was identity technology for mid-market shops.
00:08:56
Speaker
I think that's fascinating. and You know, I think, you know, one of the things I think we want to talk about for this episode was probably the ah the overall state of the AI market. yeah I have some some projects on the go and and finance and understanding the state of the market is kind of a little important for me right now. And, you know, folks, maybe you'll hear about it in 2026. things go our way.
00:09:15
Speaker
But, you know, i think I think there's a couple components in AI right now that are are really telling to what the market state is. And based on what I'm seeing in AWS, what I see in the actual consumer market, and what I'm seeing in government and academia.
00:09:29
Speaker
The first thing is that there's widespread but uneven adoption, right? and I think, you know, you're looking at about 90% of organizations are using AI for at least one business function, which is up probably about like, I would say at least, you know, maybe, maybe four fifths, right? You're you're looking at like 80% up from last year where everyone was talking about AI, but now they've all implemented it.
00:09:54
Speaker
Some, some a lot more successfully than others. A lot of use cases make more sense than others. A lot of layoff announcements that happen for, you know, trying to elevate that that stock price have been quietly clawed back and they're begging employees to come back.
00:10:09
Speaker
Sometimes paid it at fair rates, sometimes not. That's a whole other conversation. But I think, you know, we're not a place where we can um replace humans. I think the real gains with AI are when it's deeply embedded within the actual process. So the use case makes sense.
00:10:24
Speaker
Because a lot of companies have been treating it like a one-off pilot, just embedding it into workflows, yeah you know redesigning certain processes. But when you're looking at trying to adopt management practices and really investing in the data and infrastructure and actually creating an AI enhanced organization where your business processes are now optimized, not replacing them, but optimized by its existence. I think that's where the the biggest growth and gains and efficiency and innovation are.
00:10:51
Speaker
i think there's still unending massive growth and market size. Like I think I read a stat that was like something 244 billion alone and in terms of AI market growth in 2025. And the forecast is still expected to grow more rapidly in spite of all the bubble fears. Like you can't turn on YouTube nowadays without seeing someone post something new about the AI bubble.
00:11:13
Speaker
And I think it's actually now becoming part of of the core digital infrastructure, right? So I think now we're at a point where you really can't compete in today's market without having a part of your organization. And then one thing I'd want to look at though, and I want to get your take on this, George, where do you see like the risks? Like where do you see things being shaky in terms of limits, hype, and the structural challenges we're still seeing? Because obviously you work at Avar that it deals with the cutting edge.
00:11:41
Speaker
Yeah, I, the bubble fear to me is the market pressure to see realized gains.
AI's Impact on Labor and Workforce Dynamics
00:11:50
Speaker
And if that does not happen quickly and people get nervous, a bubble becomes kind of a self-fulfilling prophecy, which is, you know, how sell-offs happen.
00:11:59
Speaker
but I think the more dangerous thing that I think about when I think about the market is a lot of the ways that the financing is very incestuous and circular.
00:12:11
Speaker
So, you know, you've got CoreWeave, which is heavily leveraged and it's building out data centers, but it's financed in large part by NVIDIA and they use their access to GPUs as collateral against the loans that they're taking out. Right. So it's not like Jenga and it's not a house of cards. It's just like this broken, slinky, twisty mess where a lot of stuff is double backed on itself. And so
00:12:44
Speaker
What makes me nervous about that level of intricate financing is us a sell-off would ah ricochet in that system very quickly.
00:12:55
Speaker
And unlike a dot-com collapse, we have a lot more, as you said, infra tied to it, right? Like data centers are not small things. They employ lots of people, not when they're ah finalized, but when they're being built.
00:13:12
Speaker
And then If you look at the Magnificent Seven, they account for something. The last count I saw was like 37 to 38 percent of the S&P 500. and I worry about the number of public sector pension funds that track the S&P. You think about the number of teachers, firefighters, police unions, everything.
00:13:32
Speaker
um And so if you have any kind of meltdown in one third the market, that becomes a little shaky. Right. so that makes me a little bit nervous. I do think. To your point about AI and business, it does take, it's very uneven because it takes kind of a wholesale reimagination. It's not like, let me take this one crappy process we have and let's make it cheaper and faster.
00:13:57
Speaker
It's like, do we even need that process at all? And I think there's a dearth of business leadership that is willing to imagine in that but at that level, right? Because a lot of them have their own fiefdoms. They're trying to like protect their little piece of the leadership pie. And I think it really takes a wholesale reimagining of how some of the businesses work. Now, to your point about labor, labor and AI are really hard to tease apart because there's a lot of other things, tariffs and geopolitics and stuff that are making things a little shaky. Brookings Institution had a report come out August, not August, October first
00:14:37
Speaker
That actually found no large scale displacement in labor because of the because of ai at least not that could be measured in broad strokes working with the Yale Budget Lab.
00:14:50
Speaker
However, I do think that there's because everyone's waiting to see, like, what will AI shake out to be? There is a lot of that downward. pressure on hiring entry level, right? That's where I think we're seeing a lot of quote unquote, AI displacement is just not a lot of entry level hiring because people are like, will I need these or I'm going to wait to see if AI can just wholesale replace that layer in my org, which is, i think three to five years from now could be a bit problematic, right? As your management a class ages out, retires out, like you will have wanted some new blow blood in the system.
00:15:30
Speaker
Hey, just a quick word to say thank you for listening and to ask a favor. If you're digging the new direction of the show, which is looking more at human flourishing and the impact of technology more broadly, share it with friends.
Infrastructure Costs and AI Model Transition
00:15:44
Speaker
It really helps the show.
00:15:45
Speaker
We're really trying to grow something here organically. We don't do paid media. We don't do a lot of sponsorships, so we'd appreciate getting the word out and getting it to people who care about the questions that we're tackling, how to keep tech human, and how to make technology work for us instead of the other way around.
00:16:07
Speaker
Thanks so much for your time and attention. And now back to the interview. Well, and that's that's kind of like, you know, we we talk about some of biggest risks and one of them is overhype versus return, which obviously speaks to the bubble.
00:16:19
Speaker
um But, you know, i think one of the bigger things is is the infrastructure costs still remain enormous, right? So now you're seeing a whole separate niche market open up where Within the hyperscalers, trying to figure out a cost optimization between hosting and GPUs and CPUs is now a big regular part of the conversation. You know, like dealing with my CIO, it's a big regular part of the conversation. And when I talk to different partners, it's it's something that they brag about where they can help us reduce costs on hosting.
00:16:47
Speaker
um And that's now a really big important selling port. So I think, you know the infrastructure cost is really the biggest challenge. The fact that we're stuck on LLMs when SLMs like small language models, I think are actually the future because I think it's going to be a lot more localized. It's a reduction of security and privacy risk, and it's a lot easier to manage from a cost infrastructure standpoint.
00:17:07
Speaker
Ultimately, I think one of the biggest challenges, and I'd like to get your opinion on this because you have that um anthropological background, is the uneven global and regional adoption factor, right? Because you have like advanced economies or even like advanced jurisdictions within those overall countries and advanced economies that might be really cutting edge.
Global and Regional AI Adoption Variances
00:17:29
Speaker
But then, you know, there's a difference between what you see in a new York City, Austin, um Miami, you know, Silicon Valley, San Diego, Seattle, like those cities versus what you see in like the Midwest and like a Boise or or Des Moines or whatever, right? Like, I think that might be one of the biggest issues because when AI gets adopted there, it's usually by the government or the municipality in some form of privacy intruding control measure.
00:17:59
Speaker
you know, where they're monitoring you and spy on you. Or those are the marketers that are doing facial recognition of, well, they looked at this billboard more, they like this ad more, and that's absolutely in in breach of privacy and compliance, but it's what they do now.
00:18:11
Speaker
yeah Where do you see kind of like the regional disparate ah the regional disparities having an impact based on your experience talking to startups all around the world and all over America?
00:18:23
Speaker
Yeah, I think a lot of the stuff that's tied to Silicon Valley, So whether that is overseas investment um or otherwise is really over indexing on these huge scale models still.
00:18:44
Speaker
Whereas I think we will be surprised by some disruptive technologies that come out of other, I would say, quote unquote, unexpected corners of the world where they're experimenting with chaining together different models, not just a language model, but maybe visual classifiers, stuff like that. It reminds me of how we bank in the West and how like a lot of Africa leapfrogged they just like skipped web-based banking and went to much faster mobile banking than we did like we were actually i don't know if our listeners know this we were like playing catch up when near field communications and apple pay that was like in east africa for a long time just like costa rica leapfrogged us in mobile technology because
00:19:36
Speaker
while we were um hanging telephone poles for a century and stuff like that, when mobile came out and they couldn't ah cut down their entire rainforest because it's a... a tourism driver for them, they were able to just skip that step entirely. So I wouldn't be surprised if we see some more disruptive inventions coming out of different corners, because I think our media landscape is just overly saturated on the AGI Kool-Aid and this idea of creating a general purpose
00:20:09
Speaker
super monster that can do all the things versus some very narrow applications that can do some other things extremely well. um Just like the voice thing, I'm very excited for how this technology can be used for, you know, marginalized communities, you know, like,
00:20:29
Speaker
how quickly you could spin up sign language and stuff like that or translation. um But yes, I think to your earlier question about regional adoption, it does become similar to how we saw broadband internet, right? Like it took a long time for rural communities to get broadband internet until they figured out how to hang fiber optic from the telephone wires instead of trying to dig everything underground.
00:20:53
Speaker
um But that very much became... I think if anyone remembers in the Obama administration, there was a push to do that because like to get a job required you to be online.
Market Predictions and AI Utilization
00:21:04
Speaker
And if you lived in a rural community, you had to go to like the local public library to fill out a job application because you couldn't get it at home. And that was definitely a limiting factor there. So, yeah, I think I think we will see a lot of regional variation. But I also think, George, just like.
00:21:22
Speaker
email entering the workforce and internet SaaS more broadly. It took several years before like the quote unquote returns were were being seen and realized by businesses. You know, like when email was first a thing, I remember my my mom's work transitioning from literally type written typewriten memos on a typewriter to to email. it It took a while before before the speed of that communication made a material impact.
00:21:48
Speaker
And I think you really hit the nail on the head because my, you know, to to get away from being a ah doomsday sayer about this bubble, my opinion of it is not a bubble, but I think we're going to have what's called, or I don't know if anyone calls it that, I'm calling it the great correction.
00:22:05
Speaker
but i think yes the market's actually going to correct based on actual outputs of utilization and results and what people are actually buying and spending money on and what, what you know, the consumers are saying because in a democratized consumer economy, people vote with their wallets.
AI's Societal Role and Human Adaptation
00:22:20
Speaker
And I think A lot of these experiments, and all these big investments in data centers that are trying to make predictive bets on where the market's going to go, or ultimately many of them are going to fail. And it's going to be a rough time for a lot of folks.
00:22:33
Speaker
But I think the technology is not going anywhere. And this is it. we We are post AI. The revolution's hit. I'm more worried about quantum. Most other people who are in the know are worried about quantum.
00:22:44
Speaker
But I think the market correction is going to be very painful, but it's not going to destroy our economy. I think it's going to weed out the, it's it's going to cut the fat and force those that have not taken the time to educate themselves and invest in training.
00:23:01
Speaker
um They're going to, they're going to go the way of, of, you know, horse and buggy operators and switchboard operators. Yeah, i heard I heard it said this way. This is a quote, and I'll link to the podcast in the show notes. But if you can do your job locked in a closet with a computer, you're far far more at risk in the future with AI than if you can't.
00:23:21
Speaker
which is to say that if your job requires a lot of bundled tasks and diverse skill sets, especially ones that involve going and influencing people talking to other parts of a business, like physically talking to them, you're safer than if, as we have seen, if you're just sitting at a terminal and coding,
00:23:41
Speaker
Like, who would have predicted in 2002, when everyone wanted to become a software engineer, that they might be the first to be replaced by artificial intelligence? um that That was a surprise, I think, to a lot of people.
00:23:58
Speaker
um Another thing I'm looking at for 2026 we don't yet know the life of a gpu no Like how how hard can you run the training runs and before you run up against the laws of physics and there is electrical resistance and circuits and things get hot. That's why data centers require a lot of water and cooling, etc.
00:24:23
Speaker
So the reason I ask what is the life of a GPU is because, as I said before, If you have one of the largest data center builders and leasers in Coroweave leveraging their debt against the value of their GPUs, like we have 100,000 A100s,
00:24:40
Speaker
a one hundreds but what happens when you start burning through those and they have to get replaced? Like if everything's tied to the commodity value of this thing and we don't really know what is the lifetime value of the thing, like how long does it retain that value? I think that is like the shakiest brick in the AI financing ah ball of yarn.
00:25:04
Speaker
Yeah, I think it's kind of difficult because you can't really predict how it's going to go. and And really like, you know, I had a good talk with my, ah with my CIO about this because we were kind of trying to plan out, you know, staffing and looking at numbers and looking like, all right, well, you know, what do we want to do from implementation standpoint? And,
00:25:27
Speaker
You know, I was of the of the old school thing. I'm like, well, we why don't we set like a strategy where we create a strategy document and we set like a one to three year strategy plan for this. And, you know, he was sitting with me with one of our architects and they both looked at me and rolled their eyes and said, what are you talking about one to three years? This thing changes one to two months.
00:25:47
Speaker
like Like we can barely keep up. And I think, you know, I think that was a bit of a wake up call to me. It'd be like, you know, like the answer to to risk mitigation, I think in this space still comes back to the old school fundamentals of like having a proper SDLC and you still need those devs or some devs that know how to do this shit properly and manually, because I think safety and and and risk aversion is in the traditional best practices.
00:26:16
Speaker
But innovation is happening so quickly that we don't know what resources are going to be there and what we need. So you look at the question of GPUs, it's like, okay, well,
00:26:29
Speaker
we might invest x amount of money this year, but if the GPUs that are in use that, you know were implemented in 2021, 2022, 2023 fry out, you know, is our plan still going to function?
00:26:41
Speaker
Like what's the implication if if there's a major outage and then we have all these business processes. We've had three or four this year, right? Single points of failure that take down whole parts of the internet.
00:26:52
Speaker
Yeah. and then And the sad part is, is that it's the same shit. Like it's still just like, a handful of dudes running ah an os OS software that's, you know, the the pin that's holding down the entire internet. Well, unfortunately, we have someone who's sold some GPUs to a hyperscaler or some organization that's hosting everyone. And when those GPUs burn out and there isn't capacity in the overflow to actually cover off the need,
00:27:19
Speaker
what happens to the businesses and the governments and the organizations that are on like the now um powerless infrastructure. Or as we like to joke, it's always DNS.
00:27:34
Speaker
It's always DNS. When if went in doubt. Yeah. um Yeah, so that is what I am looking forward to. i think 2026 is a year where we see a lot of moves on the enterprise front.
Future Speculations and Legal Challenges for AI
00:27:49
Speaker
I think people getting smarter about you can't just put generative AI in it and get business value out of it. There's going to be some real tests there.
00:27:58
Speaker
ah Interested to see, for example, OpenAI kind of chasing its tail in multiple directions and declaring a code red, whereas Anthropic, maybe having decided that they can't compete on the consumer front, has doubled down on enterprise and are, you know,
00:28:15
Speaker
recognizing real revenue in that regard. That's interesting to me that I think, I think OpenAI is going have to really staff up on some legal teams though, because everybody's suing them right now.
00:28:26
Speaker
And that's, that's, well, that is what happens when you move super fast without any regard for data privacy, uh, or, legality, right? um So i think I think we're going to, some of that correction that you spoke about is going to come in market economics. Some of that correction, I think, is really also going to come from societal expectations and pushback around some of that stuff.
00:28:52
Speaker
um And one of my favorite inventions for the end of this year was a browser extension that sits in Chrome and filters out AI Sloth by giving you only ah return results from everything before November 2022. Like it's like just like brute forces a return to a simpler time ah just to prevent you from seeing all the all the nonsense.
00:29:17
Speaker
But yes, 2026 will be exciting. I think looking at new kinds of interfaces voice I still have a lot of hope for augmented reality I want that giant projection monitor where I can just move holograms around one day um i'm I'm really here for that future and I think we're gonna have to watch very closely this financing ball of yarn as I said that all of them are wrapped into one another they're also
00:29:48
Speaker
competing with one another. So they they buy GPUs, but they design their own chips and what that means for onshore building capacity, which could be very exciting. I think there's just a lot to shake out. But as you said, the revolution has come.
00:30:02
Speaker
The AI is here and we are here to continue to reckon with the human impacts and to make sure that that technology, the questions we are asking, make it work for us rather than the other way around.
00:30:15
Speaker
Yeah, i think I think one of the biggest things I'd look at, think we're entering into a time of great change. I think, you know, not to tee up overhype, but um listeners of our show are going to be very pleasantly surprised at some things that you and I are working on that maybe not going to talk about quite yet, but...
00:30:34
Speaker
I think the time has come for us to really take this show and our own careers and everything we're doing and really kind of um blow it up to levels that we probably haven't experienced before. Because if you're not swimming in deep waters and you're just kind of bored,
00:30:49
Speaker
All gas, no brakes to mix fuel and metaphors in medium. That's it. So i think there's going some new technologies that are coming out. We're going to involved with some new technologies. We've worked ourselves in the wonderful positions. I think there's going to be really good times for the society. George, you guys have done a great job. You, Jay, Marie, everyone on the team, Larry.
00:31:07
Speaker
um i think there's going to be some fun times that I might drive up and, you know some folks might start seeing me around America a lot more. And, um and you know, I think,
00:31:18
Speaker
I think when it comes down to it, this show, i just had to to, I was thinking about this earlier today. The direction we took for season four, i think has been one of the most fun and um kind of like reinvigorating things that I've done in my career in the last little while because,
00:31:38
Speaker
As technology gets crazier and as we get more deep into the business of it and all that kind of thing, the fact that we're still getting back to the human beings that have to exist in it and what makes them different and what they're doing.
00:31:51
Speaker
don't know, man. I feel like this little show of ours, is just a touch of a voice of reason in a world full of hype and madness and bullshit. And I think we kind of speak a bit of common language so that people who are also like us, who are all exhausted and overworked, can at least get some value. be like, hey, I'm not the only one. And this is really cool. And we should watch out for this. And I really hope we just continue to provide value to our listeners. And that's kind of what I'm excited about doing as we get more innovative for 26.
00:32:20
Speaker
Yes, we are here to tell you, you are not taking crazy pills.
00:32:26
Speaker
It is crazy out there. And we are here to to poke holes, ask questions. So doubt where needed. But that is it for this episode. We will have a few best ofs, but we will see you again with fresh new content in the new year. You safe, all.
00:32:45
Speaker
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00:32:58
Speaker
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