This one is from Season 2, Episode 5, with Alex Millar, co-founder and CTO of GovAI, and previously the co-founder of Bonfire, a procurement platform built for government. First released almost exactly a year ago.

Let’s look into it! 👓

When first spoke with Alex, people were already arguing about whether AI products were “real” products or just wrappers around foundation models. That question still floats around a lot today, surprisingly enough, and I appreciated that Alex didn't just stop there.

He challenged: is that really the interesting question to ask?

🎙 From the Archives: Alex Millar on How Everything Is A Wrapper

“Well, technically everything’s a wrapper.”

The question was simple: how does a product stand on its own if it's just a ChatGPT wrapper? An app that changes the way you interact with ChatGPT… but still, fundamentally, is just ChatGPT under the hood.

Alex's answer made me laugh out loud (and not figuratively). 😂

Yes, a product may wrap a model. But, if you think about it, every SaaS app out there is also just a web interface that’s a wrapper to a database somewhere. The real interesting question is whether the app enables more value than the raw underlying capability would on its own.

For a product to do that, it means doing the work to understand the niche well enough to know what the buyer needs around the AI itself.

In Alex’s case, that included things like privacy controls, acceptable use policies, PII detection and redaction, SSO, staff training, peer groups, and pricing that works for a city budget instead of a generic per-seat SaaS motion.

Today, a lot of AI discussion still stays at the level of capability. You know, can the model do this? Can it retrieve that? Can it call tools? Can it act agentically?

But, in reality, buyers care more about purchasing something they can roll out, govern, budget for, explain internally, and trust enough to keep using… rather than the raw list of features and capabilities.

💡 One Core Insight: The Product Is the Fit

One of the parts that stood out the most to me was Alex’s point that positioning and offering are part of the product.

Those are often seen as marketing terms, but really, they're more fundamental.

He gave a very practical example with pricing. Instead of charging per user, GovAI was working from a flat-fee model based roughly on city size and employee count. He learned that a city needs fixed costs. A city also doesn't want to decide which staff get access and which staff don't. That's just an overhead nightmare.

The pricing was part of product judgment that became part of their unique value proposition.

Same with the way he described training and peer groups. And, same with the way he talked about serving a niche tightly enough that you can identify common workflows and put useful rails around them.

Product strength often shows up in the surrounding decisions, not just in the model choice or the interface.

If you are building in AI right now, that still feels like a very useful filter:

Are you just shipping a capability? Or, are you shaping something people in a specific context can adopt?

🎧 Listen to the full episode now!

🔑 One Key Clip: A Great Product Still Has to Make a Noise

“But at the end of the day, if you have a great product and no one knows about it, does the product even exist?”

In the bonus episode, Alex said something I think a lot of technical founders need to hear.

Yes, you have to build something that genuinely resonates with people and delivers value. But, that's only half the battle.

You also have to figure out how to tell people about it and get it into their hands.

A lot of technical people are comfortable with that first half and uncomfortable with the second. They want the product to speak for itself. They hope a good build will naturally get discovered. Sadly, that's not how it works.

That felt especially relevant in the context of AI, where new products appear constantly and where the market is crowded with demos, launches, experiments, and noise.

A useful product, no matter how innovative, still needs distribution, timing, and sales and marketing that fit the stage of the company… just like every other business.

👂 Listen to the bonus episode now!

🕔 One Year Later

The part that still stands out to me now is how grounded Alex’s framework was.

He wasn’t talking as though the hard part was having access to the model or the tech. He made it clear the hard part was understanding the buyer, shaping the offer, handling the edge cases, and building something people could actually adopt.

And after all this time, and all the other conversations we've had with other founders and builders… that still feels… right.

It seems that a lot of strong AI product thinking looks pretty ordinary from the outside. It looks like pricing choices, rollout choices, training choices, workflow choices, and careful decisions about what to support now versus later.

That may not be the flashiest part of AI… but it may just be the most valuable.

As always, thanks for listening. 🙏

P.S. Artificial Insights is a podcast on how AI is changing work, life—and us. Every other Friday, Daniel Manary sits down with leaders, thinkers, and builders in AI to have candid conversations on what they’re doing right now and how they think the world will change. If you’re a podcast listener, we’d love for you to check us out!

P.P.S. If you liked the episode, please subscribe, share, and or give the show 5 stars. Every little bit helps! ⭐

Artificial Insights is a part of the Manary.haus family ❤️ Come say hi!

This one is from Season 2, Episode 4, with Bijan Vaez, CEO of Merchkit, three-time founder, and former CTO who has spent years building SaaS products through more than one technology shift.

🛍 Let’s go shopping!

This conversation aged really well and it was fun to revisit. You know, like good wine. 🍷

When first spoke with Bijan, the AI world was still in one of those especially dizzying phases where everything felt possible and very little felt settled. New capabilities were showing up constantly and everyone was trying to figure out where the real opportunities were.

No one could really tell.

That was the reason this podcast started at all: to cut through the noise and distinguish between hype and lasting impact.

So, let’s dig into it: what does actually last?

🎙 From the Archives: Bijan Vaez on Why We Shouldn’t Get Stuck On What AI “Can” Do

“When you’re just focused on the feature that AI enables, that could be wiped out very quickly.”

Bijan is the founder and CEO of Merchkit, where he works on AI-powered product catalog management and enrichment for retailers, marketplaces, and enterprise brands.

I loved how honest he was about the path that got him there. He didn’t start with a neat vertical AI thesis already in hand, he started where a lot of curious builders started: with the frontier itself.

AI inspired him to get back into being a technical person again, not just a builder of companies. That was cool.

He talked about getting pulled into GPT-3 early, experimenting with Stable Diffusion, and even trying to dig into the deeper research side of the field. Then he went to CVPR, asked researchers all his hardest questions, and came away with a very clarifying realization: this space was much bigger, and much less settled, than he’d assumed.

“And I was like, oh no — if they don’t know, I have no chance of figuring this out. I just realized how big and how nascent this whole space is.

That realization changed the direction of his work.

Instead of trying to move the frontier forward himself, he stepped back and asked how the new capabilities could be applied to a business problem he could understand deeply enough to solve.

💡 One Core Insight: The Hard Part Is Usually Upstream

Turns out, the problem was pretty unglamorous.

Most people start with, “What is the coolest thing AI can do?”

Bijan instead asked why customers kept getting disappointing results from AI tools they were already excited to use.

And the answer, again and again, was… dun dun dun…

Data.

Teams were dumping huge amounts of messy product information into ChatGPT and hoping it would somehow transform that into useful output. Instead, they got results that created more cleanup work. The model was, essentially, being asked to perform magic on bad inputs.

I suppose, that was the promise, right?

Bijan and his team realized the real work that needed to be done: helping retailers ingest, clean, contextualize, and enrich product data, so the downstream workflows became tractable. Once the data quality improved, a lot of the “AI magic” people wanted actually started to become possible.

This episode was a reminder that lot of AI conversations, even today, stay at the level of capability. Can the model do structured output? Can it reason? Can it summarize? Can it generate? The answer is usually “Yes, but…”

If the workflow is weak and the source data is unreliable, the capability itself, no matter how magical, won’t help you.

Anwar talked about how AI companies run into that trouble all the time in the very first episode of the podcast: real-world data is messy… and it turns out AI can be quite picky about its data.

🎧 Listen to the full episode here!

🔑 One Key Clip: Big Markets Give You Room to Learn

 “… if there’s a big enough market, you can keep tinkering, and there’re enough customers to throw a new idea at.

In the bonus episode, Bijan talked about something that sounds obvious once its been said: when a technology shift is this large, it’s actually hard to know where to start. There are too many opportunities, too many possible wedges, too many things that might work…

So, best to start in a very large market.

That way, if the first idea misses, there’s still enough room to keep testing, adjusting, and learning from the same kind of customer without immediately running out of runway… or actual customers.

He started in AI product photography. That was the first swing.

It led him into conversations with customers. Those conversations exposed mismatched expectations, thin budgets, and a much more painful operational problem hiding underneath the original use case. And that’s how he ended up much deeper in retail data infrastructure and enrichment.

That still feels like very good advice for founders building in AI now:

Bijan didn’t get overly attached to the first story he told himself about where the value was—he used his first product idea as a listening device. And then, through relentless focus on trying to discover what the jobs that needed to be done were, he found a bigger, juicier problem to solve.

👂 Listen to the bonus clip now!

🕔 One Year Later

The surface-level feature race feels like it’s only intensified—more and more capabilities are getting bundled into the major platforms. Tools that once looked differentiated get crowded out very quickly.

In that environment, Bijan’s warning is more relevant now than ever: if your whole business is a thin wrapper around a feature, you’re in a fragile position.

I’d argue, you technically always have been… but it’s just a lot more obvious now. The market is less forgiving.

But, if your business is built around a real workflow, real context, and real operational pain, you have something sturdier to work with.

That might not be as glamorous, and a lot of times, it makes the work much harder. You have to understand how people actually do their jobs and put in the work to understand the data they rely on, the edge cases they run into, the review steps they can’t skip, the trust thresholds they care about, and the parts of the process where human judgment is still doing indispensable work.

All that is way slower (and way less fun) than shipping a vibe-coded demo.

But, it’s also much closer to building something useful.

This conversation reminded me that “Applied AI” is often a much “humbler” discipline than the phrase makes it sound. Instead of flashy, frontier tech, it means going deep into a business context, solving old problems in old systems with newly available tools.

It means caring whether a team can trust the output enough to actually use it.

That’s still so relevant now, Pat Belliveau and Daniel talked about it in the most recent episode of the podcast!

(Totally organic connection, I promise. It just stood out to me.)

As always, thanks for listening. 🙏

P.S. Artificial Insights is a podcast on how AI is changing work, life—and us. Every other Friday, Daniel Manary sits down with leaders, thinkers, and builders in AI to have candid conversations on what they’re doing right now and how they think the world will change. If you’re a podcast listener, we’d love for you to check us out!

P.P.S. If you liked the episode, please subscribe, share, and or give the show 5 stars. Every little bit helps! ⭐

Artificial Insights is a part of the Manary.haus family ❤️ Come say hi!

This one is from Season 2, Episode 3, with Jennifer Moss, an international speaker and the author of Why Are We Here?.

🤩 Let’s get to it!

Some conversations were recorded in a different season of life, but we can still feel the weather in them today. When first spoke with Jennifer, AI still felt brand new to many people, and “AI Anxiety” was a new term going around.

Today, it feels simultaneously like things have and haven’t changed at all.

🎙 From the Archives: Jennifer Moss on The Post-Pandemic Multiverse of Work

“We’re in the multiverse of work. We’re not in the future of work. We’re in a whole different timeline.”

Jennifer Moss is an award-winning journalist, a Harvard Business Review columnist, and a globally recognized voice on workplace wellbeing and leadership. She’s written The Burnout Epidemic and Why Are We Here?, and she’s spent years doing the kind of work most of us don’t have the time or access to do: interviewing widely, reading deeply, and tracing patterns across what employees, executives, and researchers are all seeing at the same time.

I particularly liked this episode because she came at the conversation as a workplace culture person, not an “AI person”, who kept getting pulled into AI conversations because the human effects were showing up everywhere.

Like she just couldn’t escape it.

I suppose, no one could.

In the episode, she talked about how the pandemic changed the baseline for how people relate to work, why “why am I here?” became more than a passing thought for so many, and why leaders kept reaching for the wrong levers when they felt the pressure to move fast.

It was fun!

💡 One Core Insight: We’re All Still Living in The Aftershocks of the Pandemic

This interview was released almost exactly a year ago… and yet, I think everything that was said then still holds true. Especially the bit about how we’re in a “multiverse of work”.

(Still one of my favorite clips. 😊)

We didn’t return to the old timeline after 2020. A lot of people changed how they personally related to work, meaning, and time, and they did it in the middle of shared loss, shared grief, and the unsettling awareness that normal life can disappear suddenly and unceremoniously.

We all had to rethink life in the unsettling company of mortality salience.

As a society, we were forced to face the finitude of life, and when that happened, it created stress, reactivity, and polarization for some, and a deeper pull toward meaning and impact for others. In other words, the pandemic shaped what people tolerated, what they prioritized, and what started to feel pointless.

In the main episode, Jennifer connected this to why so many people made big career moves, why the “why am I here?” question became persistent, and why it’s naïve to treat the pandemic like a thing we can simply put in the rearview mirror.

Jennifer made the observation that many workplaces are still trying to solve today’s morale and engagement problems with pre-2020 instincts. They tighten the rules, increase visibility, reassert control, push speed and urgency as a substitute for clarity.

However, mortality salience changes the context people are bringing into work. When someone has been shaken awake to “life is short”, they don’t automatically snap back into old patterns just because a mandate says it’s time. Unconsciously or not, they start looking for purpose, they start measuring leaders differently, and they start asking harder questions about whether work is fuel or just grind.

AI landed in the middle of all that… as an accelerant. If leaders treat AI adoption as a race, while workers are still navigating a deepened sensitivity to meaning, safety, and direction, it creates the conditions for disengagement that looks like laziness from far away and feels like self-protection from the inside.

If you’ve been wondering about “AI Anxiety”, “FOBO”, and why engagement at work is at an all-time low and hopelessness is at an all-time high, the full episode explores all of that and includes some practical ideas on what to do.

🎧 Listen to the full episode here!

🔑 One Key Clip: How AI Turned Into a Race Before It Became a Plan

The bonus clip is a tight explanation of why so many AI rollouts feel frantic.

Jennifer shared a surprising data point: in 2019, technology wasn’t even in the top six disruptors CEOs were most worried about. Then, in 2020, it finally showed up on the list. Within two years, it shot up to number one.

That shift illuminated a specific kind of executive pressure:

“We can’t get behind.”

Jennifer described the push and pull she saw inside leadership teams. On one side, urgency and competitiveness. On the other side, a quiet admission that the fundamentals were missing:

“I don’t really have a strategy.”

“I don’t really have a plan.”

“I don’t really have a communication strategy.”

And employees felt that absence immediately—they felt the adoption pressure, but they didn’t hear a coherent story about what changed, what stayed, and how people would be supported through the transition.

Jennifer pointed out that the uncertainty became a subtle, persistent force. If people stopped seeing themselves in the future of an organization, hopefulness dropped, and employees didn’t see a point to being loyal or engaged.

She also named the missed opportunity: AI was supposed to remove the mundane tasks and open room for more meaningful work. But you can’t just automate someone’s tasks and call it progress if you don’t redesign the role and invest in upskilling.

Where was all the meaningful work that we were promised?

👂 Listen to the bonus short here!

🕔 One Year Later

It’s been a whole year, and yet, if you look at the world… things haven’t really changed.

Many teams are still living with the aftershocks Jennifer described. Mortality salience didn’t actually evaporate when restrictions ended—people still feel that low-grade awareness that life is finite, and there is still a tangible sense of existential angst.

I think that might be why Matt Schumer’s viral essay “Something Big Is Happening” hit everyone the way it did. It framed this current “AI moment” as “February 2020 again”, with AI as the incoming wave, and it tells people to prepare for job disruption fast or be hit unawares like most of the world in 2020.

It’s almost a little triggering.

But panic isn’t a strategy. If leadership can’t explain what AI is for, what changes in real jobs, and how people will be supported, workers interpret “move fast” as “you’re on your own”.

And more than anything else, that’s just a surefire to get AI Anxiety, Fear of Becoming Obsolete, and disengagement, even in high-performing teams.

As always, thanks for listening. 🙏

P.S. Artificial Insights is a podcast on how AI is changing work, life—and us. Every other Friday, Daniel Manary sits down with leaders, thinkers, and builders in AI to have candid conversations on what they’re doing right now and how they think the world will change. If you’re a podcast listener, we’d love for you to check us out!

P.P.S. If you liked the episode, please subscribe, share, and or give the show 5 stars. Every little bit helps! ⭐

Artificial Insights is a part of the Manary.haus family ❤️ Come say hi!

Today we’re rewinding to Season 2, Episode 2 with Atif Khan, who at the time was Chief AI Officer at Messagepoint, and who is now AI Architect at MindBridge, as well as a Data Growth Coach at Communitech.

Let’s get into it!

A year ago, “AI-first” meant something specific.

I mean, it still does.

I think most people just forgot.

Upon revisiting this, I realized how often we now use “AI-first” without defining it. Atif did a year ago, and in a way that still holds up.

🎙️ From the Archives: Atif Khan

“I organically grew in the industry as a software developer, computer scientist… and then went back to university… to understand not just the engineering part of things… but also what’s the science behind it.”

Atif lives at the seam between research and production. He cares about the theory, and he cares just as much about whether said theory survives contact with the real world.

One story he shared really stood out to me. A talented student joined his team and, within a week, was in tears.

“My classifier is not working.”

In class, the data had been clean. In the real world, it wasn’t. The theory was applied correctly, but the context she found herself in wasn’t what she expected… and she wasn’t trained to know how to compensate.

That gap between elegant theory and messy implementation is where most AI efforts stall. Atif’s message was poignant: understanding how something is built is only half the work. The other half is adapting it to reality.

💡 Core Insight: AI-First Is a Change in Expectations

In this episode, Atif defined “AI-first.”

If someone hands you a thousand numbers to add, you wouldn’t add them manually. You could, but that would be a very bad idea.

Instead, you’d use Excel because it compresses effort, removes friction, and would do it way more accurately and perfectly. “AI-first” is the next step. You show the machine the problem, and it returns not just the answer, but the reasoning behind it.

“Potentially it can look at those numbers. It can write a Python script in the background… give you the Python script so you can validate that this answer is correct, as well as give you an answer in real time.”

If your team had access to tools that compress cognitive effort, but your expectations haven’t changed, you are not AI-first. You are AI-enabled at best.

At Messagepoint, this became tangible. Copilot was rolled out to developers. They were not micromanaged in how to use it. Instead, the expected outcomes were adjusted.

“Less code reviews, more robust code… better coverage from unit testing… you can definitely measure each and every one of those on a daily basis with every commit.”

That is AI-first: a recalibration of what competent work looks like.

🎧 The full episode unpacks this mindset in more detail, and it’s worth revisiting.

🎧 Check out the full episode here!

🔑 Key Clip: Go Touch the Elephant

“There’s a story… people were taken to a dark room… and they had never seen an elephant before… everybody described the elephant differently.”

In the bonus episode, Atif uses this parable to describe AI. Depending on what you’ve touched, the beast looks different.

And then, he ups the stakes.

“This specific beast… it’s not just an elephant, it’s a dinosaur.”

Confusion, he argues, doesn’t go away by staying outside the room.

“We all have to go into the dark room and touch the elephant.”

And the barrier to entry is lower than ever.

“You don’t need to learn a programming language… you can literally just get on a browser, talk to it and get a feel for what it can do.”

What surprised Atif most over the last two years wasn’t that it could generate text, but that it could reflect.

“It’s going to be… at a point where it’s a cognitive companion.”

That phrase still stands out to me. AI not just as a text generator, but a cognitive companion. Something you can test ideas against, refine thinking with, and use to accelerate learning.

“I can potentially tap into the voice of many experts.”

And there’s no perfect time to start… or rather, any time is the perfect time to start.

So, the question is simple: have you stepped into the room yet?

👉 Check out the short

🧭 One Year Later

Revisiting this conversation, what stood out to me, again, was the mindset shift that seemed required to wrap our heads around AI properly and correctly.

AI-first was never about hiring researchers or announcing a roadmap… it was about recognizing that cognition had become cheaper and adjusting your workflows accordingly.

If you say your company is AI-first, what changed in how you measure work?

That question still feels uncomfortable in the best way.

A year later, the tooling has improved and the headlines have multiplied, but the core insight remains the same.

“AI-first” isn’t just a feature roadmap, it’s a necessary change in expectations.

As always, thanks for listening. 🙏

P.S. Artificial Insights is a podcast on how AI is changing work, life—and us. Every other Friday, Daniel Manary sits down with leaders, thinkers, and builders in AI to have candid conversations on what they’re doing right now and how they think the world will change. If you’re a podcast listener, we’d love for you to check us out!

P.P.S. If you liked the episode, please subscribe, share, and or give the show 5 stars. Every little bit helps! ⭐

Artificial Insights is a part of the Manary.haus family ❤️ Come say hi!

Today, we’re looking back exactly one year, to our Season 2 opener with Pat Belliveau, Managing Partner at GambitCo.

Let’s goooo! 💃

The conversation was recorded during a different AI moment, before “agents” became everyone’s favorite word, and while most teams were still trying to decide whether AI was a toy or a threat.

Pat was already living in the part of the curve we’re all catching up to now: building something real, putting it in people’s hands, learning fast, and staying close to the problem.

🎙️ From the Archives: Pat Belliveau

Pat is an entrepreneur and technologist whose AI journey started before the rest of us had language for it. He built AskEllyn an AI companion based on a breast cancer survivor’s memoir, so patients and families could access shared lived experience without being dropped into the chaos of unmoderated groups.

He did it with no coding background, learning by trial, YouTube, and ChatGPT. The result is now used in over 15 countries. That one project became the seed of GambitCo, and a repeatable way of building tools that people actually trust enough to use.

Pat is unusually grounded for an AI optimist and builder, and also refreshingly focused on building a real business.

“…if you can’t package something to sell to a client… you don’t have a business… because… the most dangerous thing you could do with AI is raise a bunch of money to go heads down, build something for a year…  it’s, like, the number one way to light money on fire.”

Unlike most AI tech founders, he talks about refusing VC money to stay focused on building what customers will pay for and calls out the game that most VC-funded tech startups and entrepreneurs play—they like to say it’s looking for the elusive Product-Market Fit… but at the end of the day, really, it’s looking for their next successful round.

The incentives are just totally misaligned.

💡 Core Insight: Build for “Dignity,” Not Just “Safety”

This interview had so many fun stories that I still refer to today. No joke.

One of my favorites was when Pat told of a story about a failure mode they couldn’t accept—if someone asked AskEllyn, “Can I take Tylenol?”, the system would sometimes answer:

“Yes, I took Tylenol on my journey.”

Which was true.

But, that “yes” was doing too much work. It sounded too much like medical advice, even if it’s meant as a personal anecdote.

And Gambit learned pretty fast that you can’t just write a giant list of things not to say—you just can’t and won’t be able to account for every single edge case that might come up.

You simply can’t put your arms around all the ways a person might ask something risky.

So they shifted the target from “What must it never say?” to “What does a dignified answer sound like?”

“…we got very good at… instead of worrying about the things that it shouldn’t say, worried about what is a dignified answer…”

Now that’s a weird engineering constraint.

“No medical advice” is still there, but “dignity” becomes the design standard for tone, framing, and how the system handles ambiguity.

Most AI teams treat safety as a checklist, then ship something that is technically compliant and emotionally clumsy. Users then bounce, or worse, they feel unseen. Pat’s insight is that for certain domains, trust is about how the tool behaves when the user is vulnerable—facts matter, but delivery is also important.

He gave a few really cool examples that show what “dignified answers” unlocked:

• Hospitals wanted to put AskEllyn QR codes in waiting rooms once they saw it was supporting the human side without stepping into medical advice.

• Husbands used it to learn how to support a spouse through chemo. No body thought husbands would be an ideal user, but there you go.

• A friend used AskEllyn to build a gift basket that actually matched what chemo can do to taste and appetite, and it created a moment where everyone felt understood.

If you’re building AI that interacts with people at an emotionally loaded moment, “helpful” is just not enough. The output needs to feel like it came from someone who understands the stakes.

Listen to the full interview if you want to hear how Patrick got there, and how his team thinks about product, business, and responsibility as one system.

🎧 Check out the full episode here!

🔑 Key Clip: Education Is Pointing in the Wrong Direction

The bonus conversation takes a sharp turn into education.

Pat zeroes in on AI detection tools being used in schools and universities and calls out the downstream harm of treating students as suspects.

“You are now putting kids on trial having used AI… and that thing is wrong.”

To make the case concrete, he describes running the same text through an AI checker, then asking a model to lightly rewrite it with small mistakes.

“100 percent plagiarized… then 0 percent likelihood of AI. Thanks.”

So, now we have to ask, what is the education system is actually teaching?

Instead of helping students learn how to use tools they will be expected to use at work, schools are penalizing them for touching the very technology employers already assume is table stakes.

“If you don’t use AI, employers are not going to hire you. That’s, like, a real thing when you graduate.”

Accusing students with tools that openly admit they are unreliable is reckless, especially when layered onto an existing mental health crisis.

“We can’t do this. It’s not good for them long term, and it’s definitely not good for them near term.”

He closes the thread with a deeper reframing: if intelligence is still being measured primarily as information retention, we have already lost the plot.

“If we measure intelligence on information retention… then we’re just dumber than an AI.”

The sad thing is, a whole year later… and as far as we can tell, nothing much has really changed. We are still hearing stories from students whose marks and futures are in jeopardy because their teachers are being forced to use AI plagiarism checkers that are unreliable.

It begs the question:

Are we educating for the world students are entering, or just protecting systems that no longer accurately describe reality?

👂 Check out the short here!

🧭 One Year Later

Pat ends this conversation with a line that feels even more relevant now:

“You have not missed the boat… you’re probably still early… AI is not very old and AI is not going anywhere.”

A year later, that reads less like encouragement and more like a challenge. If we are still early, then we still get to choose the habits we build into our products, our teams, and our definition of “good enough” in AI.

The question is, are we being intentional about it?

As always, thanks for listening. 🙏

P.S. Artificial Insights is a podcast on how AI is changing work, life—and us. Every other Friday, Daniel Manary sits down with leaders, thinkers, and builders in AI to have candid conversations on what they’re doing right now and how they think the world will change. If you’re a podcast listener, we’d love for you to check us out!

P.P.S. If you liked the episode, please subscribe, share, and or give the show 5 stars. Every little bit helps! ⭐

Artificial Insights is a part of the Manary.haus family ❤️ Come say hi!