Let’s get going.

Hot Take #1: OpenAI’s acquisition of TBPN was about buying engagement, not reach.

For those of you not laser-focused on tech news, OpenAI bought a podcast for a price tag rumored to be in the “low hundreds of millions”. Technology Business Programming Network (TBPN) is a daily, three-hour live show where two hosts discuss the latest happenings in technology and interview executives and other interesting figures from across the industry.

In my opinion, it’s an entertaining listen and their roster of guests rivals that of another top tech podcast, All In. But check out this: TBPN has only 85,000 YouTube subscribers and their live stream averages just 4,000 to 10,000 views daily.

Those statistics don’t scream “major media exit.” A few things to unpack.

Despite having relatively low subscriber numbers (for what it’s worth, I would give an arm and a leg to have these figures for Relentlessly Curious), where TBPN shines is in their clips performance. Today, live streaming is the “hot” form of media, and the red-hot form of media is “clips”. Think of small snippets of conversations or scenes from a longer-format video (i.e. live streaming). Their clips regularly reach hundreds of thousands of views, sometimes breaching millions of views on X.com. They also have advertising sponsors for their clips, which makes the claim that TBPN has a $30 million advertising run-rate business more realistic. So, from a pure media economics perspective, the suspected valuation is quite high, but not truly out of this world.

Taking a step back, it may seem that buying a podcast is contrary to OpenAI’s current directive on increasing their share of the enterprise and coding market. They recently shut down Sora and Instant Checkout in a heavily publicized move to reel in any side bets that were diluting company focus.

Buying TBPN wasn’t about expanding OpenAI’s reach into mass consumer adoption of AI. It was the opposite. They want to win back the power users of AI, those who work in tech or in tech-adjacent careers. TBPN consistently attracts the biggest names in tech and has incredible engagement and view counts on their X.com clips, the epicenter of tech discourse.

Despite claiming TBPN will maintain editorial independence, the brand association with TBPN may warm up AI power users to spend less time in Claude Code and more time in Codex (OpenAI’s agentic coding software). Also, the acquisition is an acquihire of people who truly understand the cultural zeitgeist of technology in 2026. If the TBPN crew can help OpenAI craft the right marketing messaging to capture enterprise clients from Anthropic or Google, the acquisition pays for itself and then some.

One last thing. TBPN has a vibey, but approachable brand. If OpenAI can leverage TBPN to repair their brand image post-Pentagon deal, they may win back retail investors ahead of a likely in the back half of 2026. Given their IPO likely could surpass $1 trillion, a few hundred million is a drop in the bucket for the sake of improving “vibes”.

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Hot Take #2: Anthropic’s doomsday messaging around their new model Mythos is actually a good thing for society.

If you’re deep in Claude Code (like me), you understand just how great Anthropic’s top model Claude Opus 4.6 is. What I find most impressive about Opus is its ability to act as a strategic partner when designing a workflow or process. Agentic coding capabilities aside, its ability to synthesize the context I provide and quickly identify edge cases and holes in my logic helps build stronger, cleaner process automation.

Apparently, Anthropic’s new model, Mythos, has coding skills so capable that it has identified cybersecurity vulnerabilities across the web.

As a result, Anthropic quickly assembled a coalition of major enterprises named Project Glasswing because Mythos, “has already found thousands of high-severity vulnerabilities, including some in every major operating system and web browser”. Companies like JPMorgan, CrowdStrike, and Amazon are expected to work closely with Anthropic over the next 90 days to understand and fix any of the vulnerabilities surfaced by Mythos.

What’s notable is that many of these cybersecurity risks have been hiding in plain sight. As AI’s coding capabilities keep improving, it will continue to identify risks we weren’t able to comprehend with existing technology, which is particularly scary if this technology gets into the wrong hands such as a foreign adversary or a malicious domestic actor.

Riddle me this. Anthropic has built a differentiated brand in the AI space around “safety”. In fact, their legal entity is listed as a public benefit corporation, which effectively means they can prioritize stakeholders beyond shareholders.

With a brand focused on “safety”, Anthropic chooses to virtue signal frequently about the risks associated with their AI models and the steps they are taking to manage them. They release what is called a System Card with each model launch, that dives into model performance against benchmarks and potential failure modes (as well as how bad actors can exploit their technology).

Their fearmongering is a form of marketing. They focus on the downside of their models as their competitors often don’t highlight them. It makes Anthropic seem more responsible and trustworthy, particularly critical for enterprises considering using Claude models. Furthermore, it allows them to further embed themselves in enterprise and government infrastructure, which bodes well for long-term adoption of Claude. No wonder why Anthropic is crushing the B2B market.

But in the instance of Mythos, I believe caution is necessary and the 90 day pause is justified. If Anthropic is willing to risk their lead in the AI race for the sake of getting major enterprise players prepared for the risks associated with Mythos, this is a moment worth paying attention to.

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Hot Take #3: Perplexity’s move towards agentic solutions represents the only thing AI companies should be building for: outcomes.

If you’re building an AI company, you should pay attention to this. Perplexity’s annual recurring revenue (ARR) jumped 50% from February to March, on the back of their Perplexity Computer release. Their monthly active user counts topped 100 million as reported by the Financial Times, and a new pricing model, including both subscription and usage charges, has catapulted Perplexity out of the characterization of being a glorified search engine.

Perplexity Computer, a series of AI agents that leverage 19 different models to tackle the project you give it, has provided a new runway for Perplexity to build on. It also signals to the market what users value: agentic solutions.

Anthropic has Claude Code, OpenAI has Codex, and now Perplexity has Perplexity Computer. If you can knock out a complex task for a person or a company, they are willing to pay for it. Better yet, if the software you offer allows users to write in plain English what they want, and your software creates a tech product without them writing a single line of code, you’re golden.

People are willing to pay for outcomes. If your AI can’t automate a customer workflow, freeing them up to focus on higher-value tasks or scaling their business without additional headcount, good luck. Software creates insights, AI creates outcomes

*Crickets*

Well, anyway, it seems like a good time to reflect on some of the predictions I’ve made since the inception of Relentlessly Curious. For this week, I pulled out some of the hottest takes from articles that I wrote at least six months ago and am grading myself on whether I was on the right track. If you’ve been a longtime subscriber, I’m sure some of these pieces will ring a bell. If you’re new here, you get a recap of the content to date.

Let’s get going.

Prediction #01: Apple will acquire Perplexity

Article: AI + Acquisitions

Date: July 25^th, 2025

Score: Wrong 🔴

This one was way off. At the time I made this prediction, Apple did not have a clear AI strategy while their peers were seeing blistering progress. Despite not being particularly acquisitive (is that a word?) over the years (Beats by Dre is their largest acquisition to date), Perplexity was an appealing fit for the tech giant. Perplexity’s strong user growth in July 2025, recent launch of the Comet agentic browser, early adoption of advertising and affiliate channels, and relatively low valuation compared to their peers (i.e. OpenAI, Anthropic) put it in a prime position for acquisition discussions.

Spoiler alert: Apple did not acquire Perplexity. Since I wrote this article, Perplexity tried to acquire Google Chrome, even though Chrome was valued more than Perplexity at the time of their bid. Additionally, they ditched their advertising business and launched Perplexity Computer, their AI agent platform. Simply put, I have no idea what Perplexity’s longer-term strategy is, and I’m going to pause on making predictions about them. However, what I do feel confident about is that they need to choose a niche sooner rather than later, as they do not have the capital to compete with OpenAI, Anthropic, or Google on either mass-market consumer or enterprise AI adoption.

Maybe they reach down and try to own a portion of the vertical layer. I’ve always found Perplexity helpful for deep research. Maybe with their Perplexity Computer platform, they can build de facto software for researching anything. Yes, I know that’s vague, but for a company that has a $20 billion valuation, they still need to tackle vague problems.

Prediction #02: ChatGPT will launch in-prompt purchases

Article: AI + Brand (Part I)

Date: May 2^nd, 2025

Score: Correct 🟢

I was early on this take, but I don’t want to take too much of a victory lap as the writing was on the wall. There were Shopify code leaks referring to ChatGPT as a shopping channel that surfaced as early as April 2025. Thus, it was a matter of when, not if.

OpenAI needs to take on trillion-dollar industries, and commerce is certainly one of them. Given their “spray and pray” approach to new product development in 2025, it was a natural fit. They began rolling out in-prompt shopping experiments by October 2025.

How did that go? See below.

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Prediction #03: OpenAI’s Instant Checkout will struggle upon launch.

Article: The Case Against Instant Checkout

Date: October 14^th, 2025

Score: Correct 🟢

As a follow-up to the previous prediction, I bet that shopping within ChatGPT would struggle upon launch. Which it did, so I’m marking this one as green.

Yet, I didn’t predict that OpenAI would shut down Instant Checkout as quickly as they did. Instant Checkout first became available in October 2025 and OpenAI pulled the plug in March 2026. The quick takeaway is that OpenAI spread itself across too many initiatives, Instant Checkout did not drive the level of user engagement and checkout volume they predicted, and the operational challenges with integrating commerce into AI proved to be quite challenging. The AI goliath retreated from commerce, as well as from their video generation app Sora, in a prioritization push to capturing the enterprise segment (although they are still introducing advertising into ChatGPT).

My take was that Instant Checkout would struggle because the sheer amount of sales needed to make their investment worth it was unlikely to be achieved. Just because shopping is available within an AI prompt does not mean consumers suddenly have more purchasing power. The bulk of sales volume was likely to be a share shift from existing e-commerce platforms like Shopify and Walmart. Not net new consumer spending.

But Walmart didn’t need to worry about their website losing volume to ChatGPT. They saw conversion rates roughly three times worse within Instant Checkout than on Walmart.com. No share shift of volume there.

I’m still marking this as green as I did call out that people were unlikely to immediately associate ChatGPT with “shopping”, which was observed in the lack of shopping intent among ChatGPT users, according to OpenAI.

Prediction #04: Strava will IPO in 2026 and become the next major social media platform.

Article: Hot Takes on Consumer Brands

Date: August 19^th, 2025

Score: In progress 🟡

Looking to diversify away from AI in your portfolio? Try out Strava*, potentially heading to public markets soon. In February, Strava submitted an S-1 form with the SEC, a legal precursor to a public stock offering.

I marked this take as yellow, as the groundwork appears to be laid for the take to be at least partially correct. When it comes to “the next major social media platform”, that remains to be seen. As of a few weeks ago, Strava claims to have over 195 million users, with 50 million monthly active users. The key statistic to anchor when comparing social media giants is monthly active users (MAU).

Now, the 50 million MAU figure is a far cry from the likes of mid-tier social media players like Pinterest (619 million) and Snap (946 million). If we draw the line of major platforms at Pinterest, then Strava is unlikely to hit the milestone even in five years.

But Strava certainly has tailwinds in its sails, with running more popular than ever. Also, Strava recently announced a substantial global expansion with localization in ten additional countries, along with new sports for activity tracking. I believe Strava is looking to be the social media platform for exercise.

*Not financial advice

Airports are essentially billboards for credit card companies. And trust me, this is no accident. Credit card companies understand psychology and consumer behavior like the back of their hand. They aim for the first card you pull out of your wallet on your vacation or business trip to be theirs. It’s a concept called “top of wallet”, referring to the card being literally at the top of your wallet, or in the most accessible spot for repeated use.

Credit card companies want to subtly nudge you to associate their card with travel because when you are traveling, you are likely to spend significantly more than you do in your daily life. “Top of wallet” quickly translates into “top of mind”.

Airports are critical to the credit card economics flywheel, helping acquire customers and kick it off through spending. Let’s dive into the three margins critical to credit card companies and how airports help start the flywheel and push it into high gear.

Note: We will focus on examining premium rewards credit cards for major players like American Express, Chase, and Capital One. Also, if you’re new here, I used to work in the credit card industry.

Account Margin

This is where customer acquisition costs sit. But thankfully for credit card companies, they’ve found a way to offset the costs.

For an annual fee*, you gain access to exclusive perks like airport lounge access, rewards points multipliers, and travel, dining, and subscription credits. Not just anyone is allowed into an American Express Platinum airport lounge. It’s an exclusive location reserved for people who are willing to fork over the steep annual fee (nearly $900 at the time of publication for the Platinum card).

In the context of airports, these lounges are an enticing offer if you are a frequent traveler and appreciate a cleaner, secluded waiting area away from the masses. Despite being costly to maintain, they pay for themselves and then some, as a strong nudge for new sign-ups.

When it comes to monthly and annual credits, credit card companies rarely pony up all the cash. They tend to be at least partially merchant-funded, with merchants willing to subsidize the cost of the credit for their product or service with the hope that you’ll become a recurring customer of theirs. This is especially true given that premium card members typically spend more than those with $0 annual fee cards.

Rewards points multipliers come at the cost of the credit card company. You know, the 4x multiplier on dining spend (i.e. if you spend $100 at dinner, you will receive 400 rewards points, usually redeemed at ~$0.01 per point). These typically are geared around dining and travel, which tend to be higher-spend moments for people. This is a play to increase spending volume, with issuers willing to accept lower margins as the rate versus volume trade-off will likely work in the credit card company’s favor.

*For cards that do not charge an annual fee, perks tend to be quite limited. Perhaps a rewards point multiplier (i.e. greater than 1x rewards points) on a single category, but rarely any meaningful travel or dining credits.

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Spend Margin

Once you have been convinced to sign up for a fancy credit card so you can unwind at your airport lounge of choice, it’s time to get into the habit of spending. Thankfully, the credit card companies have already thought through how to get the spending flywheel going.

They offer big-time welcome bonuses. You know, the “get 100,000 rewards points”, and then in smaller font, “if you spend $6,000 within the first three months of membership.”

These welcome bonuses sit in the Account Margin but are necessary to create an urgency around using the card. And the required spending is steep for a reason. They don’t want you to be able to reach your spending threshold after a few swipes (remember Bilt?); they want you to form a habit of using your brand-new card.

Every time you use the card, the credit card company receives a small percentage of each transaction. This is an oversimplification, but this fee ranges from ~2-4% of the transaction depending on the size and category of the merchant. After paying out fees to various networks and partners, the “discount rate”, commonly used terminology within the industry regarding the spend fee percentage, nets out lower. Given that overall network volumes reach into the trillions of dollars for the likes of American Express and Chase, the spending revenue adds up quickly.

This is a core revenue driver for credit card companies: people swiping their cards to pay for goods and services.

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Lending Margin

So now that you are convinced to sign up for your grandiose premium rewards credit card at the airport and you’ve spent the amount needed to receive the welcome bonus, you are faced with a predicament. You cannot pay back the amount you owe in full.

They sure won’t admit this publicly (given the amount of legal scrutiny they already receive), but credit card companies are licking their lips when this scenario arises. They offer a minimum payment option, allowing you to pay your outstanding balance over time. But it comes at a steep cost: interest payments.

This is often the most profitable source of revenue for credit card companies. They borrow at relatively low rates from the capital markets and lend it back out to consumers and businesses at a punishingly high rate, otherwise referred to as an annual percentage rate (APR). In the case of American Express, they borrow at ~7% and charge APRs that stretch above 30%, depending on the prime rate.

Sure, the credit card companies take on the risk that you may not pay them back. For their lending business, they keep a certain amount of capital on the books in the case of charge-offs. But the interest income and the late payment fees more than offset the occasional instance that a card member stiffs them.

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See, credit card company economics work in a synchronous flywheel. They offer exclusive rewards and benefits to card members to incentivize them to sign up for the card. Then give a reason to spend immediately and frequently on their new card for the sake of unlocking their welcome bonus, which builds habit and association with the card. And finally, provide a credit line which can lead to overspending and the need to borrow. And there’s no better place to get the flywheel going than where people are primed to begin spending more than they usually do: at the start of their journey, or in other words, at the airport.

Side note: I understand I’m preaching, but whatever you do, avoid credit card debt at nearly all costs. Not only can interest rates creep up into the thirties, but also, what most people miss is that once you start revolving a balance, all additional purchases you make are subject to interest until you pay off the balance in full.

We are living in weird times. DoorDash is paying people to film themselves washing dishes.

Yes, you read that correctly. Last week, Bloomberg reported that DoorDash created an app for their “Taskers” to record themselves completing chores around the house, like dishwashing or folding clothes.

Why? It’s all about offline data collection.

As foundational model companies continually look to upgrade their AI, they’ll need new, niche datasets to maintain their edge. And believe it or not, there’s only so much data they can train on that is available online. Thus, they must look in a place no tech bro would have considered. The real, physical world.

Now, there’s plenty of niche datasets out there. So why is DoorDash offering compensation to those willing to complete everyday tasks on camera? Check out the quote below from the Bloomberg article.

“As an example of how the paid video submissions work, instructions for a dishwashing task ask that the person capture footage with a body-worn camera pointed down toward their hands, scrubbing and rinsing at least five dishes and holding each clean dish steady in frame for a few seconds before moving to the next dish. That camera footage may be valuable as robotics firms hone their humanoids’ ability to recognize objects.”

Companies building physical AI, ranging from autonomous vehicles to at-home robots, need to understand all the intricacies and variations of how people complete these everyday tasks to build a mechanical substitute.

It’s a bit dystopian. The gig economy workers may benefit in the short term by providing training data to DoorDash. But they’re training their robot replacements, as DoorDash may sell this data (they already have a partnership with Waymo) to robotics startups.

I believe 2026 is the year when the physical AI narrative starts to gain steam. Waymo has already completed over 20 million rides and is planning to launch in an additional 20 cities by year’s end. Also, you have Elon Musk shifting the storyline of Tesla from an electric vehicle manufacturer to a robotics company. Oh, and let’s not forget that the main highlight of NVIDIA’s 2026 GTC conference (some refer to it as the “Super Bowl for AI”) was innovation in physical AI that their semiconductor chips will enable.

Robotics outside the factory floor have always seemed far in the future. More so reserved for the plot of a sci-fi thriller. But as I went for a walk today in Manhattan, I strolled by a Waymo car in training. A person was in the car, but still, if an autonomous vehicle can figure out how to navigate traffic in New York City, ride-share drivers will need to find a different way to earn a living.

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The world is changing. Candidly, I believe it will become quite normal to see someone filming themselves throughout the day because they are getting compensated by an AI company for doing it. AI valuations are through the roof, particularly on the private market side. Tech companies need to justify their valuations, and physical AI presents another avenue to live up to the hype. Yet, the clearest story is how robotics are likely to reduce the labor force. That’s a topic for another article.

Because tech firms need to live up to their current valuations, the demand for real-world data will continue to rise. The issue is on the supply side. It’s not easy to capture niche tasks at scale.

Which is why I believe that tech-enabled marketplaces employing the gig economy stand to reap significant rewards in the coming years as robotics companies look for training data.

In addition to delivery companies (i.e. DoorDash), marketplaces like Angi (formerly Angie’s List), Uber, and TaskRabbit are sitting on a treasure trove of data waiting to be unlocked. Looking to create a robot that mounts televisions? You’re going to want to strap a camera on a TaskRabbit TV mounting specialist. Or have a contractor on Angi film themself cleaning a pool and checking the chlorine levels.

Private equity is famous for rolling up* service firms like plumbers, electricians, and HVAC companies for the sake of streamlining processes and adding modern technology across the group. It’s become such a popular investment strategy that there are plenty of memes about the thesis. The joke here is that, believe it or not, running a real business isn’t as easy as the math makes it out to be in a spreadsheet. But perhaps this business idea has a new runway. There’s opportunity to roll up a business within a particular trade, record how each employee completes the task at hand, and then sell that data to a robotics startup.

For example, try landscaping companies. They complete both routine (lawnmowing, fertilization) and nuanced tasks like tree cutting, irrigation system installations, and patio building. I imagine within the next five years, we’ll see the first widely adopted autonomous lawnmower. But how could one build an autonomous lawnmower? They first must acquire a ton of specialized data on how people cut lawns today.

*By rolling up, I am referring to purchasing several similar companies. The idea is that by operating, say, 20 electrician businesses, there are operational efficiencies to be had with scale and those will lead to more effective operations.

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Fast forward. Thanks to people willing to film themselves folding laundry, robots are here and ready to complete your chores. But do people really want to delegate their daily tasks to a non-human object?

It’ll be interesting to see how consumer adoption of physical AI plays out. I believe consumer adoption will lag, as people are scared by the narrative that Hollywood has portrayed about robots for decades. A robot living in your home and washing the dishes is likely many years past what any tech guru predicts. But then again, Waymo has done an excellent job with branding around autonomous driving. They claim that their cars are involved in “92% fewer crashes causing serious injuries or worse than human drivers.” Perhaps people will warm up to the robot family member sooner than I think.

But in the short-to-medium term, the opportunity for investing or making some extra cash is in the acquisition of offline data. If you told me I could get paid to do the dishes by DoorDash, if I filmed myself doing it, I would have been happy to clear the table as a kid. Sorry about that mom!

Last week, Google announced a major update to Google Maps where AI is being introduced into the core experience. By leveraging Google Maps data, which includes hundreds of millions of places and user reviews, you can now receive hyperlocal recommendations for your next trip or night out directly within Google Maps.

I believe this paragraph from the announcement sums up the new value proposition quite well:

“Your results are personalized based on things like places you’ve searched for or saved in Maps to help you get the most relevant recommendations. So when you ask, “My friends are coming from Midtown East to meet me after work. Any spots with a cozy aesthetic and a table for 4 at 7 tonight?” Ask Maps already knows you like vegan restaurants and finds convenient midway spots with vegan options.”

Personalized, niche, and informed. The makings of an ideal recommendation engine.

Before we dive into what this update means for consumers and businesses, let’s backtrack to discuss the problem that I believe Google is attempting to solve.

AI works best for deterministic problems, or in other words, outcomes that can be reasonably predicted. Sure, there is a long tail of possible questions a customer could ask an AI chatbot (like this), but most questions have something to do with a company’s assortment, return policy, or shipping delays, based on the data I see in e-commerce customer service reports. AI trained on a company’s brand voice and tone, as well as historical customer service conversations and order history, can act as an adequate facsimile for a human customer service representative.

That’s why billions of dollars have flowed into companies developing AI customer service agents. You know, startups like Fin AI, Decagon, and EliseAI. They provide enterprise firms with an option to rely on AI for their customer touchpoints across email, voice, and chat instead of relying on people.

But what about B2C, or consumer AI customer service? Now, you’re looking to solve a probabilistic scenario, where there could be many different answers based on slight variations of the question. Questions about any business or place have a much longer tail than questions that could be realistically asked of a singular business.

Last year, Google quietly shared a feature that lets Google call businesses on your behalf. Maybe you want to ensure the local shore store has your size before running over. Delegate that task to Google (assuming the store’s phone number is online).

This is inherently an example of consumer-facing AI customer service (and an example of agentic voice AI). I believe that Google is looking for niche, offline data to leverage to improve their foundational models and I suspect they launched this product to acquire phone transcript data.

Google, OpenAI, and Anthropic will continually need more data to enhance their models. And eventually, they may see diminishing returns from internet data: they’ll need to find a way to harness real-world interactions and knowledge that only exists in the physical world.

Google’s quest to acquire offline data will lead them to back into creating consumer-facing AI customer service: a highly personalized (and action-oriented) recommendation engine that allows people to enhance their offline experiences with their online presence. Google Maps is an excellent venue for this goal given the sheer amount of user contributions (over 500 million contributors, according to Google), and omnipresence of the product.

Today, Google Maps is a transportation app with crowd-sourced reviews of businesses and places. There’s a massive, untapped opportunity to turn people’s shared maps into a social feed. However, I think Google is more focused on acquiring as much data as they can, particularly data that their rivals wouldn’t be able to capture. In doing so, they are creating AI customer service for consumers that can act on behalf of people (i.e., phone calls) and provide hyperlocal, personal recommendations regardless of their location in the world.

Google states quite plainly, “combining our Gemini models with our deep understanding of the world unlocks entirely new possibilities”, which I believe nods to Google’s interest in further enhancing its offline data collection.

Think about this scenario: you’re on a road trip and you need to make a pit stop. To fill up your gas tank and use the restroom.

“Hey Gemini, find me a gas station within 15 miles that has a restroom.”

Gemini will quickly learn people’s common driving routes and the reasons why they make the stops they do. They’ll combine user reviews with prompt intent to better understand why people choose to go to one restaurant over another.

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So, Google Maps gets AI, and it’s a massive data play to learn more about how and why people travel to certain locations across the globe. What’s the business implication?

Generative Engine Optimization (GEO) matters for all businesses. Regardless of size.

Apparently, Google Maps has over two billion monthly users, which makes it hard to comprehend how much influence Google has with this product . At those numbers, AI isn’t just for people who sit behind a desk all day. It’s for everyone.

If you’re a local pizzeria that relies on foot traffic in a busy part of town, you’ll probably still be fine ignoring GEO. But if you rely on customers looking up non-branded keywords in Google Maps (“pizza near me”) to find your spot, you’re going to want to make sure you show up in AI search results because Google Maps now provides AI-generated output. This leads into my core argument: since Google Maps will leverage Gemini, optimizing your presence in Gemini will translate into more frequent and favorable recommendations within Google Maps.

Optimizing the pizzeria’s web presence for AI search results (particularly for Gemini results) will be critical for small businesses thanks to Google adding AI into Google Maps. And this goes for more than just pizzerias. Car dealerships, mechanics, and gas stations will all need to consider how people find out about their business in the first place. Adding content like a frequently asked question (FAQ) guide, directions to the dealership on major highways, or that the gas station has a public restroom, are examples of content likely to perform well on an AI prompt within Google Maps.

If you’re building in the GEO space, please reach out. I’d like to chat more about the implications this Google Maps update has for small businesses.

Either way, consumers are the real winners of AI making its way to Google Maps. As hyperscalers compete to win the AI race, we benefit from their innovation. I look forward to using the new features on my next road trip.

Hot Take #1: AI may eliminate the apprenticeship aspect of work.

Two weeks ago, Block announced that they were laying off ~40% of their workforce. Not due to poor business performance. Instead, CEO Jack Dorsey shared that they simply did not need as many people because of AI.

His tweet must have sent a chill down the spine of any corporate middle manager pushing paper every day. Executives likely know where they can trim their headcount, and if the market rewards layoffs under the guise of “AI”, their stock price (and their compensation) will benefit tremendously. Note: Block’s stock surged following the announcement, helped by strong quarterly results.

I’ve seen it in my day-to-day: AI makes me more effective and more efficient. Tasks that used to take me hours a few years ago now take minutes. And I’m not the one doing them anymore. I do agree that companies can be more productive with fewer people through well-designed systems of AI agents.

Although I’m bullish about AI’s impact on effectiveness and efficiency in the workforce, I am wary of what happens when recent college graduates enter and are told to do everything with AI.

AI works best when you start with the end in mind and work backwards to describe what you want and how you expect to get there. For instance, if you’ve been working in finance for years, you have a strong understanding of what it takes to build a financial model, slide deck, or research report without AI. You understand the workflows and have a strong sense of “what good looks like.”

Imagine it’s the first day of your career and you’re told to use AI to build a financial model valuing a skincare manufacturing plant. You write out some prompts that a coworker gave you and, boom, you receive a financial model.

How do you check the outputs? How do you know what to prompt back? How do you know the problems that you need to solve? What are you learning and how do you get better at your job if all you know is AI? How do you design processes for automation if you don’t understand the intricacies of the process?

AI allows you to become more efficient once you’ve written an effective prompt or system. And that comes from putting in the time and effort upfront to learn the craft.

Hot Take #2: The core tenet of education will need to be communication. Both with people and AI.

Much of my K-12 education centered around memorizing facts. From history class to science, I spent much of my time memorizing elements on the periodic table, world wars, and mathematical proofs.

With where AI is headed, the value of a memorization-based education comes into question. If we can simply ask AI and receive an answer in seconds, what’s the point of spending all this time memorizing much of anything?

Of course, there’s some baseline education that needs to occur. But perhaps our secondary education needs to center around how to get the most out of AI. Which starts with how to communicate with it.

This comes in the form of first principles thinking, systems design, and sequential workflows. Instead of entering the workforce and being a cog in a machine, people should be prepared to become orchestrators of workflows. This starts with understanding what problems must be solved, breaking the problem into tactical steps, and then building automated point solutions that feed into a larger system. Everyone will have the agency to solve problems at scale thanks to AI. They might as well take advantage of learning how to do so.

Learn how to think. Learn how to speak. Learn how to write. In one phrase: learn how to communicate (which requires forming thoughts and then extracting them from your brain). Whether it’s with AI or people.

In a world where memorization is no longer an advantage, the alpha is in your ability to communicate. Structured thought processes will allow you to build with AI, as well as share your ideas with others to gain buy-in. Strong, cohesive writing cuts through the noise of AI-generated slop. Articulate dialogue and the ability to strike up a conversation with anyone will be critical for differentiation as people naturally rely more heavily on machines and less on their ability to socialize with others.

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Hot Take #3: OpenAI’s slow start to agentic commerce is more of a supply problem than a demand issue.

Last week, The Information reported that OpenAI would roll back its ambitions around shopping within ChatGPT.

Need a refresher on OpenAI’s agentic commerce initiative? Check out #61: The Case Against Instant Checkout.

In the fall, OpenAI and Shopify drummed up a lot of press about how their partnership to bring shopping to ChatGPT was the future of commerce. They were building the backend infrastructure to support both humans buying products within AI chatbots and AI agents making purchases on behalf of humans.

Since then, OpenAI has been slow out of the gates in adding brands to the platform, and shopping-related inquiries often end up being glorified Google searches*. They likely bit off more than they could chew with the sheer number of challenges around payment guardrails, fraud, security, brand inventory, and overall commerce awareness. With so many competing priorities, OpenAI appears to be slowing down and focusing more on advertising based on recent news releases.

*Next time you ask ChatGPT a shopping-related question, look closely at how it searches the web to answer your question. This post explains very well how ChatGPT shopping is essentially a wrapper on Google Shopping.

Despite being a massive prize (trillion-dollar industry), commerce is incredibly difficult to crack. Especially if you’re trying to reinvent how people shop on the internet through agentic commerce.

I’ve written about my bearish sentiment on ChatGPT shopping (i.e. Instant Checkout) because people use ChatGPT for a whole host of tasks, in addition to shopping research (check out here for more). This take describes a demand problem, where users are not in a state where they are willing to make a purchase while using ChatGPT.

After reading through The Information’s scoop, my opinion has swayed towards slow adoption because of a supply problem. There were few brands on the platform ready to go.

Promising that any brand using Shopify as its e-commerce platform would be included in agentic commerce created lofty assumptions about the level of brand supply available to shoppers.

But that has not come to fruition (likely due to how complicated agentic commerce is to begin with). Anecdotally, I have not come across any products I could buy directly within ChatGPT during my day-to-day shopping research. Even large brands like Nike and Timberland required you to check out on their website. You’d think OpenAI would have gotten the bigger names on the platform first.

If you don’t have a wide assortment, people won’t associate ChatGPT with shopping. You might as well go to Amazon if you cannot guarantee that a product will be on ChatGPT.

But I believe the market overreacted. Sure, there are fair questions about the go-forward margin profile of a software business in the age of AI. However, the key advantages of deep entrenchment within customer workflows and partnerships remain.

Companies like Adobe and Figma have their products deeply embedded in their customers’ workflows. Job descriptions across the creative industry frequently require proficiency in products like Adobe or Figma because they are the default tools. AI isn’t going to take that away overnight.

Another point: if a Salesforce enterprise sales representative takes their Fortune 500 CTO client golfing each month, the CTO doesn’t suddenly want to stop golfing just because AI is here.

Through their deep client relationships, enterprise software companies have built-in distribution. They can also introduce AI (which they are) into their current offerings to satisfy evolving client needs.

Now, who else has suffered a stock price drawdown, yet has a major distribution advantage? Consulting firms. Check out Accenture’s stock, down 20% year-to-date according to Yahoo Finance. I’m sure OpenAI agrees with this.

Last week, CNBC reported OpenAI announced partnerships with major consulting firms Accenture, Boston Consulting Group (BCG), Capgemini, and McKinsey to help OpenAI’s enterprise clients accelerate AI adoption. The gist is, the consulting firms will help OpenAI’s clients get set up and running with AI in the way OpenAI intends.

If you want to learn about a company’s longer-term strategy, check out their careers page. OpenAI is hiring for hundreds of AI customer success, deployment, and partnerships roles that all appear to be client-facing. In tandem with the news article I linked above, they posted a role for a Global Partner Director, whose key responsibility is to scale their partnership with McKinsey. Although OpenAI is often perceived as a consumer-first AI company while Anthropic is often viewed as more B2B-focused, clearly OpenAI is trying to flip the script. With 40% of revenue coming from enterprise customers, they certainly have a point.

On the surface, this partnership is a win-win situation, primarily about speed. OpenAI is staffing up to support their burgeoning B2B vertical and wants to reduce the time it takes to effectively integrate OpenAI technology into their customers’ day-to-day workflows. They can focus on building the technology, and the consultants can ensure it gets used properly. Custom enterprise AI arrangements require deep knowledge of a customer’s ways of working, guardrails, and regulatory environment. There is likely significant enterprise client overlap between the consultants and OpenAI, meaning that the consultants already know how each company operates. This is a great example of each company leaning into its competitive advantage.

In a world where the return on investment (ROI) of AI is beginning to be questioned (i.e. Google and Amazon’s stock price declines following announcements of major AI capital expenditures for 2026), it’s critical to make sure that OpenAI clients are properly implementing AI and setting themselves up to see a strong ROI on the investment.

Remember the NANDA MIT report that claimed “95% of GenAI pilots are failing” from August? Regardless of how misleading that report was, OpenAI can’t take their chances that clients are suddenly going to become tech-savvy and know where and how to implement their new enterprise plan into the business. So many big company CEOs are relieved when they can announce an enterprise contract with OpenAI because that signals that they “have an AI strategy”. But it’s not going to be a good look for either party if by the second contract year there’s been very little adoption nor impact.

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So once again, the consultants win. Same set of customers (i.e. enterprise) but selling a new product: the promise of revolutionary technology.

Still, consultants face similar existential risks to their business model as software does alter how work is priced. AI certainly compresses the number of billable hours needed to complete a competitive landscape research report. And clients know this.

Even so, consultants are doing just fine financially when it comes to AI consulting. Ernst & Young saw 4% revenue growth in 2025, yet their AI-related consulting revenues jumped 30% versus the prior year. According to the New York Times, McKinsey expected its 2025 revenue to be 40% attributed to gen-AI projects. That’s billions of dollars.

The continued dominance by consulting firms to move onto the next thing to sell is no surprise. There’s so much hysteria and anxiety in the job market today about job losses (i.e., the Citrini Research report), what much of the gloomy analysis misses is the relationships forged over decades. If a Bain & Co partner has been a close confidant of the Coca-Cola CEO over the past 20 years, the working relationship isn’t going to evaporate overnight because AI is here. The advice will change, but the relationship isn’t subject to automation. There’s plenty to be worried about in this economy, but if certain areas are slower to change, it’s those where relationships serve as the anchor of distribution. Contracts are a lot stickier than you think in the enterprise client services space.

Hopefully their distribution advantage ends up leading to increased economic productivity. Consulting firms can add a lot of value to their clients on the basis of this partnership by helping clients establish their AI fluency baseline. So many companies don’t have consistent standards on what good looks like for AI adoption. Since the consulting firms will have a front-row seat for the latest AI updates as well as a strong understanding of their clients’ operations, they can craft a set of standards from which that organization can improve. Once the standards are set and adopted, the consultants can work together on establishing ROI baselines for assessing AI-related investments. As a result, a more sustainable environment will be created around AI, helping to find signal through the noise.

Shumer’s post on the future of life and work in the age of AI has reached over 80 million views, and he has since appeared on CNN and CBS News.

He makes some bold, dystopian predictions about how quickly AI is developing and the ramifications of its potential to fundamentally disrupt the job market, regardless of industry. Essentially, humans won’t be needed because AI can do the work for them.

I suspect many people will read this and feel confused. Where I believe Shumer missed is not diving deeper into why AI has become so capable. And that’s what I’d like to unpack today: the concept of agentic AI.

Different Types of AI

If you’re thinking, “I use ChatGPT and it sometimes gives me the wrong answer. How will this displace my job?”, I get it. I frequently receive fishy answers from AI chatbots like ChatGPT, Gemini, or Claude, and must regularly question their logic too.

But chatbots are simply the tip of the AI iceberg. The AI that Shumer is referring to isn’t the free version of ChatGPT (in fact, he specifically urges readers to try out a paid AI subscription). Like most services, the premium version is behind a paywall. The same goes for AI.

Side note: Premium AI tools are not expensive today. For a little more than the cost of a fast-casual lunch in Manhattan, about $20, you can see for yourself why experts feel uncertain about what the future will bring. Whether it’s ChatGPT Plus, Claude Pro, or Google AI Plus, pay $20 per month and see what you’re missing.

What’s the difference between “AI” and agentic AI?

Here’s how I’d explain it. What I’ll refer to as “regular AI” is interacting with an AI chatbot like ChatGPT or Gemini. You ask it questions, request tasks, and go back and forth in conversation. This is what most people’s experience with AI has been to date.

Yet over time, you notice the AI chatbot starts to forget key details from earlier in the conversation. That’s the limitation of a context window in effect. There’s a preset limit on what the AI chatbot can remember. So, you open a new session and remind it about what you had previously told it. AI chatbots are, in a lot of ways, like glorified search engines that can complete singular tasks. As you could have guessed, higher-tier subscriptions generally provide larger context windows and better persistent memory features.

Now, agentic AI executes multi-stage workflows. You can provide agentic AI goals and translate those goals into actions for the AI to take, which it can then run autonomously.

Regular AI is reactive. Agentic AI is proactive. Regular AI handles one task at a time. Agentic AI can handle many tasks, including those that depend upon another’s output.

Here’s what I mean.

AI chatbot: Summarize this article for me.

Agentic AI: You are an expert at evaluating newsletter platforms. Research the top three competitors to Substack, analyze each competitor’s pricing model, draft a pro and con list on each competitor, and then provide a recommendation with 80% confidence in a five-slide presentation deck on which competitor is the best fit for Relentlessly Curious.

In say an hour (if not sooner), you’ll have an output that would normally take a junior team member a week to complete.

I believe agentic AI is the future of work. We already have the capability to create AI “employees” by creating an agent to handle specific tasks. We (humans) will serve in the role of agent orchestrators, managing many agents and helping instruct the bigger picture of the project goal.

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Agent Orchestration

Check out the agent orchestration for the newsletter platform research project mentioned above.

Research Agent: Educated on first-principles thinking and decision-making frameworks, this agent searches the web and any separately provided data source to distill a curated viewpoint on the topic you’ve requested.

Price Scraper Agent: Specifically instructed to find the current prices of pre-set competitors at a pre-defined time cadence.

Slide Deck Agent: Provided a presentation structure, brand style, and writer’s voice, this agent can build presentations for you based on the data you provide it.

As the user of AI, you can instruct the Research agent and Price Scraper agent to work in parallel and then once both agents’ work is finished, request the output be used by the Slide Deck agent to create the presentation.

You don’t need to know how to code to create the above agents. By instructing AI in natural language prompting, you are simply writing instructions similarly to how you’d teach a junior employee. And thanks to emerging standards like Model Context Protocol (MCP) which aim to connect models to external tools and data sources, agentic AI can access information across the data sources that you feed it, including your Google Shared Drive, Slack, and even Shopify account. If your organization has a written document or chart on best practices for data analysis, you can tell your AI agent to reference this document, and it will factor that into its process.

This scenario is why there’s growing anxiety in the job market. You don’t need to be an engineer to build an AI agent, which democratizes software development and puts the power in the hands of those willing to be curious.

I’m oversimplifying it, but the key point with agentic AI is that it unlocks multi-dimensional workflows typically executed upon by humans. For those of you that are looking to get ahead of the curve and understand what current AI tools allow you to do, I recommend checking out some of the following tools.

Custom GPTs in ChatGPT Plus

Custom GPTs in ChatGPT Plus are not considered agentic AI, but they are still a strong starting point. A much more robust version of an AI chatbot, Custom GPTs allow users to store information in the way of written context, files, external API integrations, and model preference. No need to re-provide baseline context each time you log in.

I encourage this as your agentic AI launch pad because it teaches you to create a knowledge base for AI to regularly pull from. Examples of Custom GPTs I’ve created include written document editors, analytical thought framework partners, and social media copy generators.

Claude Code in Claude Pro

This is my favorite agentic AI tool, and I’d liken it to magic. I’ve been increasingly relying on Claude Code in my day-to-day. It’s worth calling out that I am by no means an engineer. Despite its name including “Code” you don’t need to know how to code to effectively use the tool. I provide access to my computer files and third-party connections, as well as instructions on tasks I’d like to complete. Claude Code handles the rest. I’ll refine my prompting over time and frequently push Claude to question its own logic. But the magical thing is that this agentic AI tool can reason on its own, pressure test its assumptions, and correct itself when it senses an error, all with relatively little oversight.

I will admit, there is a bit of a learning curve with how you set up Claude Code given that you generally access it through your terminal. I recommend checking out the training program, Claude Code Course for Product Managers, to get started. I know it is meant for Product Managers, but it teaches Claude Code basics and prompting best practices in a first-principles framework. This is important for any knowledge worker. Oh, and it’s free!

I understand this may feel overwhelming, but rest assured, you’re not behind. I’d like to share this chart from a LinkedIn post I stumbled across. Most people haven’t used AI before. And only a microscopic percentage are using AI at a high level. All you need to do is be curious and willing to explore!

You see it in the news. Big companies are growing revenue despite laying off tens of thousands of employees. All thanks to AI. Quarterly earnings call after earnings call; it’s the same storyline. The market rewards the efficiency wins, and as a result, the stock price soars. You’ve seen this across Big Tech, as well as technology-adjacent industries like logistics and retail.

I believe we are only at the beginning of this layoff trend. As AI innovations like Claude Cowork and OpenClaw enter the mainstream, it’s only a matter of time until employers rely more heavily on AI agents rather than traditional employees to handle the lion’s share of the workload.

However, I believe AI’s efficiencies are best demonstrated in small companies or recently founded companies. It’s a lot easier to build an AI-native foundation and culture than to convert a decades-old enterprise into one of AI-embedded workflows. For larger companies, it’s likely to take years and significant cultural change to see tangible financial results driven by AI-enabled efficiencies.

I’m skeptical of some of the mainstream headlines surrounding AI layoffs that you see today because they tend to highlight the impacts at large enterprises. Back in August, we discussed an MIT report that claimed, “95% of generative AI pilots at companies are failing.” To jog your memory, the gist of the report is most enterprises are struggling to have success turning AI into revenue gains. In my opinion, the MIT report borders on sensational and misses the point that pilots are tests, and most tests fail.

But the news keeps telling us that AI is responsible for mass layoffs. Yet, if most AI pilots are “failing,” how can managers lay off tens of thousands of people while revenues still grow?

Here’s my thought: I believe AI is taking too much credit for the recent rounds of layoffs. It starts with understanding the incentive structure of most Corporate America executives.

Right now, the AI-driven efficiency story is generously rewarded in the stock market. A common scenario: The CEO of a large company hops on the earnings call, mentions that revenues grew, and says they laid off employees due to “AI.”

I’d really like to dive in deeper to understand what percentage of these layoffs is driven by successful implementations of AI. See, Corporate America has plenty of people who aim to do the bare minimum. All the power to you, but keep in mind that your manager and your manager’s manager will likely know you’re coasting. And so do the executives, who have the power to lay off entire divisions if no longer deemed productive. Maybe there wasn’t a sense of urgency to cut underperformers or redundancies in the past. But now, there’s a carrot on the stick.

It’s just more convenient to lay off employees now because you can lump them into your company’s AI story and be handsomely rewarded by the stock market for doing so. Even if you haven’t seen success with AI yet. See, executives tend to be incentivized through stock price growth, and if they can increase the price of their stock through an AI-driven layoff story, they likely will do so.

I’m bullish about AI and believe it will lead to step-function productivity gains for society, while at the same time creating real adverse impacts on the job market. However, until I hear more executives clearly articulate how AI directly enabled headcount reductions, I’ll remain skeptical. Let’s dive into two topics that would enhance the credibility of an AI-driven layoff if included in the narrative.

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AI Fluency

This starts from the top. An organization must define what they consider their baseline for AI fluency. A CEO can come out and say that everyone at their company has access to a ChatGPT account, but this can mean so many things. Engineers may be using OpenAI’s Codex to write 80% of their code, allowing them to significantly increase output and tackle more projects, while others at the company may be typing a few keywords into ChatGPT and claim to be “AI fluent.”

It’s imperative for companies to establish a baseline around both hard and soft AI skills. Tactical details around what “good” looks like, which programs to depend on for specific tasks, when to leverage AI and when not to, safety guardrails, and plenty more must be shared broadly with employees.

Helpful examples include Shopify and Meta. Last year, Shopify’s CEO implemented a new hiring policy where managers must demonstrate that AI can’t perform a job function before gaining approval to post the role. Additionally, Meta recently announced that AI usage will be factored into go-forward employee performance reviews.

These two policies stand out as tactical changes that can lead to cultural shifts. They place AI front of mind for their employees and incentivize them to think of AI-first solutions. The sooner companies adopt similar principles surrounding what AI fluency is at their organization, the sooner they will reap the rewards.

Institutional Knowledge Transfer

The longer-term impact of reducing headcount is the loss of institutional knowledge. Even when employees leave a company on their own today, there tend to be details that fall through the cracks. And even with a robust handoff plan, not everything makes its way from the departing employee’s brain into internal documents.

This particularly matters as the workforce changes shape. It’s especially difficult for junior employees to find work as AI has begun serving an entry-level role in certain pockets. If the workforce ends up being a mix of senior people setting the strategy and mid-level people operating the AI agents, there isn’t a next generation to train and pass the knowledge on.

We’ve already seen what happens when knowledge transfer breaks down, notably in the case of Boeing. I recommend reading this essay by the Democracy Journal which highlights how Boeing’s transition away from an engineering culture to a business culture influenced where employees were hired and how they were trained. Despite admitting that it was relying on an inexperienced workforce at its new South Carolina facility designated for 737 MAX production, Boeing trudged on since it was the more cost-effective option. They relocated to a less expensive location (from Seattle) and did not properly train or hire employees with the same skill levels as those in Seattle. Boeing lost institutional knowledge, and I think you know how this story ends.

Companies will need to introduce stringent requirements and build comprehensive intelligence layers to turn their company data into living, breathing organisms that can function like humans. This could lead to policies around recording all internal meetings, and external meetings when appropriate, asking employees to save all documents on a company shared drive, and leveraging data from instant messaging software for additional context.

I imagine most organizations are doing some combination of the above today, but it will need to be turned into an intelligence layer for employees to ask questions of, helping reduce the inevitable knowledge gap during periods of layoffs and structural changes to the workforce.

The growing theory is that as more code becomes AI-generated, traditional software companies will struggle to grow because firms will develop internal tools in-house. Instead of relying on Salesforce for your company’s customer relationship management (CRM) tool, you can use a program like Claude Code to build your own custom solution.

Based on much of the online chatter I come across, I sit with majority who claim that the software stock sell-off is overblown and AI isn’t going to eat the entire software industry overnight.

But then a headline like this one makes me question everything: Goldman Sachs taps Anthropic’s Claude to automate accounting, compliance roles.

Anthropic is beginning to partner closely with enterprises to embed AI into real-world, niche workflows. The takeaway here is less that Goldman Sachs is looking to invest in automation, and more so that Anthropic will learn much about solving the types of problems facing complex, financial firms.

Through Claude Cowork (similar to Claude Code, but for non-technical tasks), users are able to automate daily tasks like market research, synthesizing customer feedback, and reorganizing your file system based on prompts typed into a prompt bar. Their AI is focused on automating as many workflows as possible, it seems.

Now imagine through Anthropic’s work with Goldman Sachs (and likely other enterprises), the level of context they’ll receive regarding which types of problems are worth diving deeper on. When Anthropic rolls out more specific use cases that firms like Goldman Sachs co-sign, it could be the reckoning moment for software companies without large proprietary datasets or omnipresent brand names.

OpenAI is trying to do the same thing too. Not too long ago, I saw a job posting where OpenAI was looking for a relationship manager to partner directly with Deloitte (it’s since been removed). A role dedicated to customizing OpenAI technology for Deloitte and its clients. A recent job post suggests OpenAI is still all-in on building out their enterprise relationships.

My hunch is this is less about making money off one client, and more about learning all the problems their client sees on a daily basis. We’ve talked about it before at Relentlessly Curious: the value is in the application layer. But with what Anthropic is building, the foundational layer and the application layer are starting to blur.

With all this said, the software market is likely to fluctuate over the next few years as the impact that foundational model companies have on this industry has yet to shake out.

So, let’s dive into a few industries that are likely to experience a tailwind because of AI.

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Industrials

Big Tech has signaled plans to spend nearly $700B on AI capital expenditures (capex) in 2026. For reference, the country of Argentina has a GDP of $700B. Between Amazon, Apple, Meta, and Google, that single expense line would collectively rank with the 25th largest GDP in the world.

So, where’s all this money going to end up? Building data centers, of course. And the infrastructure to support these massive investments.

Companies directly tied to building data centers, like Caterpillar (construction equipment), Honeywell (heavy industrial inputs), and Martin Marietta Materials (concrete), are likely to go on a tear this year. Caterpillar itself has been directly linked to data center announcements, and the others stand to benefit immensely from the GDP of Argentina being pumped into data centers.

When it comes to industrials, think as basic as it gets: inputs. Steel companies, copper mines, and cooling equipment manufacturers are all poised to win major new contracts this year with AI-related demand.

Caution: although I believe this picks and shovels play is one of the safer AI investments, pay close attention to language surrounding funding “commitments” versus breaking ground. If reports start to flurry in July that Big Tech has barely spent a fraction of their headline commitments, I expect this sector to take a hit.

Prefer an ETF? Check out FIDU from Fidelity.

Autonomous Vehicles (AV)

Uber is far and away the de facto ride sharing platform in the US. In Q3 2025, Uber recorded 180 million monthly active platform consumers, while Lyft notched 25 million. I’d say that’s capturing market share.

The rise of Waymo and autonomous driving is gaining momentum. However, in five years, my guess is that Waymo is not the only AV provider. I believe there will be plenty of new entrants that we haven’t even heard of today that will be major players in the early 2030s.

But you know who doesn’t have to deal with competition in the AV market? Uber. They can serve the same role they do now, as the platform. Buy an AV and let it drive Uber rides while you sit at home and watch TV. Or Uber may partner directly with AV manufacturers to operate its Ride Share network. Either scenario, Uber acts as the platform and doesn’t have to compete on the make and model. It is worth noting that this assumes that the regulatory environment supports such a seismic change in transportation.

Candidly, I’m not sure where Tesla will fall here. Tesla’s revenue continues to fall year over year, and Musk seems more focused on the SpaceX, xAI, and X combination. But I certainly wouldn’t bet against him.

Energy

Those data centers aren’t going to run without energy. With major enhancements to the electrical grid needed to power data centers, electric utilities located near planned data center expansions are poised to benefit from increased power demand.

Plenty of data centers are planned to be built in the Midwest and South. See below for a list of planned data center locations by major tech firms, including but not limited to:

• Amazon: Ohio

• Google/Microsoft/OpenAI: Texas

• Meta: Louisiana

• xAI: Tennessee

One approach is to focus on energy companies that serve these regions. American Electric Power covers the Ohio region, while Duke Energy also covers Ohio, as well as the Carolinas, and Tennessee. It’s worth diving deeper to better understand these firms’ energy mix, but their geographic coverage is a competitive advantage.

Prefer an ETF? Check out XLU from State Street.

We’ve touched on the credit card industry a few times here, in How Far Can Brand Take You and How Credit Card Companies Manipulate Consumer Behavior. Credit cards are a personal interest of mine, as I used to work at American Express.

Let me first catch you up on what’s been going on with Bilt, and then dive into a few predictions for Bilt’s future.

Background on Bilt

Bilt is the first major credit card program that allows card members to pay their rent or mortgage, without a transaction fee. They pulled this off by providing users with an ACH account that acts like a checking account through which payments run. Card members earn a 1x rewards point multiplier for each dollar of rent (or mortgage).

Earn rewards points on rent and put your rent check on your credit line? That’s a huge value add. For starters, I live in New York City, and rent is my largest monthly expense. Being able to earn rewards points on rent means I’m receiving multiple free flights each year. Also, paying rent with my credit card allows me to build my credit history and manage cash flow more effectively.

Additionally, Bilt created a network of hyperlocal perks that included rewards point multipliers at nearby restaurants, as well as free experiences such as workout classes. It’s a very consumer-first card program.

But nothing good lasts forever.

See, Bilt’s value proposition of no-fee rent payments with rewards points was never sustainable. It turns out, Wells Fargo has been footing most of the bill.

Bilt isn’t a bank: they’re a marketing engine disguised as a fintech company that handles card advertising and customer service. The bulge bracket bank reportedly has been losing upwards of $10 million per month (according to the WSJ), as they saw card members use Bilt for primarily their rent payment, and then pay their credit card bills in full on time. Given Wells Fargo covered much of the cost associated with offering no-fee rent payments, they didn’t have much opportunity to earn interest on revolving balances nor additional card member spend on everyday purchases.

Wells Fargo was hoping Bilt would become an acquisition channel for their mortgage business (as renters transition to homeowners), but apparently that didn’t work out. Unfortunately, Bilt attracted many people who can be categorized as “gamers”, meaning they strictly use the card for the perks, and little else. Check out this quote from Bilt CEO Ankur Jain, regarding Bilt’s rewards point requirement to transact at least five times within a month (in a recent email to all Bilt card members).

“It is probably not a surprise to any of you, but if members only purchase four bananas and earn free rent points, it doesn’t allow us to sustain such a rich value proposition for everyone.”

So, Wells Fargo cut their deal with Bilt. This triggered the need for Bilt to find a new banking partner, as well as implementing a revamped card offer that would be more enticing in the long term for a new partner.

Bilt 2.0 Launch

Fast-forward to January 2026. Bilt launches their 2.0 credit card without Wells Fargo and announces the discontinuation of all current Bilt cards. The announcement was cheery and innocuous.

That is, until you read the fine print buried deep within the credit card agreement. You know, the terms that are only accessible if you click the asterisk in the launch announcement.

I stumbled across the most confusing credit card offer I have ever read. Despite working in the credit card industry, specifically focused on the math behind credit card and loan offers, I was scratching my head. After reading over the agreement five times, I finally pieced together what was going on.

Two key points stood out.

First, rent is now auto-debited from your account upon payment. The implication: rent no longer posts to your credit balance (it is pulled from your account upon payment), thus does not contribute to building credit history. Moreover, if you want to continue earning rewards points on rent, you’ll need to pay a 3% transaction fee on your rent check. But if you spend 75% of your rent on everyday expenses, the transaction fee is covered in full under the launch terms. So, if your rent is $4,000, you’ll need to spend an additional $3,000 to avoid the transaction fee. That’s a lot of extra money.

That paragraph was confusing even to write, and that’s after I synthesized what I read and edited it.

To no one’s surprise, Bilt received major backlash online about their misleading launch announcement. The credit card industry has a poor reputation when it comes to being opaque, and Bilt further reinforced this narrative.

Since the announcement, I’ve received multiple emails from the Bilt CEO apologizing about the misleading launch and tweaking the card offering. I’ll spare you from the granular details, but the gist of the new value proposition is similar to the original launch.

To justify moving onto the Bilt 2.0 program, you really need to make Bilt your primary credit card to benefit from the higher spending requirements and conditional perks. Which makes sense, if the CEO is calling out that people are gaming the system, they likely don’t have significant share of wallet. Bilt wants a higher share of wallet and to move card members away from their existing credit cards like American Express, Chase, or Capital One.

Candidly, Bilt seems desperate to regain the trust of their community as I’ve received half a dozen emails from the CEO, and other members of the company, explaining the benefits of the new Bilt card. It’s a public relations disaster, and a good lesson in how not to communicate with your customer.

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Takeaways

The “growth at all costs” strategy funded by venture capital doesn’t work if you fundamentally change the value proposition.

Venture capital tends to be a solid fit for scaling software businesses. Price the software at a loss to fuel customer acquisition, and then over time increase the price as customers have demonstrated a reliance on the product.

Think about Uber. For years after Uber’s initial launch, rides were cheap as they were subsidized by venture capital investment. The plan, “growth at all costs.” And then slowly over time, Uber increased the price of each ride. But their product as a ride-sharing platform has always stayed the same.

Bilt is attempting a similar exercise, yet with a key flaw: they’re changing the core value proposition of their product. In a lot of ways, Bilt is increasing the price of the card for the customer but not offering the same value. Bilt’s brand equity was built around their differentiated no transaction fee rent payment offering. And by ditching that offering, they’re ditching much of their brand identity.

Bilt is going from having little competition in their niche to competing with nearly every major issuer.

Bilt owned its niche in being the credit card for housing payments. With its new value proposition, Bilt now resembles a typical premium rewards card from American Express, Chase, or Capital One. A very competitive landscape.

As we discussed in How Far Can Brand Take You, I’m skeptical about Citi breaking into the premium credit card market with their Strata Elite card because they don’t have the premium branding that drives irrational card loyalty.

Bilt hasn’t achieved premium branding status, and more broadly, is taking a major brand hit due to their fundamental switch of their value proposition (not to mention questionable public relations). Bilt had their differentiator. It’s going to be a steep hill to climb from here.

Bilt needs an experienced operator to navigate out of their tricky position. Thankfully, they have it.

Former American Express CEO Ken Chenault sits on Bilt’s board given his position as chairman of General Catalyst, an anchor investor in Bilt.

From my time at American Express, it seemed that Chenault’s legacy centered around two main things. First and foremost, he turned American Express into a premium lifestyle brand. And secondly, he was an excellent leader during crises, navigating Amex through the troughs of 9/11 and the Great Recession. Both instances where consumer spending fell drastically.

Chenault has handled difficult business environments in the payments industry before. Although he now has his hand in a peculiar problem where the core value proposition is changing, I would give him a reasonable likelihood of helping right the ship.

Hot Take #1

By the end of 2026, Gmail will establish itself as the default AI assistant for everyday consumer use.

Google claims that Gmail has 1.8 billion users. That is roughly double the weekly active users of OpenAI’s ChatGPT, which sits closer to 900 million.

Earlier in January, Google announced that it will slowly introduce AI Overviews and Gemini into Gmail. This is a significant moment, as Google Search evolved throughout 2025 with the introduction of AI-generated summaries layered over traditional results (AI Overviews) and prompt-based interfaces resembling ChatGPT (Gemini).

Gmail is finally getting the AI upgrade it has long needed. As someone who uses Gmail for both personal and work purposes, I find several of its core workflows weak at best. The new features include an AI inbox that summarizes missed emails, concise synopses of long threads that piled up during meetings, and tools that let you ask complex questions of your inbox and have Google take action on your behalf. Let’s look at a few examples.

Today, it is difficult to catch up on an email chain with dozens of replies. Maybe you are tied up in meetings, and by the time you open the thread, you end up doing your best Indiana Jones impression just to figure out where the conversation started.

With AI Overviews, Gmail will summarize email threads that went haywire during back-to-back meetings. Context is delivered instantly, and time is saved. These summaries will also allow users to ask questions about their inbox instead of manually scanning email threads.

Here’s why I think Gmail will win. It already functions as your operating system (main communication channel for email correspondence), and AI will only make it more productive. Moreover, AI updates in Gmail mean billions of people will begin using AI more frequently. The association between Gmail and AI may lend them to stick with Gemini-related AI properties. The education aspect of how to use AI is underrated.

Yet, my favorite feature of these Gmail updates is the ability to ask questions and assign tasks to Gemini. Gone are the days of missing an email and forgetting about it. Check out a few prompts I’ve written for myself using the Gemini-powered AI chatbot on the right side of your inbox.

“Help me sort through emails that I haven’t replied to that request action. Prioritize by business impact, showing the top 5. Also, include a one-liner on why each request should be prioritized”

“I’m looking to better assess how consistent my email writing is. Look through my last 90 days of sent emails and categorize the intent of the email, as well as how consistent my writing structure/syntax/grammar was across categories”

“Write an email in a similar tone to my last 3 emails related to responding to potential vendor inquiries along the lines of ‘We are interested in the software, however, have to make a few internal changes on our side and would like to reevaluate in 3 months’”

You’ll be impressed by the results of these prompts in the time they save you. The new Gmail features allow you to level up your productivity and efficiency. Because of the all-in-one nature of Gmail, I believe people will begin viewing Gmail as their default AI assistant.

Hot Take #2

Generative Engine Optimization (GEO) platforms will become Generative Engine Marketing (GEM) agencies.

At its core, many GEO software platforms do roughly the same thing. They allow you to track prompts relevant to your brand and show how your brand stacks up against competitors in terms of share of voice on these prompts. Simply put, GEO is about creating and curating content to organically influence AI search results in your favor. GEM, on the other hand, is advertising to users via paid placements within AI search results.

Now, the best GEO platforms think like their users and can automate the step from insight to action by allowing users to create prompt-related content within their platform. There are clear winners who have built full-fledged operating systems for marketers in the AI era, as well as a long tail of glorified vibe coded tools. Need a refresher on GEO? Check out #57: Chronicles of the Generative Internet.

As ChatGPT advertising begins to take shape, and as ad placements within Amazon’s Rufus and Google’s AI Mode continue to evolve, it remains unclear what AI ad inventory will ultimately look like for brands. Much is still up in the air, including ad formats, pricing, and the degree of control brands will have over prompts within ChatGPT. My guess is that it ends up resembling the following screenshot, which Amazon shared to illustrate how it envisions in-prompt advertising evolving.

If ChatGPT opens a similar advertising portal that lists base prompts and activity, GEM effectively becomes an exercise in identifying which prompts are most relevant to your brand and how material each one is. In practice, this looks a lot like GEO, except you are paying for placement.

Getting ahead in GEM will require a deep understanding of how to rank and maintain your brand’s perception within AI search results, as well as how to adapt content strategy to the specific model you are optimizing for. If I were working at an advertising agency today, I would want to get very smart about how GEO works, as its mechanics will likely translate directly to GEM.

As the AI chatbot advertising landscape matures, I expect a similar ecosystem to form, made up of agencies that manage AI ad budgets on behalf of brands. Since some GEO platforms already pay for private data access from foundational model companies, they will have both the partnerships and insights needed to offer GEM services. Their existing strengths in sentiment tracking, prompt sourcing, and model-specific optimization will put them well ahead of non-GEO players.

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Hot Take #3

By the end of the first half of 2026, advertising within Amazon’s shopping assistant, Rufus, will be a smashing success and validate AI shopping assistants as a highly effective acquisition channel.

So far, Amazon has chosen not to rely heavily on external foundational model partnerships in the same way others have, and it has been explicit about blocking third-party AI chatbots from accessing its website. Heck, they even sued Perplexity for finding a backdoor agentic shopping method through their AI-native browser, Comet. Amazon holds a clear competitive advantage through ownership of the customer relationship and deep vertical integration, spanning acquisition through fulfillment.

Note: Amazon does have a substantial investment in Anthropic and leverages Claude to help power its internally developed LLM, Amazon Bedrock.

Pure speculation here, but Amazon likely believes it does not need additional partnerships. Instead, it has been building a walled-off agentic commerce environment through Rufus, which appears to be gaining traction based on what the company shared in its Q3 earnings. As Amazon rolls out the ability to tactically target Rufus prompts within Ads Campaign Manager, brands will begin to see just how much traffic flows through Rufus and will adjust their bidding behavior accordingly.

Once Amazon becomes more transparent with AI advertising data, which I expect to happen in the first half of the year given its willingness to preview the prompts section in Ads Manager, brands will catch on quickly. A material portion of ad budgets will shift toward Rufus. While cost-per-click (CPC) will likely increase, return on ad spend (ROAS) should rise as well, driven by higher intent, clearer prompt-level insights, and improved conversion rates as Amazon continues to funnel customer attention toward Rufus. They have the data and a high-intent shopper. If someone can figure out AI advertising, it’s Amazon.

We’ve discussed the concept of advertising within AI chatbots several times at Relentlessly Curious, most recently detailing why advertising was the next likely business opportunity for OpenAI in #62: OpenAI’s Monetization Roadmap. Given the sheer amount of capital that OpenAI has raised, it needs to pursue trillion-dollar industries. They are currently checking the “commerce” box with their Instant Checkout rollout and appear to have plans to enter another trillion-dollar industry: advertising.

But I have doubts about OpenAI’s ability to create a successful advertising product in the near term, being roughly the next twelve months. By contrast, Amazon is much better positioned to create the ad product that OpenAI aspires to achieve.

In theory, advertising within AI chatbots should be an effective acquisition channel. Foundational models like OpenAI have plenty of historical context on your lifestyle habits and specific preferences, and they can use this data to target you at the right moment with the right product. The goal is the right place, right product, right time. You type in a prompt and ChatGPT provides an answer, with a small “sponsored” section at the bottom.

Thanks to ChatGPT’s nearly 900 million weekly active users, OpenAI has no shortage of data points to provide a user with hyper-specific advertisements. More precisely curated ads should translate into higher conversion rates on these ads. The combination of hyper-specific audience targeting and high conversion rates is a unicorn scenario.

As a result, OpenAI should expect to command a high price for this precious advertising placement due to the intersection of a curated audience and a contextually relevant moment. A common benchmark for advertising prices, cost per mille (CPM), islikely to be priced quite high because of these dynamics. An AI chatbot is direct response advertising at its most powerful. It’s as good as a marketer can dream up.

However, this optimistic scenario makes a few key assumptions.

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First, it assumes the ChatGPT user is looking to make a purchase. There are plenty of reasons why someone may use ChatGPT beyond looking for guidance on buying something. In September, OpenAI published the “How people are using ChatGPT” report which claims that the ‘Doing’ category of tasks, which represents roughly 40% of consumer usage, encompasses task-oriented interactions like drafting text, planning, or programming.

That’s 40% of inquiries that don’t have to do with shopping. My guess is that when someone visits Amazon’s website, they generally intend to buy something. That is not always the case with ChatGPT.

Moreover, OpenAI claims it will allow users to clear any data used to inform ads at any time, as well as “never sell… data to advertisers.” OpenAI has a treasure chest of relevant data, but it’s up in the air about how much of it they’ll be able to use to show you an ad. This is reassuring from a consumer perspective. At the same time, it raises questions about how relevant the ads can be if OpenAI cannot act on all user data.

Additionally, creating a great advertising product is difficult and challenges the ethos of its core software. OpenAI knows this, and its deliberation suggests both caution and a need for experimentation. Friday’s press release reads almost like an open admission of difficulty and that they won’t ruin their core product with advertising.

Platforms walk a fine line between preserving their mission and serving advertisers. But there is one platform that is pushing full steam ahead in implementing advertising within their AI chatbot. That platform is Amazon.

Amazon’s AI shopping assistant, Rufus, can serve as a benchmark for ChatGPT advertising.

According to Amazon’s Q3 earnings report, Amazon claims that shoppers who engage with Rufus demonstrate a 60% higher conversion rate than shoppers who do not engage with Rufus, as well as generating an incremental $10B in revenue. In addition, roughly 250 million people use Rufus each month. How’s that for adoption?

Rufus seems to be a success so far. But how effective is advertising within Rufus’ interface?

As of now, Amazon does not give brands the option to bid on ad placements within Rufus. Instead, Amazon will sometimes slot a brand’s Sponsored Products and Sponsored Brands advertising into Rufus as a test. Consequentally, this means this means that brands do not have a direct way of assessing ad performance within AI chatbots.

But I’ll attempt to make this corollary: if brands were seeing high returns from Rufus advertising placements, Amazon would separate out Rufus into its own campaign structure and charge brands high rates for the placement. But that’s not happening.

Amazon’s cost-per-click (CPC) costs have not increased astronomically over the last year, despite their Rufus testing. In the Beauty category, Sponsored Products (think ads on a search results page) CPCs averaged between $1.04 and $1.12 in 2025, which is up 10 to 15% when compared to 2024. This is a meaningful increase, but not dramatic, and can be the result of many factors.

Rufus leverages Amazon Bedrock, an internally developed LLM that incorporates billions of product data points, decades of sales history, and many millions of customer reviews. They are much better positioned to build a high-converting advertising product than OpenAI since they have an endless supply of commerce-oriented data.

Rufus may be effective at organic recommendations (as per the financial statistics that Amazon shared), but perhaps the advertising placement within the chatbot is not quite right. Whether customers are less interested in engaging with ads inside a chatbot (existential issue) or the targeting just isn’t there yet, I would expect Amazon to be far more vocal about its AI advertising success if that was the case.

Anecdotally, I’ve found its recommendation engine a mixed bag and its understanding of what is already in my cart suspect (recommends products that I’ve already added to cart). So even Amazon, with such a tremendous swath of commerce-focused data, hasn’t built an AI chatbot advertising product at scale yet.

My bet is that ChatGPT’s advertising product will initially struggle and will need to be inexpensive at least in the short to medium term to incentivize brands to test it out. That said, I am bullish because, as a consumer, if I am going to see ads, they might as well be as relevant as possible.

What does this mean from a user perspective? Customers will be able to provide payment details, submit discount codes, and set up a subscription all within their prompt inquiry. So, the next time you ask Gemini to, “find and buy running shoes in size X that meet Y requirements”, you don’t have to leave Gemini to check out on the brand’s website. AI chatbots are becoming a true one-stop shop.

Need a refresher on agentic commerce? It’s when AI agents buy things on behalf of people. We give AI agents instructions on what we are looking to buy, along with conditions around price, features, and payment information. Then, an AI agent makes the purchase on a person’s behalf. These types of purchases can be triggered by a prompt we write to an AI chatbot like Google’s AI Mode, and they can also be triggered by a set of predefined guardrails (a workflow) that we’ll unpack in the morning wake-up scenario below.

“Your alarm clock goes off at 7AM. You immediately regret going to bed late last night and choose to hit the snooze button.

After nine minutes go by and another ring goes off, you check your phone to see a weak sleep score on your Whoop app.

Ping! You get a few more notifications on your phone coming from Instacart, Starbucks, and Peloton.

Instacart adjusts your grocery delivery order today adding more fruits and vegetables since your sleep score was low, and your Starbucks order changes your typical latte to a red eye since you are dragging. Then Peloton swaps your heavy-lifting session to cardio and stretching as your body’s strain level is too high for the former. You’ve provided pre-approval on spend limits, so the apps can work in sync with each other and execute transactions so that you can be optimized in the way that you prefer.”

Agentic commerce is nothing new here in Relentlessly Curious land. The excerpt above is from last May, when we chatted about a world where AI agents make decisions for us in When AI Buys Your Groceries.

The idea of AI agents acting on our behalf has been thrown around for a while. However, Shopify’s announcement (strategically timed on the first day of a massive retail conference, NRF 2026) is a key milestone in laying the foundation for the scenario imagined above.

What Shopify has been to websites, it is attempting to be to agentic commerce. For the past 20 or so years, people have visited websites to ultimately make a purchase of a good or service.

But expect to see a behavioral shift in the short term. How people discover information is already fundamentally changing (think less reliance on Google Search and more reliance on AI chatbots), and shopping is the next big habit to change.

The Shopify and Google partnership represents the ability for any brand (whether they are using Shopify to power their website or not) to have a shoppable catalog for AI agents. It creates the protocol for how a brand’s catalog is read and ingested by AI agents, so that the proper information is communicated to the person (or AI agent) prior to making the purchase.

This screenshot is from the press release. The key callout is the list of names under “agentic storefronts.” None of these are “[insert brand].com”.

I believe Shopify is putting so much effort into establishing a protocol for how AI agents shop across the web because its business model faces an existential threat if people begin buying from AI chat interfaces (i.e., agentic storefronts), and they don’t have a piece of that business. Maybe not today, but perhaps three years from now. If people stop visiting websites, there’s less reason for a brand to put so much effort into maintaining their website and thus paying high subscription and transaction fees to Shopify. Shopify knows where the puck is going and is building the next generation commerce infrastructure to stay on top.

The difference is, shoppers will come in the form of both people and AI agents. And that’s what all the fuss is about regarding the UCP news. Agentic commerce requires a retooling of the existing front-end and back-end e-commerce experience to support both people and AI agents making purchases.

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A few thoughts.

Understanding customer intent will be an incredibly tricky exercise.

You type in, “Buy me new running shoes at a price below $150.”

This is a vague prompt to tell an AI agent. There is a nearly infinite number of outcomes the AI agent can come up with to satisfy your running shoe request. Agentic commerce will need to have the proper guardrails for keeping a human in the loop to provide the necessary information prior to taking an action. In this instance, the size and width of the shoes, color, style, intended use (long-distance or track running), and climate should be filled out before the agent begins its research and makes a purchase.

And say a customer doesn’t like the shoes the AI agent purchases on their behalf. Do they take this up with the AI agent or do they email the brand asking to return the product? Customer service is another nuanced element of the commerce experience. I didn’t see any post-purchase information in Shopify’s announcement.

Building intent-based models to help refine the prompt inputs feeding agentic commerce will be key to limiting disruption of the buying process.

Brand becomes everything and nothing at the same time.

Websites can be an excellent educational hub for a brand. But if customers don’t make it to your website because they checked out within an agentic storefront, they are less likely to learn the brand story or understand the full assortment.

That poses a major problem for brand loyalty. Customers will put their trust into what ChatGPT recommends. That means that unless they intentionally seek your brand out, you’re at the whims of whatever ChatGPT recommends.

And I haven’t mentioned what happens when AI agents act on your behalf to make a purchase. If you don’t specify which brand you’d like to buy from, brand may as well be meaningless in the eyes of an agent.

I believe agentic commerce will force brands to be built offline, or they’ll have to become top notch pupils of the generative engine optimization game.

How your brand is perceived and how frequently it shows up in AI search results will be critical for agentic commerce success.

Practices like generative engine optimization (GEO) will become (if not already) a key strategy for brands looking to improve their visibility in AI search results. With Google Search’s growth rate slowing and AI platforms like ChatGPT seeing nearly 900 million weekly active users, AI search is an acquisition channel you can no longer ignore. Creating content that’s easily indexable and retrievable by LLMs (proper schema markup, FAQ pages, etc.) and maintaining a presence on relevant LLM data sources for your brand (i.e., Reddit, YouTube) will take up a larger mindshare for growth marketers. You’ll need to make sure you’re marketing to AI agents, which is a different strategy than marketing to people.

Agentic commerce disrupts the priority of owning the customer relationship.

In either OpenAI’s Agentic Commerce Protocol (ACP) or Shopify’s and Google’s Unified Commerce Protocol (UCP), the brand will maintain its status as the merchant of record. This means that they receive the opportunity to collect email or phone number information, as well as transaction data.

But sending a customer a slew of emails suddenly becomes less relevant when they are going to ChatGPT or Gemini for guidance on what to buy. Continuing this tangent even further: if a person delegates buying decisions to an AI agent, the emails that land in that person’s inbox are essentially irrelevant, even if they have been opened or not.

Savvy marketers will still find a way to generate value from an email list, and transaction data is critical to better understand the business. But email as sales and retention channels will likely become a lot less relevant.

When I say I’m “building an app”, I’m vibe coding. For those who are not familiar, vibe coding is essentially creating software by typing words rather than traditional code. You write out instructions in natural language prompts for a large language model (LLM) to interpret and produce an output. You can vibe code a video game, a web scraper, or even a mobile app.

Vibe coding empowers those who are either curious or creative, and willing to take action to turn their idea into reality. It lowers the barrier to entry for creating software. You guide AI on the idea, providing close guardrails on design, taste, and structure.

With that said, I view vibe coding as a process to build mock-ups or prototypes. Software created through natural language prompts is unlikely to have proper security by default (think if you need to store a user’s payment information) nor infrastructure to handle user activity on a large scale. However, it can be an excellent way to communicate your ideas to colleagues, giving them a clearer vision of what you’d like to build.

Vibe Coding Introduction

App/Website: Low-FODMAP Guide

Tech Tool: Replit

Between Christmas and New Years’s Day, I created a resource for those on the low-FODMAP diet. It’s a very restrictive diet during the elimination phase and those that adhere to it are conscious of what they eat, as well as the serving size of each food they consume.

The website I created lists over 600 of the most commonly consumed food (i.e. fruits, vegetables, grains), detailing whether they are safe to eat and their acceptable portion sizes in adherence to the low-FODMAP diet. Additionally, the app has a low-FODMAP recipe generator and a menu analysis section to let you know whether a dish you’d like to order adheres to the strict protocol.

For what it’s worth, I am not on the low-FODMAP diet. But I am aware of how restrictive it is and the sometimes-conflicting sources of information that exist. Thus, I figured I would look to build an interface that could be helpful for someone who is on this diet.

Check out the website in the link above. I’m still in the process of debugging, so you may come across errors as you play around with the functionality. But that’s part of the process of building software!

So how did I do this?

I used Replit, a popular vibe coding platform. I’ve built web scrapers, nutrition trackers, and analytics tools with Replit before. You can also use platforms like Lovable, or Claude and Gemini assistants to vibe code.

How did I start?

Before I entered a single word into the Replit prompt bar, I first outlined what I wanted to build, who I wanted it to be for, ideal user experience (UX), look and feel, and core functionalities.

This sounds like a lot of work to do before hopping into the platform, but it’s worth it. Going back and forth with the Replit (or any vibe coding platform) chatbot is time consuming and expensive (most platforms charge you by credits used). You’ll build a disjointed spider web on both the front-end (user-facing) and back-end if you don’t provide an outline of what you want. Let’s dive into the requirements document that you’ll feed to Replit initially.

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Summary

This section tells AI what you’re looking to build. Be direct and to the point. Consider it your value proposition.

The low-FODMAP diet guide website will serve as a web-based application to help people observing the low-FODMAP diet. The tool will show which foods and their respective portion sizes are safe to eat, as well as which foods are unsafe to eat.

Target Audience

Specifying your target audience will educate AI on the intended user, which will help inform the first iteration’s look and design, as well as curate information in a way that appeals to this user profile.

Primary Audience: New dieters to the low-FODMAP diet. Focus on those who were recently told that they will need to start the low-FODMAP diet or those who recently began the diet.

Secondary Audience: Family members or caregivers of the person on this restricted diet.

UX & Design

Be specific on the color scheme and emotion you’d like to evoke from a user when interacting with the application. Make sure to add details around sizing for both mobile and desktop.

Minimalist, sleek aesthetic, with bright (yet soft) colors. The primary goal is to establish domain authority and trust with the user, but in a friendly way. The website should mimic the persona of your very smart, kind friend who happens to know a lot about the low-FODMAP diet and wants to get you up to speed on the diet.

Make the app fully responsive across mobile and desktop using a mobile-first approach. Ensure no text or UX elements are cut off, layouts adapt fluidly, and spacing and typography scale appropriately across screen sizes.

Core Functionalities & Pages

This is where you’ll need to put in some thought on what you tactically want to deliver to your user, as well as how they’ll interact with the functionality. See this as a first pass. You will end up iterating functionality, as well as UX after the first version is created.

Home Page

This is where the searchable food database should live. Each food should be visible in a card format that highlights the level of adherence to the low-FODMAP diet, portion size (if relevant), variety of food (if relevant, think through a type of squash or a type of bread), source of information, and date information around when the source was last updated. Choose the top ~600 most popular foods to serve as the foods in the database. Tag each food to a “category”.

Food Categories

• Fruits

• Vegetables

• Grains

• Dairy

• Proteins

• Nuts & Seeds

• Condiments

• Beverages

Food items should be searchable via a real-time search bar that filters the food item database, as well as being able to be manipulated by a Food Category filter.

Also, each food item should be classified into one of the following diet categories.

• Safe

  • Green text

  • Safe to eat in regular portions

• Limit

  • Yellow text

  • Can eat in small portions (specify the portion)

• Avoid

  • Red text

  • Completely avoid (can’t eat)
    

Source of Information for Food Items

Not only is it important to tell AI what you’d like to build, but it’s also crucial to specify what it should not build. You’ll see that in the sourcing section below.

Source information and diet categorization from Monash University. If information is not available from Monash University, please source from another reputable organization in the low-FODMAP realm. Examples include the Cleveland Clinic, King’s College London, or Clinical Consensus. If the source of information does not exist amongst these four options, please disregard the food from the list.

Create a confidence scoring system that falls into either “High”, “Medium”, or “Low” based on how confident AI is in its recommendation for each food’s categorization in the low-FODMAP diet. This should reflect source strength, not medical certainty.

Important Information Rules

• The website/app is not a medical professional. Always recommend consulting a dietitian for personalized advice in any open-ended response (i.e., in Recipe Helper)

• Never provide specific medical advice or claim certainty about food not in the database, particularly in an open-ended response

• If you’re uncertain about food categorization, say so clearly and recommend caution

• Focus on general FODMAP principles, not specific diagnoses

Definitions Page

This page should have a button at the top right of the Home Page that leads to a glossary of terms related to the low-FODMAP diet. This is meant to serve as an information hub for those less familiar with the diet. Make sure to source information from medical organizations and cite the source below each term.

Provide a search bar at the top of the page for a user to look for a term in the glossary they had in mind.

Recipe Helper Page

This page will be accessible via a button on the bottom right of the Home Page. It will consist of ChatGPT-style prompt bar and nudge the user to ask for low-FODMAP recipes based on the foods they have in their kitchen. Users will receive recipes after submitting their prompt that are low-FODMAP friendly. Do not include any ingredients in these recipes that are not friendly. Consult the food item list before providing an answer.

With that said, please be creative and encouraging. Suggest flavor combinations, cooking techniques, and ways to make meals enjoyable while staying FODMAP-safe. The website/app is not a medical professional, therefore always recommend consulting a dietitian or doctor for personalized advice.

Please use OpenAI integration with Replit to be able to ideate recipes for the user (I can authenticate the OpenAI connection). Increase creativity/temperature of OpenAI’s responses, but do not sacrifice medical integrity. It is essential to not provide incorrect guidance on the low-FODMAP diet to the user.

Success Key Performance Indicators (KPIs)

It’s helpful to provide AI with how you’ll measure success, as you’re giving the AI a benchmark to check its own work against.

• Average time on the website/app: > 90 seconds per session

• Usage: >2 food item searches per session

• Retention: >40% percentage of users returning to the website/app within 14 days

Furthermore, I added functionality around helping users when they’re thinking through whether a dish at a restaurant is diet friendly. But at this point, I think you will get the gist of how to write the product requirements.

Upload your version of product requirements to the prompt bar of a vibe coding platform and let the magic happen!

A few tips:

Whether you successfully turn your idea into reality will likely be determined by how curious and relentless you are (yes, pun intended). Oh, and patience is key too.

Vibe coding can be incredibly frustrating when you can’t get AI to create what you have in your head. Although I consider myself proficient in SQL, I am no software engineer by any stretch of the imagination. So, I need to be curious and ask many questions about how certain functionality was built to help think through fixes.

Tell AI what you want and tell it what you don’t want.

I’m repeating myself from earlier in the article for good reason. AI will make a lot of assumptions around your vision, and you need to narrow the scope by implementing constraints. You’ll go off the rails quickly in the design if you aren’t clear up front.

Ask for help.

I regularly ask Replit to explain the changes it made. When I don’t like the output I receive, I ask ChatGPT to tweak my prompt and provide feedback. Use AI to help you use AI.

There’s plenty more to share, but I’ll leave you with just one more thing. To quote Stephen Covey from The 7 Habits of Highly Effective People, “start with the end in mind.”

The growth of Relentlessly Curious has been both humbling and energizing, a reminder that there’s real appetite for clear thinking amid the noise and for curiosity that’s practical, not performative.

In 2026, topics will continue to explore the human interaction between business, media, and technology. And yes, expect plenty of AI along the way.

I’m hitting the pause button for the next two weeks. In place of a typical Relentlessly Curious piece, I’ve pulled passages from some of the highest-engagement articles this year.

Thank you to everyone who read Relentlessly Curious this year. I’m grateful for your continued readership. See you back on Tuesday, January 6th, for the first Relentlessly Curious edition of 2026!

Evaluating AI’s Impact on the Customer Service Industry

Publication Date: November 18^th

Tackling the consumer side (B2C) of customer service automation is tougher as there is a theoretically infinite number of inquiries for businesses of all types and sizes. It’s difficult for a company without a swath of data and global distribution to start training an AI agent that can represent consumers. And that’s not even to mention the sheer capital needed to turn all this data into insights. In summary, B2C AI customer service can be best supported by the foundational layer (possess the resources and expertise), while B2B AI customer service (niche, proprietary data sets) is a better fit for the application layer.

Yet, you know who has a tremendous amount of data, expertise in AI, and global distribution? Google. In the grand scheme of things, the acquisition of hyperlocal data is an ancillary benefit. I believe Google introduced “Let Google call” as a first step at redefining the consumer side of customer service.

What makes the “Let Google call” product launch even more impressive is how it bridges AI agents across the online and offline worlds. Plenty of businesses barely have a website, so why not have AI do the legwork and reach out to these businesses in their preferred communication method? Over the phone.

Risks of Consumer-Facing AI

Date: November 11^th

Whether brands realize it or not, AI doesn’t just support the brand: it becomes part of it. Most brands are not technically qualified to develop or represent AI tools. When customers receive poor recommendations, trust erodes, and they may switch to competitors. It’s not natural for a consumer to verify a brand’s guidance, nor should it be their responsibility.

If ChatGPT recommends a serum that doesn’t work, you might shrug it off, thinking, “It’s ChatGPT, it sometimes gets it wrong.” Tools get more slack than brands when it comes to wrong answers because it’s their business to move quickly and build innovative technology. Some screws are bound to fall off in the process. When AI tools fail, we shrug. When brands fail, we are likely to switch.

The Case Against Instant Checkout

Publication Date: October 14^th

Can OpenAI get 35M people per week to spend $50 and how long will it take them to get there? But most importantly, where is this sales volume (gross merchandise value, GMV) going to come from?

OpenAI currently has an Etsy integration in place and Shopify is soon on the way. Etsy as a marketplace puts up ~$12B in GMV per year, and Shopify reports ~$300B in annual GMV. By my estimates (which could be wildly optimistic, please comment below what you think), Instant Checkout will receive about one third of these massive platforms volume. Will there be a pure share shift of demand from shopping on brand websites to shopping in a prompt format? Or will new demand be created?

In terms of demand generation, I’m dubious as it’s not like people just suddenly have more disposable income because the user experience is cleaner. Remember, 10% of US consumers make up 50% of overall spending. I don’t know how much more we can squeeze out of the top 10%.

Why GenAI Isn’t Supercharging Businesses

Publication Date: August 22^nd

Furthermore, it’s critical to define what “GenAI” means for each business. Are executives viewing it as, “let’s get everyone a ChatGPT account?” or are they seriously thinking about how each function can become more operationally efficient in a silo, as well as a cohesive machine? The former is helpful but unlikely to lead to a material revenue stream as there isn’t an enterprise strategy of how automation leads to top-line growth. The latter is strategic yet requires patience, time, and an attitude shift towards innovation.

There’s another reason why companies aren’t seeing huge revenue growth thanks to AI. And it’s rather simple. AI isn’t good enough yet.

We discussed in Are We Ready for Everything to be Automated? that the technology isn’t at a point where businesses can consistently get 100% accurate answers. Particularly in the way of LLMs like OpenAI and Perplexity. They are looking to create broad-sweeping solutions that appeal to everyone, not to your very specific use case. LLMs have raised enormous amounts of capital, so they need to tackle major industries at large like advertising, commerce, and software engineering. With that said, they’re more focused on providing a solution that is “good enough” for your specific use case. The problem is, 80% correct doesn’t cut it when you need 100% correct.

The vertical layer of AI presents a tremendous opportunity for value creation because of the need for companies to go from the 80% that LLMs will provide to 100% correct. That extra 20% is incredibly valuable and companies will pay dearly for it.

Why AI Companies are Licensing Publisher Data

Publication Date: July 15

I’m curious if we’ll see a wave of front-end (chatbot) licensing deals emerge to strengthen recommendation systems. Think of how helpful all of those “My Top 10 Skincare Products” or “What Type of Grill Should I Buy for Summer BBQs?” articles will be in matching shopper intent to products. Ask Rufus for a breathable golf polo, and it’ll likely pull from a “Top 10 Golf Shirts in 2025” article.

Not only are these articles relevant to the transactional intent of someone using Rufus, but they also have clean metadata for AI companies to train on. Publishers should lean less on how many people read their site, and more on how many algorithms crawl it. Sure, this clashes with the ethos of journalism. But times are changing as publishers need to realize that AI is a growing share of their customer base.

Target, Walmart, and other retailers will likely launch their own chatbots to guide purchases, and they’ll need publisher partnerships to train them. The business case for integrating media (publishers) with commerce (retailers) is so clear and one that the publishers will need to hold on as tight as possible for the sake of survival. Commerce media is thriving, and monetization models and deal structures are evolving fast.

Why Brands and Consumers Love Erewhon

Publication Date: June 10^th

A boutique market shopper is sophisticated, prioritizes what goes into their body, and has the disposable income to choose healthier, cleaner products. They view the extra cost (compared to Wegmans or Trader Joe’s) as an investment in their health, so of course they would pay extra.

Taking a trip to a boutique market is a more emotional grocery shopping experience than strolling into Trader Joe’s. You’re likely to find brands and products you aren’t going to find anywhere else, all emphasizing health and wellness. These markets make you feel healthier when you walk out, even if all you do is gawk at a 1.5-ounce vitamin C shot for $8.

When AI Buys Your Groceries

Publication Date: May 13

Say you want to make lasagna for dinner tonight but don’t know which ingredients you’ll need. It’ll soon become instinct to ask ChatGPT (or Claude, or Perplexity) what you need from the grocery store to make lasagna. The LLM output will give you a specific grocery list and a recommendation for picking up the ingredients at nearby stores. Or if you want to really automate the process, ChatGPT will communicate with your Instacart account to add the ingredients to cart and then make the grocery purchase for you (of course, using your credit card details).

Boom, lasagna ingredients delivered to your door 30 minutes later. Type in another prompt and you’ll have a detailed recipe walkthrough too.

The lasagna example is just one way AI makes commerce more efficient. No endless browsing or guessing what you may need. One clear list of ingredients and you didn’t need to jump from website to website or tab to tab on your phone or desktop. The experience happened all within your ChatGPT app.

How Credit Card Companies Manipulate You at the Airport

Publication Date: March 21

That’s how they prime you. Airlines and credit card companies want you to have a positive association with travel as the data shows you spend more when you are on the go. So, their goal is to get as top of mind as possible as you are starting off your trip, which may lead to you using that same credit card throughout your travels. Fancy dinner? Museum tour? Train from London to Paris? Throw it on my Amex. I’ll get the extra membership rewards points so I can book my next trip to Mexico.

And in some ways, they physically prime you for your card to be top of wallet as well. Ever realize that the lounge front desk asks you to see your credit card? They need to verify you but that’s likely going to be the last time you touch a card before you land at your destination. And you may have put that credit card at the top of your wallet. This nudge is thought out way before you stepped into the airport.

In a lot of ways, airlines are marketing companies that happen to have planes as assets on their balance sheet. They market all the ways you can spend your money on travel and have revenue share agreements with credit card companies to make a buck on every part of that cobranded spend.

Hot Take #1:

Shopping on AI platforms like ChatGPT will lag in adoption because AI is optimized for awareness, not conversion.

When you read “Amazon.com”, what do you think? It’s likely that your first (and second) thought has to do with “shopping”.

People associate Amazon with buying stuff. You can buy truly anything on Amazon’s marketplace as they seem to sell everything under the sun. When you go to their website, you’re either considering making a purchase or planning to make a purchase. With that said, you may go to Amazon to browse, read product reviews, or build a wish list for a later date. But all of these activities are shopping-adjacent.

But when you hear “ChatGPT”, what do you think? Your first thought probably isn’t “shopping”. I believe that will be a problem for OpenAI as they roll out their Instant Checkout feature in the coming months (expected based on news releases). For those looking for a refresher on Instant Checkout, take a peek at #61: The Case Against Instant Checkout.

Back in October, I presented a case that the vast majority of Instant Checkout transaction volume will be from the existing e-commerce players (thus unlikely to materially increase consumer spending). This time, I’m stating that when people go to ChatGPT, they plan to do plenty of other things besides shopping.

According to research conducted by Profound, roughly 6% of ChatGPT prompt volume is transactional in nature. Now, everyone’s definition of what implies “transactional intent” may vary (and the statistic is relative to the data set Profound analyzed), but the point ties in nicely with the ChatGPT use case report. People go to ChatGPT to discover information or help complete a task (not only guidance on what to buy). More on the ChatGPT use case point later.

See, people go to Amazon to make a purchase. The singular focus allows Amazon.com to be optimized for conversion, of which ChatGPT does not have that luxury. It’s a product positioning issue, not a user experience issue as the wireframes for Instant Checkout seem clean and shopper-friendly.

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Hot Take #2:

In 2026, not all ships will rise with the “AI tide” as they did this year.

Note: None of the below is financial guidance.

In 2025, if you held a hefty allocation of Big Tech stocks in your portfolio, you did well. Like, really well.

I don’t see the AI tide lifting all ships in 2026. With so much run-up in valuations, steep P/E ratios, and circular financings in the Big Tech realm, I imagine there will be events we can’t foresee today that will knock some of 2025’s winners off their pedestals. Consider that we may see continued rate cuts (as the next Federal Reserve chair will very likely be Trump-approved), which subsequently means that there will be fewer monetary policy tools for the US government to use if inflation starts to run away.

I recommend investing in low-cost index funds. On a long enough time horizon, neither you nor I will beat the market. But if you had to ask me what I’m watching, here are two themes.

Power

Energy generation, storage, transportation, and technology (i.e., cooling or grid modernization). The more we rely on AI, the more energy that is needed to power the critical technology. With hundreds of billions (announced) of Big Tech capital expenditures in 2025, capital will continue to be put to work to support the infrastructure build-out of AI. Invest in the picks and shovels companies, which I believe is a safer play.

China-based Foundational Model Companies

A bit contradictory to my previous take about investing in picks and shovels companies, yet I believe it’s worth examining the current state of AI in China.

When DeepSeek was released back in January, NVIDIA’s stock was rocked on the premise that fewer semiconductor chips are needed to create large language models (LLMs). Although the market (and NVIDIA) soon recovered, plenty of LLMs have been popping up in China at a fraction of the cost and reasonably similar performance compared to US-based models. Moonshot AI’s Kimi K2 and Alibaba’s Qwen are some of the leaders in open-source LLMs, with Airbnb’s CEO boasting that they are relying heavily on Qwen for Airbnb agent performance.

Investing in China-based companies has its own risks regarding foreign investor claims and geopolitical uncertainty. But current valuations (both public and private markets) suggest that the US will completely own the global AI story. I see that as unlikely given China’s proven ability to create low-cost, strong performing models.

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Hot Take #3:

Outsourcing low-stakes decision-making to AI may erode your high-stakes decision-making abilities.

Maybe it’s just me, but I’ve been increasingly aware of how frequently I ask AI (mainly ChatGPT or Gemini) for advice lately. Earlier this year, I stuck to only asking trivial questions, like what to order at a restaurant. However, as I got more confident in my prompt writing and model understanding, I began to treat it as a true copilot or advisor, asking anything and everything, from innocuous questions to more meaningful ones about life and work.

Although I’m generally satisfied with the output I receive from AI, I do recognize that I’m slowly developing a crutch. Instead of making decisions based on either my own research or intuition, I’m outsourcing this process to technology. A very intelligent technology, but it’s only as wise as the data it’s trained on. And that data excludes many years of personal context that AI does not have access to.

For better or worse, my reliance on AI isn’t uncommon. OpenAI published a report back in September outlining how its users leverage ChatGPT. They broke out uses into three distinct categories: “asking”, “doing”, and “expressing”. Turns out, 49% of prompts fall into the “asking” category, in which users are asking ChatGPT for some sort of advice.

When I ask AI to execute a task for me, my internal dialogue is usually, “I’m glad I can delegate this task, but I’m also glad that I have the knowledge and experience to actually complete the task if I need to.” That provides me with a sense of relief and resolves some guilt about relying on AI to do things for me that I really could do myself.

Have I become more efficient and effective thanks to AI? Absolutely. But in 2026, I’m going to make a conscious effort to still make sure there is productive friction in my decision-making process. When it comes to bigger, more complex decisions, I want to make sure I have the necessary reps on things that are less consequential. Low stakes reps help strengthen high stakes judgment.

It’s unlike Big Tech to publicly acknowledge how strong another’s models are. Which means that the Gemini 3 update is worth paying attention to.

Now Gemini 3 has several cutting-edge features, however in today’s edition, I’d like to draw attention to the one with the silliest name: Nano Banana Pro. Don’t let this cutesy name distract you. Many regard its image generation ability as the best on the market given how realistic its output is.

Personally, I’ve played around with Nano Banana Pro and Flux (another image generation model) and believe that Nano Banana Pro is far and away the best image generation when it comes to realism and fewer hallucinations. It’s also a big upgrade from the original, Nano Banana, which was trained on Gemini 2.5.

Head to Gemini to check out the new feature.

Let’s try out this first prompt:

“Selfie taken by Michael Jordan with LeBron James hanging out on the golf course at sunset”

All five fingers seem to be present. Good sign. Let’s try another one.

“Create an image of sunrise in West Village, NYC (40.73 N, 74.00 W)”

(Yes, I’m giving it coordinates to finetune the location)

Realistic street corner at sunrise. But as a former resident, I don’t recall that awning. Still, impressive image.

Like Whey protein?* So does Jalen Brunson.

“Jalen Brunson holding an Optimum Whey Protein container during a Knicks pregame warm-up”

*For Jalen’s sake, Google includes a Gemini Watermark at the bottom right of images to depict that it’s AI-generated. However, this watermark can be removed with 3rd-party tools. What can’t be removed is an invisible watermark that Google places on each photo, keeping the AI fingerprints on their creation.

These images were created from simple prompts. Pro tip: provide camera lens settings and you can really customize your image to your vision.

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I’m sure your mind is racing thinking about the impacts Nano Banana Pro can have on every industry. Let’s dive into a few examples.

E-Commerce

As someone who works in e-commerce, I’ve been searching for the past 18 months for a half-way decent image generation tool. To date, even the most impressive tools (Flux, Flair, Midjourney) have shown clear faults. From an extra finger to bizarre facial features and misinterpreting my prompts, AI image tools always carried an asterisk with them.

I’ll tell you what: Nano Banana Pro is running around with a much smaller asterisk. It’s not perfect. Sometimes I’ll have trouble editing text from one image to another, as well as seeing aspects in output that didn’t align with my prompt. But when the photo comes out as I had hoped, I’m mesmerized that it wasn’t taken by a person. I feel like I’ve witnessed magic.

Nano Banana Pro disrupts both traditional photoshoots for website content and advertising. If you start with a high-quality rendering of your product, the world is your canvas in terms of the types of settings, backgrounds, and use case demonstrations you can create for your product.

This technology accelerates the amount of experiments a brand can conduct. Photoshoots are a costly bottleneck, straining financial budgets and slowing down pace. Now with Nano Banana Pro, brands can test many different iterations of copy and content for an ad on social media or the main image on their product page. This workflow allows teams to get to the best possible answer quicker.

Film

If you’re working on set in Hollywood, you must be sweating right now. That is, if you haven’t already been sweating as the streaming business model and now AI have turned the film industry on its head.

With Gemini’s Nano Banana Pro, it’s not difficult to imagine a world where fewer people are needed to make movies. Casts and crews will be a fraction of what they once were as AI will continue to pave the way, reducing the cost and increasing the pace of production.

What’s crazy to think about is that today is the worst image and video technology is. It will only continue to get better. The issues we see today will likely be fixed within a year, if not months. What I’m curious about is when we’ll be able to create TV sitcoms and movies from simply writing prompts. I don’t know if that’ll catch on, but

More content, more creation. But fewer jobs at film studios.

Architecture

Architecture is admittedly outside my comfort zone, but even from the sidelines it’s obvious how big this shift could be. Today, architects rely heavily on CAD tools and manual rendering work to translate sketches into something clients can visualize. With models like Nano Banana Pro, that translation layer becomes dramatically faster. A 2D floor plan or rough sketch could turn into a photorealistic, explorable 3D space in minutes instead of days.

That doesn’t replace architects, it just removes some of the most time-consuming production work. I expect new software players to emerge that focus on prompt design, workflows, and templates specifically for architecture firms, all built on top of Nano Banana Pro. As we’ve talked about in past editions, the biggest opportunities usually sit at the vertical application layer, and architecture is poised for that kind of tooling.

Education

If you’re a visual learner stuck with dense reading assignments, Nano Banana Pro changes the game. You can upload a confusing passage and instantly turn it into a diagram, timeline, or even a quick explainer video tailored to your learning style. It removes the “I don’t get this because of how it was taught” excuse.

Google’s Notebook LM adds another layer by acting as a research partner: it can turn notes into summaries, study guides, or a podcast-style explanation of a topic. Put together, these tools make it far easier for teachers to build custom learning materials and for students to grasp complex ideas quickly.

What this means for education is complicated. The accessibility boost is undeniable, but it also raises new questions around overreliance, academic integrity, and how educators adapt. Still, the direction is clear: learning will become more personalized, more multimodal, and less constrained by traditional formats.

Last week, OpenAI and Perplexity launched personal shopping assistants just in time for peak holiday gifting season. OpenAI introduced ChatGPT’s Shopping Research, and Perplexity announced Shop with Perplexity.

I highly recommend checking out these shopping assistants, as I find them helpful and a peek into the future of commerce. Primarily, ChatGPT’s Shopping Research can be found by clicking the “+” in the prompt bar and selecting the “Shopping Research” option. Let ChatGPT know what you’re looking for, with a prompt like, “I’m looking for the best running compression pants for cold weather in NYC this winter.” After pressing enter, you’re presented with a series of multiple-choice clarifying questions about budget, material, and features. Next, the Shopping Research assistant “thinks” for seconds to a few minutes and then provides a detailed side-by-side comparison of a few product options, as well as a final recommendation.

On Perplexity’s front, they offer a general shopping section (look for this on the sidebar) where you can browse products to buy, as well as a “shopping” option as part of a prompt. There’s no Q&A, but Perplexity does provide a detailed work-up (very much appreciated for my running compression pants inquiry).

As someone who strongly dislikes shopping, I’m impressed by what OpenAI and Perplexity created. One of the reasons I don’t like it is because of the cognitive load stemming from too many options. Ever hear of the Paradox of Choice concept? I’d rather someone who knows my preferences and has subject matter expertise provide a few options to select from.

Both tech challengers claim that you can purchase within their interface, however, I have yet to stumble across a product I could buy on the AI platform. Yet, I believe it’s only a matter of time until shoppable products are widespread.

A few thoughts (in no specific order):

Foundational models* (like OpenAI) are disrupting commerce because the industry is so large. When you’ve raised many billions of dollars (Anthropic, xAI, OpenAI), you need to justify that investment through a mighty monetization plan. And a clear way to do that is to take a big chunk out of a gigantic business. I believe that foundational models will attempt to influence or own the commerce experience in the way of being a marketplace facilitator, as well as an advertising platform. If it may not be a purchase within the model, ChatGPT will tell you exactly which store you can buy it from and you can sure bet there will be an advertisement soon enough next to that suggestion.

*It is worth noting that Perplexity does operate its own LLM called Sonar, but it does not qualify as a foundational model as it was built on top of Meta’s LLaMA. For the more technical reader, Sonar is an LLM fine-tuned on Meta’s LLaMA model.

Here’s the thing: foundational models should be able to provide a more curated product selection or a tailored advertisement because they have your conversational data. Sure, Google search knows what you’ve searched in the past, and your cookies are scattered across the web (which help advertisers piece together where you’ve been to help influence where you’ll go). But chatting back and forth with AI allows the model to get a more accurate sense of who you are and what you may be looking for. In the case of my running pants prompt, ChatGPT knew that I tend to run warm (from a prior conversation about sweating) and suggested pants that wouldn’t be too constricting. It does sound a bit dystopian how AI will know our nuanced preferences, but hey, if this means I’ll reduce the amount of time I spend researching a purchase, I’m all for it. That said, this is assuming users opt to share conversational history, which is typically optional.

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Don’t build a general-purpose AI agent. It must have a specific use case that isn’t disrupting every part of a trillion-dollar industry, as you’ll be competing with the likes of the foundational model companies. There’s plenty of opportunities to build on the application (vertical) layer, however you must possess an unfair advantage or operate in a niche enough market for the foundational model to avoid bothering with disruption. Remember, it’s likely not worth a foundational model’s time to allocate time and resources toward solving smaller problems.

Instead of building an AI shopping agent that can aggregate information and transact on your behalf, build an AI shopping agent that operates within a specific niche. Here’s an example: a beauty AI shopping agent that considers past skincare routines, facial skin analysis (see La Roche Posay’s image-based scan), and even a dermatology consult. After ingesting your personal data (which wouldn’t be available to a foundational model), the beauty AI shopping agent can curate a skincare routine and then purchase those products on your behalf (based on predefined spending guardrails). You can upload various pictures of your face over time, and the AI can assess whether the routine is working.

This scenario is too specific for OpenAI to spend time on. But it is still a massive market. If you don’t narrow your ideal customer profile (ICP) beyond “everyone”, good luck. You’re picking pennies in front of the steam roller that are foundational models. Now that pennies will no longer be minted, this metaphor may miss the mark. But you get the point.

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Another thought. In addition to “Shop with Perplexity”, the tech firm launched a virtual try-on feature that allows users to see how clothing would look on them before buying. Perplexity allows users to create a digital twin and then dress their avatar in the jacket they’ve been eyeing. Note, this feature is not yet available to all users.

This feature seems like a signal that Perplexity is looking to position themselves more deeply within the commerce realm. They’ve had shopping infrastructure within their platform for most of 2025. Moreover, they’ve made a conscious push for advertising (yet paused due to limited traction) and also spun up a publisher program so legacy publishers and affiliates can get paid for their content.

This isn’t by any means the hottest take, but I believe that Perplexity will attempt to be the AI engine for commerce. Over the next two years, they’ll look to position themselves at the intersection of commerce and media as these two businesses go together when it comes to influencing and making a purchase. If you’re in the CPG or advertising industry, pay close attention to what Perplexity launches. If not, their updates are likely less relevant.

But here and there, I plan to go back to my consumer brand roots for the sake of mixing it up and (hopefully) providing helpful information.

Holiday shopping season is upon us. You’re going to see plenty of flashy discounts over the next month, and at the same time, you’re going to be convinced to spend a lot more than you originally wanted to. Consider this your heads up that brands will be as cunning as ever to maximize performance at this time of year.

On the flip side, the holiday shopping season is a tricky time for consumer brands. Although some rely on the period between Thanksgiving and Christmas to drive the lion’s share of their annual sales, many brands are leery about the type of customers they are acquiring at this time of year. Everyone is shopping with a deal in mind; thus, brands are pressured into offering discounts for the sake of staying competitive.

Customer acquisition costs are already steep for direct-to-consumer (DTC) brands, and a hyper-competitive advertising market will make matters worse.

Offering a deep discount on the first order is usually worth it if you can get that customer to come back. But new-to-brand shoppers are more likely to be one-time customers as they’re shopping for a specific need in the way of a gift. They want to buy that gift for as low of a price as possible.

Strong brands know this and adjust their customer acquisition playbook to at least get something out of the deal. Let’s dive into four holiday offers that have a hidden agenda.

The Lead Generation Play: Exclusive Member Sale

The eye-popping text on websites. The juicy discounts. But wait, this deal isn’t for you. You’re not part of the brand’s members club.

Members-only sales are becoming more popular as brands need to get something in return for providing you a discount. To get past the digital rope, you’re going to need to show some ID in the way of an email or SMS (cell phone number) sign-up.

Forking over your email or phone number to gain access to steep discounts is a trade most are willing to make. You get a discount, but you’ve also provided consent for a brand to reach out to you via email or text in the future. So be aware of the onslaught of messages you may receive after signing up for the “Member Club”.

Member clubs tend to offer first dibs on new product drops, exclusive colors and sizes, and in-person experiences. They help brands develop “power users”, who tend to be their most loyal (read: profitable) customers. Right now, Lululemon is offering early access to their Black Friday sale for members only. Last holiday shopping season, I bought from an Alo Yoga member sale.

Remember, brands are worried you won’t purchase from them again. By providing your contact information, you’re opening the possibility of buying from the brand again. And the brand will take their chances on that trade.

Tip: You can always unsubscribe from an email or SMS list.

The Average Order Value Play: High Free Shipping Threshold, High Shipping Fee

This kind of offer will get you every time. It’s exactly the kind of deal that gets you to spend more money than you had originally planned on.

For example, athleticwear brand Haus is offering 50%+ discounts across their catalog. Check out this quarter zip. Normally priced at $110, it’s currently on sale for $49.

Here’s the catch: unless you place an order for $100 or more, Haus is asking you to pay a steep shipping fee. $12 to be exact. So that $49 quarter zip just became $61 quickly.

This strategy is a classic way to increase their average order value. They may not be able to sell a $49 item for a profit, but that $12 shipping fee likely boosts their margin from the red to the green.

From a psychological perspective, Amazon Prime has trained consumers to have a strong disdain for paying shipping fees. Brands know this. And they also know that although a shipping fee may hurt their website’s overall conversion rate, they’ll make more money as customers will buy more items to be above the free shipping threshold (in turn, increasing the average order value).

For Haus, offering steep discounts on an individual product isn’t likely a wise business decision for DTC brands (depending on the price). But if customers buy multiple products, the margins are stronger. You can usually throw multiple pieces of clothing in the same package, which won’t cost much more than if only one item is in the package.

Tip: Be disciplined about what you intend to buy and keep in mind that you are still receiving a discount (even if a high shipping fee is added onto the price).

The Overstock Play: Gift with Purchase

You’ll see these offers here and there. Spend above $X amount, receive a free hat, or serum, or travel bag. You think, “Oh this is great, I’ll spend more so I can get an extra item for free. It’ll be a wonderful stocking stuffer.”

It seems like a fair trade between the customer and the brand. You spend more than you originally intended, but you receive a gift for your troubles.

However, don’t think the brand is offering you a free item out of the goodness of their heart. Ask yourself: would I buy this “gift” on my own?

Unlikely. And that’s probably the answer for most people. It’s common for brands to offer a gift with purchase because they are overstocked and can’t figure out how to sell these products, so they’d rather give them for free than have them sit at a warehouse and collect storage fees. Overstock isn’t always the reason behind the free gift deal, but it certainly is the most common one.

Think about it: the “free” gifts that you’ve received are likely failed product launches or accessories that are barely profitable on their own. When customers buy more products to reach the free gift order value threshold, they’re likely covering the cost of goods sold for the product they’re receiving for free. It’s all a consumer psychology strategy.

Check out Uniqlo’s gift with purchase (on orders of $99+). You’ll receive a free duffle bag if you hit the order threshold. But in fine print, Uniqlo mentions that you don’t get to choose the color. This could indicate that Uniqlo made several duffle bags in colors that flopped and needed a way to get rid of this inventory.

Tip: Keep in mind that free gifts aren’t really “free”.

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The January Damage Control Play: Final Sale

I find this one to be the most diabolical, as customers likely don’t internalize what “final sale” means. The brand offers you a healthy discount, but the catch is that you can’t return the product.

January tends to be a brutal month for a consumer brand’s P&L. Revenue hits its annual low point as shopper fatigue sets in from all the holiday shopping spending. Pair that with a spike in refunds as customers return gifts, and you end up with a rough story.

Depending on your brand’s product category, there may not be much you can do to counteract the revenue drop. Or you can do some damage control and not allow customers to return the products they bought at a discount. Huge win for the P&L, but not great for customer experience.

Paige, which is known for their comfortable jeans, is running a Black Friday (final) sale. The inclusion of the word “final” implies that you can’t return the product after you bought it.

This is difficult for gift buyers who must guess someone’s size. It doesn’t make economic sense to buy two sizes and let the recipient choose which one fits better as they can’t return the other size.

If you know that you’ll keep the item, more power to you. Take advantage of “final sales” for the sake of the discount. But don’t be alarmed when you try to return a gift and are out of luck.

Tip: Don’t let flashy messaging fool you: purchases on final sales should be viewed as purchases from the clearance rack.

These plays aren’t inherently bad, but they are designed to shape your behavior. Happy holidays and be mindful when shopping!