Well, we talked about more than that. DoorDash had one of the first truly public examples of involving consumers in acquiring training data for physical AI. DoorDash began paying its Taskers to complete various household chores for the sake of data collection in the real world. As a refresher, foundational model companies will eventually run short on internet-scale text data to train LLMs. At the same time, any company building robotics must have access to niche, physical data to even start their endeavor.

If your goal is to build a robot maid, you’re going to need plenty of real-world data of people washing the dishes, folding laundry, and vacuuming.

Turns out, plenty of other companies (besides DoorDash) are serving as an intermediary for real-world data collection too.

I’m rarely on X, but I popped on the app this weekend and saw this announcement from Shift, a NYC-based free cleaning service. I highly recommend you click on the X post.

You read that right: free apartment cleanings for New Yorkers. The catch: the cleaners record themselves doing chores in your apartment. And Shift is super transparent about the transaction: “the value of [the] recording is what funds the service.”

This is pretty wild to think about. I can get someone to clean my apartment for free if I let them record it.

But it’s not really free. If you’re wondering why something is free, it’s because you are the product.

Same deal with most of the internet today. Paywalls and app fees are few and far between because the monetization model has shifted. Advertisers pay marketplaces and social media platforms to influence the decisions you make day in and day out. Why are Meta and Google free for users? Well, it’s because brands spend hundreds of billions of dollars each year to capture a sliver of your attention while you’re using their technology. Whether it’s Meta, Google, TikTok, or even while you wander the aisles of the grocery store (everything is a retail media network), your attention pays for discounts, entertainment, and social connectivity.

Next time you provide your email to a brand for “20% off your first purchase”, you should realize that you’re opting into giving the brand full rein to barrage you with marketing emails. It’s similar with a brand’s ads on Instagram. They are trying to meet you on the channel you are on and capture your attention, so you engage and buy from the brand.

Attention is monetizable. And in 2026, your physical dwelling is monetizable. Or at least, according to Shift.

As a self-proclaimed neat freak, I have no problem cleaning my own apartment. In fact, I find the act of cleaning relaxing at times. But I am tempted to try out Shift as a technology enthusiast.

But if I do, I’ll be careful beforehand. I imagine Shift is mainly looking to capture data points on all different kinds of furniture, kitchen sizes, and bathroom nuances to train how robots interact in a long-tail of environments. However, it’s quite likely they’ll also pick up images of family photos or valuable belongings. This data getting into the wrong hands could have serious consequences.

Shift does state the following in their privacy policy:

“We blur all personally identifiable information from screens and ID cards, to pieces of paper and cell phones to help protect both you and our home.”

But who knows what happens if they’re hacked.

Taking a step back, the AI narrative has created a lot of anxiety for society. U.S. states are considering data center bans, and the founder of OpenAI’s home was recently attacked by an anti-AI protestor. A growing subset of the population believe AI will take over the world in a negative way. Although this is heavily influenced by mainstream media who run sensational headlines to drive clicks and engagement, in return for advertiser’s budgets (you know, monetizing attention).

Things are rarely as good or bad as they seem. I’m pro-AI, but I also believe it is a “net positive”. Emphasis on net. I don’t believe AI will have as much of a long-term impact on the labor market because people find fulfillment in work and will create new jobs for themselves to add value. I also expect it’ll take longer than we think for AI to really push humans out of the loop, regardless of what any model benchmark says. But I’ll admit that AI may have drastic negative consequences on a person’s willingness to be social if they can talk to a chatbot without judgement.

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Back to Shift and DoorDash. I believe their services will further exacerbate the negative press around AI. It’ll be branded as destructive to the physical services economy, as well as hurt lower-income workers who typically serve as the delivery drivers or cleaners.

On the flip side, there is a short-term angle here that helps lower-income earners. Gas prices are soaring thanks to the war in Iran, and food supply and materials inflation is likely to tick up materially if the conflict continues for much longer. Expanding the gig economy with opportunities to pick up additional work through task recordings provides additional income streams for those who are struggling to make ends meet.

But it’s dystopian. The cleaners and delivery people are training their replacement. There’s a short-term win because it creates jobs, but over time those same workers may end up training the models that automate parts of their work. And it’s not like these jobs come with health insurance or benefits; they’re contractors.

The tech-enabled marketplaces are best-positioned to serve as the middlemen between gig workers and AI companies looking for real-world training data. I’m watching closely to see which companies are buying this data to help understand what the future will look like. The buyers will do a lot to shape how consumer-facing robotics are perceived going forward. I hope they have strong branding and are seen as trustworthy. The physical AI narrative may ultimately shape AI’s path in society because for the first time the technology will quite literally be inside the home.

Same format as prior Hot Takes in AI editions, but this one is going to be hotter than usual. Let’s get going.

Hot Take #1: SpaceX, OpenAI, and Anthropic IPOs are bad for the public investor.

If you’ve read last week’s Relentlessly Curious, it’s fair to say this take feels a bit redundant. However, I’m going to come at it from a different angle. AI start-ups have seen unprecedented growth, but often at all costs. As these AI labs enter public markets, they will be subject to the same quarterly earnings announcements as everyone else. Higher-than-expected compute costs or slower-than-expected user growth figures are bound to send the market into a tantrum given how much investment has gone into the new frontier from existing Big Tech players. Right now, no one really knows what’s going on under the hood at the AI labs. The numbers they release to the public are at their own discretion, and those figures are chosen deliberately to best position the narrative.

So, when the AI labs are forced to show their comprehensive financial statements every three months, some screws will come loose. In this scenario, more information may actually make things worse. Similar to how the broader market now seems to hinge on NVIDIA earnings announcements, I believe each AI lab earnings call will be a major volatility event for the market.

“Okay, so I won’t buy these IPOs. Thanks for the tip.”

Well, it isn’t that simple, because you’ll probably end up owning these companies whether you like it or not. If you invest in broad-based market-tracking index funds, you’re going to be an owner of each of these companies. SpaceX plans to list their stock on the NASDAQ, which has fast-entry rules that can accelerate inclusion into the Nasdaq-100, within 15 trading days. It’s still to be determined which exchange OpenAI and Anthropic list on, but they’ll likely qualify based on the Fast Entry market capitalization requirement, being in the top 40 of companies. At an $850 billion valuation, OpenAI would immediately rank among the world’s most valuable public companies. Number 16 to be exact as of May 22nd.

As an index investor, why should you be worried? Because of volatility.

Volatility isn’t ideal for the broad-based index investor. The problem is that the index investor is going to own the AI labs soon enough due to index fund tracking rules. The indexes are getting even more concentrated around tech and AI, and once the market knows more about these frontier companies, volatility will become harder to ignore. Thinking you’re fully diversified just because you own a broad-based index may become a fool’s errand.

Fluctuations in SpaceX stock aren’t going to materially move an entire index, but the market’s concentration in technology and AI continues to deepen and AI’s share of market attention keeps growing. Whether it’s SpaceX, OpenAI, or Anthropic, I find it hard to believe that it’ll be smooth sailing in public markets once Wall Street can comb through every line in their financial statements. And that’s not to mention private tech firms like Stripe or Databricks that may entertain public markets at multi-hundred-billion-dollar valuations soon, which would only deepen public market concentration in tech.

Also, side note: if SpaceX were to go public at a $2T valuation, that would be wild given it is estimated to have generated roughly $20B in 2025 revenue. Meanwhile, Amazon is trading at a little over $2T and posted roughly $750B in revenue in 2025. Really makes you think.

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Hot Take #2: Salesforce is the software stock to own coming out of the SaaS apocalypse.

Last month, I attended Salesforce’s Agentforce World Tour conference in Manhattan. Work conferences can be hit or miss, but I’m pleased to report that the show was worth my time. With that said, the food was bad. Both from a selection and taste perspective. You’d really expect better from a company worth nearly $150 billion.

Jokes aside, I was impressed with Salesforce’s continued success in integrating AI into their core product. Agentforce 360 turns your CRM into an agentic operating system, acting as the intelligence layer for your company’s data. Meanwhile, Slack has continued expanding their Slackbot, which similarly acts as the intelligence layer and integration capability across all your company’s internal and external communication. Employees can leverage Claude (or their AI model of their choosing) with the Slack interface.

Salesforce has what AI labs need: context-rich, hyper-specific data because customers have been storing their company’s central nervous system inside Salesforce for decades. You’re not going to get rid of Salesforce, especially since they’ve successfully embedded AI into their core product. As a Slack user, I’m impressed by SlackBot and the long list of available MCPs. If I’m scaling a company today, I’m leaning heavily into Slack given its ability to consolidate internal communication and AI into one platform. It’s perfectly situated as the horizontal layer within a company’s tech stack.

I’ve completed some really cool vibe coding projects (one where I replaced internal marketing analytics software that is saving the company I work for five figures per month). But I would never even consider trying to vibe code Slack. That would be crazy. Even Anthropic leans heavily on Slack. If the masterminds behind Claude aren’t trying to build a new workflow communication system, why would any other company think they could pull it off? No one is cancelling their Slack subscription.

The elephant in the room is multiple compression. AI does fundamentally challenge how investors think about a software company’s terminal value, due to its impact on the long-term growth rate. Seat-based software contracts may become less sticky, assuming clients reduce their labor force thanks to AI efficiencies. And AI-native companies will rely more on creating their own internal tools.

Even if I don’t believe Salesforce will be affected in the long term, its association with software stocks could impact its valuation. The narrative checks out for me: Salesforce looks poised for steady growth, but multiple compression could keep its stock price down.

Disclosure: I own Salesforce stock.

Last week, Anthropic announced a change to how they bill Claude usage via X, which goes into effect on June 15^th. Big shoutout to @arckollect on X who broke down this technical announcement that candidly goes over my head. I’ll do my best to further summarize (I read through this post five times).

Basically, there’s two ways people use Claude today: hands-on use and agentic use via API. Hands-on use implies that you are typing prompts into Claude, while programmatic use means you’ve built a process (i.e. agentic workflow) that leverages Claude to get work done via API. Not much will change if you are a hands-on user assuming you are not heavily using the API. But if you are heavily relying on agentic systems, your Claude usage now comes out of a separate bucket with a fixed cap. After you hit your cap, it’s up to you to pay a variable rate based on usage. Or you’re out of luck for the rest of the month.

Historically, many heavy users benefited from generous pooled usage limits. If you’re tinkering around with Claude Code, you’re probably not affected by this change. But if you are building a vertical-layer AI company or the first one-person billion-dollar company, you’re relying on AI as an operating system. Thus, your programmatic use may get a lot more expensive.

This subtle change in pricing is just the start of AI labs starting to think more about their own unit economics. It’s a safe bet to assume that AI will cost more for the user in the coming months and years as AI labs need to demonstrate a long-term viable business without the constant injections of massive capital. With public markets ambitions, these private market goliaths will need show they can tidy up their financial statements. The issue isn’t whether Anthropic and OpenAI are valuable businesses. They are. It’s whether public markets will tolerate the economics required to build these companies.

I’m curious about how this impacts the unit economics of vertical AI companies. You know, the start-ups that are built on top of Claude, OpenAI, and Gemini models. I’d imagine that few AI start-ups are profitable (please correct me if my assumption is wrong). For those that are already deeply unprofitable at the unit economics level, they will really struggle to get to profitability if token costs continue to go up. I’m afraid that entire AI businesses may be getting built on temporarily mispriced intelligence.

But these start-ups have other options in the near term. They can switch from Claude models to OpenAI’s Codex. The new agentic coding solution from OpenAI received a major upgrade thanks to the release of GPT-5.5, and OpenAI is letting developers run wild with token usage (much less restrictive compared to Anthropic). Additionally, OpenAI recently formed a joint venture with several major private equity firms called DeployCo for the purpose of growing adoption of OpenAI models at major enterprises. Despite major brand hits over the past few months, OpenAI is turning on the jets on B2B and creating serious competition for Anthropic while Anthropic takes criticism from power users.

Note: Although I rely almost exclusively on Claude models for work, I have tested out Codex and am pleased with its efficient token usage and higher-level reasoning for complex requests. Though switching my entire AI setup is time-consuming, Anthropic would need to significantly increase prices and further reduce token usage for me to fully switch to Codex.

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Here’s the thing though: it was only a matter of time until Anthropic curtailed token usage. They are burning through billions of dollars in compute each month and it’s public information that they may look to IPO by the end of the year. And when they file for an IPO, the world will get to see their S-1 offering (think of this as a prospectus for potential investors), showing what is really going on under the hood from a financial perspective.

Even with significant reductions in token usage offered on fixed rate plans, I have to imagine that unit economics on compute are a disaster. Making up numbers here, imagine a situation where users are paying $1 for $10 worth of tokens. That type of pricing arbitrage doesn’t just close overnight. It would take years and require threading the needle while staving off competition that can be subsidized with many hundreds of billions of dollars of capital. I know it doesn’t seem like it now, but eventually investor appetite will dry up. It remains to be seen how much people are willing to pay for AI if prices go up. Are they willing to pay double? Probably. But are they willing to pay ten times? You’ll need a super clear business case to do so.

Last week on Prof G Markets, guest Ed Zitron argued that Anthropic co-founder Dario Amodei’s repeated statement that Anthropic is profitable on inference (the process of providing responses to users) is misleading. Zitron makes the point that being profitable on inference is a helpful indicator as it takes many billions of dollars to train the model in the first place. A major sign of long-term business viability would be if Anthropic were profitable on both inference and training.

But maybe this time is different. Maybe unit economics doesn’t matter because AI is a winner-take-all market and massive scale is paramount to winning. Take Uber for example, who operated at a loss for well over a decade, including several years as a public company. AI is about narrative and it’s the hottest narrative I’ve seen in my career.

Narratives can stretch economics for a long time, but not indefinitely. And when the S-1 comes out, is when Wall Street gets a peek at how bad the unit economics are and what accounting assumptions are being used to support unit economics figures. I’m not suggesting anything fraudulent is going on, but my guess is that there are aggressive interpretations around profitability metrics.

The thing is, Uber eventually raised prices and is a highly cash-flow generative, profitable company now. The best-case scenario is that Anthropic or OpenAI can change their entire pricing model to variable, and people stay on their current plan.

Because the US stock market is so heavily concentrated in technology (Magnificent Seven make up roughly 35% of the S&P 500 value) and Big Tech plans to spend nearly $700 billion on AI capital expenditures this year, anything that adversely impacts the growth of the AI labs is likely to cause a cascading effect on the rest of the market. As Scott Galloway says, “the market is a giant bet on AI”.

When Anthropic and OpenAI go public (likely later this year), they will be at the whims of public market forces and investor scrutiny. They can’t hide their numbers anymore behind hundreds of billions of dollars of private market money. If the narrative around AI labs’ ability to continue growing at a record-setting clip and ability to inch towards profitability breaks, then the rest of the market will take a major dip. For those of you who invest in only broad-based index funds like the SPY (S&P 500 tracker), you’re exposed big time.

Accessing public markets brings enormous amounts of capital to the AI labs, however I believe it increases their risk profile. They’ll be subject to quarterly earnings requirements, and Wall Street will hold them to revenue, user, and profitability targets. If they stumble, the rest of the market will fall on its face given how dependent the market is on AI capex generating meaningful returns. And that is likely to affect valuations far and wide.

I wish I could invest in Anthropic and OpenAI. They are businesses that I believe will change humanity for the better. But sheesh, my investment portfolio is perfectly okay with them remaining private businesses for a little while longer.

Check out the How to Vibe (Claude) Code series!

Part I

Part II

Part III

Take that valuation with a grain of salt, as reports quickly surfaced following the Times publication alleging that Medvi may have engaged in fraudulent behavior. So maybe we don’t have the first credible one-person billion-dollar company yet.

But the concept is starting to emerge, given the step-function development of AI. Those closest to AI seem to believe this type of company structure is imminent, where one person manages teams of AI agents (in lieu of people). Check out this clip of Sam Altman (CEO of OpenAI) mentioning the near-term possibility of ten-person billion-dollar companies, as well as the glorified one-person billion-dollar company.

The idea is mesmerizing. It turns conventional wisdom about building a company on its head. For most businesses with ambitions of being valued at over $1B, conventional wisdom says you need massive teams, capital, and infrastructure.

I’d love for one of my business ideas to take off and for me to build it by myself all the way to $1B.

But what happens if I want to sell the business and get out? Who would be a buyer of a one-person, AI-native company?

At the billion-dollar level, potential suitors tend to fall into two main buckets: strategic acquirers or financial buyers.

Quick aside: a “strategic” is a company that buys another company for strategic reasons (in its purest form). For instance, Unilever, consumer goods holding company, acquired Gruns in April. Gruns fits in well with Unilever’s portfolio and corporate strategy, thus it was a “strategic acquisition”. On the other hand, a “financial” buyer looks to purchase a company, make operational improvements, and then sell the company. Strategics are typically corporations, and financial buyers are usually private equity firms.

For a strategic buyer, a lean AI-native company is attractive because they don’t have to deal with cultural integration risk. You don’t have to worry about the team you’re acquiring being gun-ho about assimilating into the culture of your company. But you do deal with key-person risk. Sure, AI agents aren’t going to jump to your competitor (or maybe they will if AGI is achieved), but that one person at the ten-person start-up could. Each employee becomes mission critical, so retention packages effectively become embedded into the valuation. Although management retention packages are commonplace in mergers, the stakes are higher if there are only ten people at the target (versus thousands).

Enter, financial buyer. If AI allows companies to operate hyper-efficient workflows and each employee managing teams of AI agents that complete various tasks, how does an outside investor come in and justify a valuation premium if there are limited operational improvements left to be made? Synergies will need to be on the revenue side, as the cost of AI agents will likely be as efficient as possible. What operational efficiencies can private equity drive if there aren’t any more operational efficiencies to create? Read: laying off workers. But wait, those don’t exist!

Financial buyers often rely on margin expansion, and financial engineering to drive returns. Venture-backed technology companies typically rely more heavily on equity financing than debt financing.

Quick tangent: I see both sides of the AI agent efficiency coin on my LinkedIn feed. Some people are bragging about how many thousands of dollars they were charged for Claude API usage the prior month as if this is a signal for AI productivity. I’ve also seen reports that entire companies, including Uber, are blowing through their full-year AI budgets within months. This inherently challenges the thesis that AI agents are cheaper than human employees.

On the flip side, I see others on LinkedIn offering Claude Skills they built to optimize token usage. I imagine a new layer of companies will emerge to help AI agents become as efficient as possible with token usage. In general, as the race to build more data centers continues and the cost of compute decreases, per-token usage costs should decrease. Over time, competition and infrastructure buildout should continue pushing token costs lower, even as total AI usage rises dramatically.

AI-native companies are lean in nature. Highly AI-fluent people are managing teams of AI agents instead of teams of employees to get work done. This means a company’s operating leverage could become extraordinarily high, prior to any potential acquirer coming in. Essentially, AI expands a company’s operating margin to the point where it becomes as lean as realistically possible. If you’re looking to make a purchase, you need a clear revenue-synergy story to justify the acquisition price, as the cost-synergy story is unlikely to be there.

Say you decide to acquire a ten-person AI-native company for $1B. You almost need to do it in all-stock, to keep the operating team incentivized to stay (assuming the target company would even accept the deal). Otherwise, those ten people just became insanely rich, and it’s unlikely they’ll be as motivated as they were pre-acquisition to work at the combined company. Because AI systems are still imperfect, you need someone to stick around and remain accountable for the outputs those agents produce.

Look, if you’re able to build a billion-dollar company by yourself, you are crushing it in life. You’ll eventually figure out how to sell the company. But it does pose a tricky question for an acquirer. Not much meat on the bone in the way of incremental operating efficiencies. Combine that with significant key-person risk, and you’re not looking at a great acquisition story.

If you’ve made it this far, let me know how you think AI will impact valuations and exit potential for the super-lean, AI-native businesses being built today. It may ultimately force potential acquirers to ask themselves: what exactly can we do better? Because the list of answers is likely to be shorter.

Models

If you’re using the Claude desktop app, you’ll see the active model in the bottom right of the prompt bar (just look for the Claude logo). Words like Haiku, Sonnet, and Opus are branded names representing different large language models (LLMs) offered by Anthropic. You can click on the model’s name to open the menu of options within the desktop app or web browser. If you’re in the terminal, type “/model” to see the available models.

It’s important to know a little bit about each one before you begin a vibe coding session, as you’ll either be frustrated by output quality or surprised at how quickly you burn through tokens.

Each model is designed for separate use cases. Haiku is best suited for quick questions and answers. If I need a model to handle basic data retrieval or to answer generic questions, I’d rely on Haiku. It’s the fastest and cheapest model. But I don’t recommend using it to build an app or a website as it lacks the strategic reasoning element of the other Claude models.

Next, Sonnet sits in the second tier. This is my go-to model for vibe coding. For any moderately complex question, I’ll send it over to Sonnet. For most of the agents I create, I’ll have them rely on Sonnet. It’s effective and cost-efficient.

Finally, there’s Opus. View Opus as your sharpest coworker. It’s the one you go to for advice or guidance when the problem you’re trying to solve is too thorny. Opus can handle deep research, strategic reasoning, and complex, multi-step/multi-agent processes better than any other Claude model.

However, be wary of how often you rely on Opus. Regardless of whether you’re on a Pro plan or a Max 20x plan, Opus burns through your token limits noticeably quicker than Haiku or Sonnet.

When I use Claude Code, I always start off in Plan Mode before I request Claude to take an action on my behalf. The more context and guidance you can provide to AI, the closer the output will be to what you originally envisioned. I leverage Opus to write the build plan for the product or system that I’m aiming to build and rely on its strong strategic reasoning to question my logic and push my thinking. I regularly ask Opus to find holes in my own logic, as well as its own. When I’m satisfied with the plan, I push the plan to execute using Sonnet. This separates high-level reasoning from execution, which is more cost-efficient.

Also, you may have noticed the phrase “effort” next to the model’s name. Sonnet and Opus both have a dial that allows you to control the performance of the model. Go low, and you’ll get a fast version, but you may create some errors along the way. Go high, and you’ll burn through tokens quickly, but you’ll be able to handle complex tasks like full reviews of your codebase, synthesizing dozens of files at once, and find even slight irregularities between app versions. If you’re wondering, Sonnet at either Medium or High effort is my default setting.

Higher effort increases the amount of compute the model uses to reason through your prompt. More compute means more tokens used up.

Note: there’s also the rumored Mythos (which we chatted about here), but since it’s not publicly available yet, no need to dive into use cases.

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Token Usage and Context Windows

“You keep mentioning, ‘burning through tokens’. What does that even mean?”

Good call out. This is worthy of an explanation.

Tokens in the AI world are like tokens in an arcade. You need tokens to play the game, and some games require more tokens than others.

Each word you type can map to one or more tokens. Additionally, uploading file attachments, requesting pictures, and asking Claude to search the web all require tokens of varying amounts to complete. Essentially, everything you do within Claude chews up tokens, but what you do and which model you use impacts how many tokens you’re using.

Another critical thing to know here is that lengthy conversations eat up tokens. When you press Enter on your next prompt, the full conversation is packaged up and sent to Claude for context interpretation. Thus, the more back-and-forth you have with Claude, the more information that gets continually sent back to the model, and the more tokens are burned in the process.

If you’ve picked up that Claude is starting to forget pieces of information you told it earlier on in your conversation, it’s because you hit your context window. This represents the maximum number of tokens that AI can “hold” within a single chat session. Certain pieces of data or information will start to fall off, and you’ll start wondering why your output looks funky. That’s because new tokens are pushing older ones out of the window on a “first in, first out” basis.

Tip: If you plan to change the topic, start a new session or tab. This resets the context window. Also, you can have Claude remember a summary of your conversation by invoking the “/memory” command within the terminal and then reference this memory in a new session. Leveraging “/compact” helps maintain context in a summarized, token-efficient form. Then, “/clear” erases all prior context, creating a new slate within the same session.

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Model Context Protocol (MCP)

MCP is technology developed by Anthropic that can be thought of as a standardized way for AI to interact with external tools and data sources. It allows users to connect various data sources to their AI setup. In an oversimplified one-liner, MCP is like an API for AI. I’ll hold off on the technical elements of MCPs (reach out if you’re interested, I’d be happy to chat) and instead focus on how you can set them up.

Say you are an e-commerce manager and you’re looking to build a dashboard that depicts real-time data from Google Analytics 4 (GA4). Claude can help you do this, and you can leverage MCP to make it happen.

What does that look like? You write the prompt, “rank site referral traffic sources in GA4 by sessions from last month and analyze bounce rate by source”. Claude can call the GA4 MCP (if configured) and return the data to your desktop app or terminal without having to log into a separate website.

Claude has a growing set of built-in connectors that leverage MCP to pull in data (and push actions too). Check out the “Connectors” section within the desktop app’s Settings area. Or you can ask Claude to help you set up a custom MCP, which will require more technical prowess. Still doable for even novice vibe coders though.

A word of caution: When making an MCP call within a chat, Claude is pulling a lot of extra data from the external data source. This will have a noticeable impact on your tokens and chew up your context window. MCPs are useful but difficult to use at scale given the cost impact and context window consequence. This is especially true if you are asking Claude to pull from many MCP connections within the same request. I recommend separating your tasks into various sub-agents that pull data individually from each MCP and then have an orchestrator agent synthesize the information between each sub-agent. This should eat up fewer tokens than trying to put together an analysis by stuffing each MCP into one session since each MCP call injects a large amount of data into the context window.

Reading between the lines of the model announcement, OpenAI subtly jabbed Anthropic by citing GPT 5.5’s token efficiency for problem solving. For context, Anthropic has faced criticism for token limits and model outages.

When I see news like this, I’m tempted to abandon my current AI setup for the flashy new thing. In some cases, that’s a wise thing to do, particularly if the new model’s upgrades are relevant to your workflow. But I believe you can reasonably expect that every major AI company is going to come up with the latest and greatest every couple of months and frequently changing your AI operating system has material switching costs in time and productivity.

My advice? Pick a model ecosystem and commit to going deep. It matters less which model it is, and more that you are putting consistent effort into learning how to get the most out of it for your workflow. I’ve chosen Anthropic’s Claude models as my focal point, but there’s no harm in spending your time in OpenAI’s Codex (Claude Code rough equivalent).

Although I’m not too familiar with the inner workings and lingo of other agentic coding tools besides Claude Code, I imagine the following tips will be transferrable regardless of solution. Without further ado, let’s get into this week’s Claude Code learnings.

Building in Claude Code

Markdown Files

Candidly, I hadn’t heard of markdown files (.md files) before I downloaded Replit a while back. It looks kind of like a .txt file (i.e. the Notepad app on your computer). A key trait of AI-generated markdown files is that they contain a lot of hashtags, asterisks, backticks, and bold-font section titles.

AI prefers markdown files because of its semantic structure. Hashtags signify hierarchy and structure, while backticks denote code. Since the communication method is clear, markdown files are relatively token efficient for AI models to ingest.

Although the formatting can take some time to get used to, writing instructions and context for agents are best handled in markdown files. I open an IDE (Visual Studio Code) and select a folder on my desktop. Then I’m able to see all the markdown files AI (or myself) has created, and either edit existing instructions or create new ones.

Markdown files look odd, but don’t be intimidated. They are essentially Microsoft Word docs for AI.

Claude Skills

Skills are clutch and are becoming an increasing part of my day-to-day. Oversimplifying it, the difference between skills and agents is that skills operate as singular tasks where context is defined ahead of time. On the other hand, agents handle more complex, sequential workflows and can maintain context across tasks. Agents can complete skills, but a skill is not an agent.

Recently, I developed a skill that transforms video transcriptions into YouTube-optimized titles, descriptions, and tags. I fed Claude Code all the information needed to complete this recurring task and now it executes the task whenever I type “/skill youtube-automation” and provide a bit of context.

You can create skills to consolidate notes, provide weekly recaps on meetings, and complete performance analysis.

How to get started? Ask Claude to help you create a skill. Make sure you’re on “plan mode” so Claude can run you through a Q&A on what you’re looking to build. Claude will proceed to generate a markdown file with your necessary information, goals, and steps to take.

Oh, and check your settings section of the Claude desktop app. There are tons of built-in skills that Claude offers today, that you can call directly from the desktop or terminal.

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Pushing to Production

GitHub 101

You are happy with what you built and you want to “save it”. If your usual workflow consists of Microsoft Excel, Word, and PowerPoint, you’re going to need to rethink what saving here means. But that doesn’t mean it is harder to do.

GitHub is a platform that stores and tracks changes to your code over time. It’s the standard way developers manage, back up, and collaborate on software. With integrations across most development tools and AI workflows, engineers and vibe coders can upload their code to GitHub relatively quickly.

You commit your code (save a version locally) and push it (upload to GitHub). If collaborating, you open a pull request (PR) to propose changes to the codebase for your colleagues to review.

First, make an account on their website and then ask Claude to help you connect your local project to a GitHub repository and walk you through the setup. There’s a bit of a learning curve once you’re on GitHub and you look through different branches and navigate the codebase, but rest assured, your code is saved in a secure spot.

The Importance of Engineers in the AI Era

There are plenty of headlines about how AI is eating up the software engineer job market. While there are layoffs in pockets at big companies, the broader story shows that engineering job postings are stabilizing in some areas or even rising (as per Lenny’s Newsletter).

As a vibe coder, I’ve gained a strong appreciation for the value formal engineers bring to building software in the AI era. Vibe coding is excellent for spinning up a wireframe or a working prototype, however, if you plan to store user information or launch the product on the web for others to use, I highly recommend you have a well-trained engineer take a look.

Cybersecurity and scalability are two major concerns when deploying software. Even with repeated prompting around closing any vulnerabilities, there are still holes. Hackers can not only find a way to extract private user information, but they can also find ways to take advantage of features on your app or website.

What do I mean here? If your product involves an API call to an LLM to retrieve data, a hacker could exploit the API integration to run their own API calls and stiff you with the bill. Next thing you know, you have a $10K charge overnight because someone else found a way to run up your tab.

If you plan on accepting and storing payment information or user identification information, reach out to an engineer to take a peek at your codebase. Engineers aren’t going away any time soon; it’s more of a flight to quality. The best ones are in more demand than ever.

If you’re just starting out and have questions about getting started with Claude Code, I’d be happy to troubleshoot and share some pointers. Subscribe and reply to this email with your question!

Author’s Note: Today’s article is a sequel to #72: How to Vibe Code 101. If you haven’t read it yet, I recommend checking it out!

I’ve certainly caught the Claude Code bug. Over the past six weeks, I’ve found myself using Claude Code all day at work, as well as in the evenings for side projects. I’m consistently running up against my token usage limits and drawing on additional credits Anthropic graciously gave me.

Taking a step back, agentic coding agents like Anthropic’s Claude Code and OpenAI’s Codex allow users to build software by writing in plain English (natural language prompting). Since the February release of the Claude Opus 4.6 model, agentic coding agents have seen a step-function improvement in their ability to automate processes, reason strategically, and build legit products from a simple written request. The future truly is agentic. As we chatted about in #79: AI + Future of Work, agentic AI is the AI that is fundamentally changing how knowledge workers complete their job.

Today, I’ll dive into my vibe coding learnings as a daily user of Claude Code. For $20 (plus tax) per month, you can gain access to agentic coding agents and experience the magic of present-day technology firsthand. Not a sales pitch, just a guy who is really in awe of the ease of building today. Unfortunately, vibe coding is rarely free because of all the tokens each conversation burns when AI completes sequential workflows.

Looking back on How to Vibe Code 101, I still agree with my thought framework laid out in the article. Strong systems thinking and “starting with the end in mind” are critical to achieving your objective when working with AI.

Getting Started

Organization is a Top Priority

Before you download an agentic coding tool, you need to get organized. Comb through your folders on your computer or shared drive and make sure each file is placed in the proper folder, as well as named accurately. Your meeting notes shouldn’t be intertwined with your performance recaps.

This matters because you’ll ask Claude to complete tasks that will likely depend on where certain files live, as well as where you’d like to place the outputs of Claude’s work. For instance, if you want Claude to create a weekly performance analysis based on last week’s data, it will need to know where the information lives to complete the task.

Tip: Have a slew of random screenshots and pictures without helpful file names to tell you what the picture is? Put these files into a folder, grab the file path, and prompt Claude with “Look through the picture files in ‘X’ folder and rename each file based on the contents of the picture”. You’ll be amazed at the results.

The Interface: Terminal vs. Desktop app vs. Integrated Development Environment (IDE)

For those of you who are non-technical (like me), you probably haven’t spent much time in a terminal. Type “terminal” into your computer’s search bar and you’ll be greeted by an intimidating black screen. When I first set up Claude Code, I wasn’t too confident of my abilities to tackle using a terminal. After a week, it’s what I use 90% of the time I leverage Claude.

Once you install Claude software onto your computer, you can simply type “claude” into the ominous terminal and then you’re brought to a much friendlier user interface where you can type, and paste to your heart’s content.

Next, Anthropic launched a desktop app that allows you to access Claude Chat (a typical chatbot interface), Claude Code, and Claude Cowork all in one interface. You can view settings, manage integrations, and switch between modes here.

Finally, you can run Claude Code from within an IDE, which is essentially an interface that lets you write code (or type in English), view your file system and actual text and markdown files all in one spot. I find it helpful to review AI output and visualize how my files and folders are set up. Examples of IDEs are Visual Studio Code (what I use), JetBrains IDEs, and Cursor.

Personally, I prefer the terminal view because it gives me the maximum flexibility when using many data sources (via model context protocol) and running multiple agents at once. Desktop is much more user-friendly and intuitive, and I’ll use it for one-off tasks.

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Operating within Claude Code

Use Plan Mode

I cannot urge you enough: spend as much time in “plan mode” as you can. It’ll save you time and money by using fewer tokens.

Plan mode allows you to talk back and forth with AI about what you would like to build and have it challenge your thinking. Many times, I’ll want to suggest a feature change to a product I’ve built. I’ll toggle on “plan mode” (hit Shift + Tab if you’re in the terminal or click the drop-down menu in the desktop app), type an idea, and finish the prompt with “ask me questions and challenge my thinking.”

Furthermore, Claude asks me questions about my idea, and will even request additional data points to better support why I want to take “X” action. This is critical because it forces me to be clear on what I’m looking for the AI to complete so I don’t send Claude down a rabbit hole.

The default mode in Claude is to receive a request and then execute (i.e., not plan mode). This works favorably for Anthropic because actions burn more usage tokens than back-and-forth questions. It’s a lot less expensive for Claude to write a plan than it is to take the actions within the plan. At the end of plan mode, Claude provides a formal, written plan sometimes with charts, diagrams, and visualizations. You can make edits to the plan or formally allow Claude to begin working.

Connecting Data Sources: Model Context Protocol (MCP)

One of my favorite parts of Claude Code is that I can connect various data sources to the software and have Claude develop insights from these live sources. For instance, a recent project of mine was automating performance analysis for an e-commerce funnel. I connected paid advertising data (Meta, Google Ads), search trends (via Google Search Console), website traffic information (Google Analytics 4), email performance (email service provider), and website behavior (heatmapping) to Claude through MCP.

From there, I provided context about the types of analysis I’d like to complete within the weekly performance recap. This included a mock-up of the final output, details about each data source, the personas and perspectives that Claude should consider, and benchmarks for past performance and future goals.

Then, Claude analyzed all the data sources I provided, incorporated my instructions, and produced a synthesized performance recap along with a prioritized list of priorities for brand, marketing, and technology teams to improve advertising efficiency, website performance, and conversion rates across the funnel.

In an oversimplified definition, MCPs are like APIs for AI. They allow AI to connect to external data sources and software, local files, and shared drives to stitch together context and execute requested tasks.

You can connect built-in MCPs from within the Claude Code desktop app or install them in the terminal by asking Claude for help on how to set up the MCP connection to the data source of your choice.

Note: Setting up MCPs can be “expensive”. When I say expensive, I’m referring to the context that MCP connections take up within a context window. AI currently has a limit on how much information it can recall within a single session, and that calling large external data sources through MCP takes up a large chunk of the context window as you start adding more sources (like I did in the e-commerce funnel analysis).

Be conscious of how many messages you have in a single session when leveraging MCP. Claude may begin to hallucinate or flat-out not remember context you provided earlier in the session. In this case, I recommend writing the terminal command “/compact” to summarize earlier information for Claude, freeing up context so you can continue prompting within this session.

Clarity of Thought

I’ve found Claude most effective when I provide a visualization of the process I’d like to create. For a dashboard I’m building now, I drew with a pen on paper, a front-end UX mock-up and a back-end data source map.

Now that I’m writing this out to you, I’m realizing that it likely isn’t that Claude works better with visualizations than well-written instructions. It’s that by putting pen to paper, it forced me to really think through what type of dashboard I wanted to build, how the user would interact with it, and the structure of the back end to support the various data sources.

The clearer your thought process, the stronger AI output will be.

Next week, we’ll walk through a 301 session, which will involve GitHub and pull requests (PRs), Claude skills, the importance of engineers in a vibe coding world, and plenty more tips!

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.