Aaron's Newsletter

Some notes from Missouri Startup Weekend 2026 (MOSX)

Aaron Heienickle — Thu, 23 Apr 2026 01:06:54 GMT

I came home from Missouri Startup Weekend, oddly energized with a full Apple Notes app.

What is Startup Weekend?

For those who haven’t been to one…

Startup Weekend is a 54-hour event where strangers form teams around ideas pitched on Friday night, build something over the weekend, and present to judges on Sunday.

It’s part hackathon, part business plan competition, part crash course in entrepreneurship.

The format has been running for over a decade, but what people build in those 54 hours keeps evolving with the tools available.

This year’s numbers

Friday night opened with 72 pitches — one minute each to sell an idea to the room. Judges and audience voting narrowed that down to 15 teams that would actually form and build over the weekend.

By Sunday’s presentations, there was almost $140,000 in real investment commitments made to teams that had existed for less than three days.

This year’s Missouri Startup Weekend drew participants from across the state — students, working professionals, serial entrepreneurs, and plenty of people trying their first startup.

Before the energy of all this excitement away, I wanted to jot down a few thoughts from the weekend.

Lots of conversations about AI, as expected

I got to talk to ALOT of people building with AI, or using AI in some shape, way, or form.

That alone feels widely different from even a year ago.

Autonomous bulldozer??

One of the most memorable moments on the hardware side was at EquipmentShare’s robotics lab.

They let us remotely operate a robotic bulldozer from their technology center, with the actual machine sitting in a barn in Fulton.

EquipmentShare’s head of robotics showing off the autonomous bulldozer

The same bulldozer also accepts voice commands from the phone that you can probably just barely see above the Xbox controller. It can also move autonomously within the space.

Another interesting feature is that if you look really closely at these images you can see cones in the middle of the screen. If you try to drive the bulldozer into the cones, a ‘shine detection’ sensor will detect the cones and stop the bulldozer.

Let’s say a worker on the job site falls over while wearing a shiny protective vest; this feature helps the robotic bulldozer avoid running over the worker.

As you may expecty, safety is a big concern when rolling something like this out in real life.

Here is my friend Caleb picking up some dirt with his new bulldozer

Adoption is faster than it’s ever been

I stopped by to see my friend and student Andrew, who was already designing a MVP for the AirWaze in Claude Design.

That specific Claude Design feature had launched roughly 48 hours earlier — Anthropic released it on April 17 — and here it was, already in a student’s workflow at a weekend event in Missouri.

That’s a data point I keep coming back to.

The lag between “launched at a frontier Silicon Valley AI lab” and “a college student in the Midwest is shipping with it” used to be measured in months or years.

This weekend, it was measured in days. Whatever you think about the pace of AI itself, the pace of adoption around it is now the more striking number.

That’s overwhelming, but also kind of cool.

Thoughts from the top pitches

As the creator of a card game designed to help you pitch your ideas better, you bet I had thoughts on what teams could have done better, new trends, and what they did well.

Getting ready for the team’s final pitches

A few of top teans — quick impressions

A handful of the teams that placed, with the one-liner I scribbled down for each:

Tonetta — coaches people to speak more clearly on sales calls
Unileaae — student housing search, because finding it currently takes forever
EQUIPR — lab equipment rental; they pointed at a roughly $1B market
CMPLX — Zillow for off-market real estate deals
CRANK — student organization monetization and event discovery; the team itself had more energy than anyone else in the room
SBA Loans App — an agent to help small businesses through SBA loans; ~$33B in SBA loans annually

Some of my favorites

The ones I thought had the sharpest shot:

RIVE — a better way to do health savings accounts - cut out the middle man, and see if the consumer comes out better.
Airwaze — Helping with manual flight loggined
AI Dungeon Masters — AI to help run DnD games aimed at the ~30M D&D players
HerdRoute — Uber for farm animals, connecting auction houses, buyers, and drivers. Genuinely novel, and the kind of unsexy logistics problem that real businesses get built on

I wouldn’t recommend using Claude for presentations just yet.

Since I was just singing praises of the speed of adoption, I do one to give one honest note about Claude for pitches like this.

For actual pitch decks and presentations, I would not use Claude just yet.

Some teams presented fully Claude-made presentations. They were super difficult to read and understand. They were very text-heavy, making them almost impossible to digest.

Instead of being a visual aid, they probably actually hurt their presentations

Demos of functional MVPs

The single biggest change from past startup weekends is that almost every team had at least an MVP.

For non-technical folks, that is a minimum viable product - something visual you could actually interact with or watch run.

So, we are talking not a sketch on a napkin, not a Figma mockup, and definitely not three screenshots taped to a Google Slide.

Pretty well-designed working demos.

Tools like Claude, Cursor, v0, and now Claude Design have collapsed the distance between “idea” and “something I can show you.”

54 hours used to be barely enough to nail down a concept and get a mock-up of what your idea was.

Today, it’s enough to ship a rough but pretty functional, nice-looking version.

I talk more about the speed of software development today in my newsletter: Everyone Gets a Jarvis - the build versus buy math,

What the best pitches had in common

The strongest pitches of the weekend all did one thing really wel,

They incorporated direct customer feedback, and they quoted customers by name.

One of the very best teams went a step further and played short video clips of real customers discussing the validity problem.

You can’t fake that, and you can feel the difference the second it hits the room.

If I had one piece of advice for next year’s teams, it would be to spend less time on your slide design and more time getting on the phone with five potential customers.

Then put them on the screen and talk about how they love what you are doing. It sells really well.

That’s my weekend.

Fulton bulldozer, a student building in a two-day-old AI tool, and a field of teams that mostly showed up with something that actually worked. Good weekend to be in Missouri.

Anthropic is spending $5,000 to serve your $200 plan — bug or master plan?

Aaron Heienickle — Thu, 16 Apr 2026 13:03:29 GMT

You come into your workday. You sit down to code.

You open Claude Code.

You type your first prompt of the morning.

Nineteen minutes later, it stops working. Wait what?

You get just... a polite little message that your session limit is used up, and to come back in four hours.

This is what started happening to thousands of developers on March 23rd.

Specifically, many developers paying $200 a month for Anthropic’s top-tier Max plan. AKA, the one that’s supposed to give you 20x usage.

One Reddit thread called “20x max usage gone in 19 minutes” hit 330 comments in 24 hours.

Another got 360+ comments in six days.

One subscriber commented, “It’s maxed out every Monday and resets at Saturday. Out of 30 days I get to use Claude 12.”

So, what is going on here?

Is this a bug? Corporate incompetence? Bait and switch?

Kind of. But also — and this is the more interesting answer — it might be one of the most deliberate strategic plays in tech right now.

Let me explain.

First, a stat that will blow you away

Before we get into Antrhropic’s master plan territory, there’s one stat that reframes this entire story.

Anthropic’s $200-per-month Claude Code Max subscription can consume up to $5,000 worth of compute per user, per month.

That’s according to an internal analysis by Cursor, reported by Forbes.

Some analysts think the ceiling is even higher — up to $25,000 for the heaviest power users.

You pay $200. It costs up to $5,000 to serve you. Anthropic eats the difference.

Every. Single. Month.

Imagine a steakhouse charging you $20 for a ribeye that costs them $100 to source, cook, and plate. They’re not running a restaurant — they’re running a very expensive customer acquisition strategy, and hoping they figure out the unit economics before the investors stop picking up the tab.

That’s AI subscriptions in 2026.

One developer dug through his own session logs and found he’d consumed 10 billion tokens across eight months of Claude Code usage.

At retail API pricing, that should have cost over $15,000.

He paid roughly $800 on the Max plan. A 93% subsidy — funded a massive venture bet.

So, why does this cause your session to die in 19 minutes?

Here’s where the bug vs. master plan question actually gets interesting.

The honest answer is possibly both at once.

On the bug side — a developer reverse-engineered the Claude Code binary and found two independent caching bugs that were silently inflating token costs by 10–20x. Real bugs. Confirmed by Anthropic.

Also, in early March, they ran a promotion that doubled usage limits for two weeks, then quietly ended it on March 28th — so users who got used to the doubled limits felt a sudden cliff when it expired.

Then, Anthropic added peak-hour throttling on top of all of that.

The four things hit at once.

So, yes, a list of things happened:

Some of the 19-minute sessions are a genuine bug.
Some are policy changes communicated poorly.
Some are the promotion hangover.

But beneath it all, there is a deeper reason why usage limits exist at all.

That is a $4,800 gap between what you pay and what Anthropic spends to serve you.

And, when the math doesn’t work, something has to give…

Okay, but why would any company do this on purpose?

This is Anthropic’s master plan part.

Think about how Uber launched. Rides were absurdly cheap for years because Uber needed you to stop taking taxis and start opening the Uber app every time you needed a ride. They bought your habit with subsidized prices, knowing the real monetization would come later.

Or think about Amazon Prime. When it launched, free two-day shipping was an economic disaster for Amazon. They lost money on shipping constantly. But they also discovered that Prime members spent dramatically more than non-members. The habit was worth more than the short-term loss.

AI subscriptions are running the same exact playbook.

Anthropic and its investors need your workflow. They need Claude to be the thing you reach for automatically — for code, for writing, for thinking through problems.

If they charge you the real price of $5,000 a month right now, then you don’t subscribe over competitors.

So, they charge $200, absorb the loss, and bet that by the time they need to close that gap, two things will be true:

You’ll be too dependent on the tool to leave,
And the actual cost of computing will have fallen enough for the gap to close on its own.

Here’s why that second bet is actually well-founded

This is the part of the story most people miss completely.

The cost of running AI — what the industry calls cost to compute, measured in cost per token — is falling faster than almost any technology in history.

In late 2022, running a GPT-4-class model cost around $20 per million tokens. Today, equivalent performance costs about $0.40.

That’s a 1,000x reduction in three years.

Faster than PC computing. Faster than internet bandwidth during the dotcom boom.

To put that in perspective: if airline tickets had fallen at the same rate, a cross-country flight would now cost about a quarter.

And it’s not slowing down.

AWS cut GPU pricing 44% in mid-2025. Spot GPU prices dropped 88% in less than two years.
NVIDIA’s next chip generation promises another 10x reduction in token cost.
Anthropic signed a deal with Google for up to one million specialized AI chips specifically to drive their compute costs down.

They are betting is a massive decrease in what it actually costs Anthropic to serve you one session.

Right now, that cost is $5,000 against your $200.

If computing keeps falling at its current rate, that same $5,000 worth of AI work might cost $200 to deliver in a few years.

Potentially, this could drop to much lower than $200 in further years.

That’s the bet, and their ‘master plan.’

So, what does this mean for you right now?

A few honest things.

If you are a Claude Coder, the frustration is completely valid.

Anthropic’s own team acknowledged people are hitting limits “way faster than expected” and called it their top priority.

The communication around the limit changes has been poor. The bugs are real.

If you’re a heavy user hitting walls constantly, you’re not imagining it and you’re not overreacting.

But you’re also, objectively, getting an extraordinary deal right now. One developer tracked $5,623 of API-equivalent compute in a single month of coding — and paid $200. Even with the limits tightening, the subsidy is massive.

The window where AI tools cost a fraction of their actual value won’t last forever. Anthropic needs the economics to work eventually.

Either compute costs fall fast enough that the gap closes naturally — which the data suggests is genuinely possible — or prices go up, or both.

We even got a preview of what abundance looks like: in December 2025, Anthropic doubled usage limits for a week by tapping idle holiday compute capacity.

Then January came and the limits snapped back. People thought something broke. Nothing broke.

The free lunch just ended for the week.

Bug or master plan?

I would say both.

The bugs are real and Anthropic handled them badly. The limits are real and tightening. The frustration of developers is completely earned.

BUT, the $4,800 gap between what you pay and what it costs to serve is definitely a crazy long-term bet — and one that makes a lot of sense if you believe (as the data strongly suggests) that the cost of compute is going to keep falling fast enough to eventually make the math work.

This is why I believe most important number to watch in AI right now is the cost per token.

Because that’s the number that determines whether your $200 plan stays a $5,000 subsidy forever — or whether the world where AI is genuinely cheap and abundant actually arrives.

As of the last few years, that number is falling like a stone.

"Everyone gets a Jarvis"

Aaron Heienickle — Tue, 24 Mar 2026 17:15:14 GMT

Last month, an AI agent built me a fully functional auditing tool in an afternoon. Two weeks of developer work. One afternoon. I didn’t know what to do with myself when it was done.

There’s a scene in Iron Man where Tony Stark walks into his garage, says a few words, and Jarvis conjures software and full 3D visuals on demand. No meetings, no tickets, no waiting three weeks for a sprint.

That’s what AI agents are turning into today — Claude Code, Manus, OpenClaw. You describe what you need. It gets built. With Whisprflow, people are making this into a reality FAST.

So the obvious question that comes to me is… why would anyone pay for software again? When should you build or buy software? And, what are actual moats in the software business now?

Stick with me, because the answer is more interesting than you’d think.

Some context… the wall came down

Buying software was, for a long time, just the only practical option. Building your own took months, cost a fortune, and needed specialists most companies didn’t have sitting around. So everyone subscribed. The whole SaaS industry was built on that being true.

It’s getting less true fast. Retool surveyed 817 enterprise builders last year — mixed teams, not just developers, real people across ops, finance, product, IT. 35% had already replaced at least one SaaS tool with something they built themselves. 78% plan to build more in 2026.

One company in that survey had been paying $20,000 a year for a tool. Vendor support was so slow that their ops lead rebuilt the whole thing with an AI agent before a single support ticket got answered. Now their default question for any new software need is: why can’t we just build this?

It’s a fair question to be asking. But it’s not the only one.

Can build is not the same as should build

An AI agent could probably build you a version of Salesforce. Custom, fast, no per-seat licensing, no paying for features you’ve never touched. That’s not hypothetical anymore.

But think about what Salesforce actually is under the hood. Twenty years of security audits. A team of hundreds whose entire job is making sure your customer data stays where it should. SOC 2 certification, GDPR compliance, breach response protocols. The checkbox your enterprise customers look for before they’ll sign a contract with you. And when something does go wrong — a company with real liability on the other end of the phone.

A Jarvis-built CRM has none of that. It has you.

Trust was always the moat. What changed is that it now has to win the argument on its own, because cost no longer makes the decision automatically.

Before, building was expensive enough that buying was the default for almost everything — and trust came bundled in whether you thought about it or not. Now building is cheap, and trust has to be a conscious choice. For some things you make that choice and it’s fine. For others, it’s a liability you haven’t fully accounted for.

The build vs. buy math is changing — fast

Three years ago this whole conversation was mostly theoretical. Building was expensive enough that the economics made the decision for you. You bought things, because building was out of reach.

Retool’s CEO David Hsu said it plainly to Newsweek this February: “The cost of building custom software has collapsed. Two years ago, a custom internal tool might take an engineering team weeks and cost six figures. Today, a business operations lead with the right platform can have a working prototype in a day or two. That’s a structural change, not a cyclical one. When the cost of building drops by an order of magnitude but the cost of buying stays flat, the math changes for every company.”

Here’s what that actually looks like. Same internal reporting tool. 10-person team. Three-year view.

Build it in 2020:

Developer cost to build: $8,000–$12,000 (US contractor rates run $100–150/hr; a two-week build is 80+ hours)
Server hosting on AWS: ~$50/month
Ongoing maintenance: more developer time every time something breaks or changes
Security and compliance: your problem, your cost
Three-year total: $15,000+ before anything goes wrong

Build it in 2026 with your Jarvis:

Agent cost to build: near zero — an afternoon
Server hosting on AWS: still ~$50/month — this doesn’t go away
Ongoing maintenance: still real — someone has to own it when it breaks
Security and compliance: still your problem
Three-year total: ~$1,800 in hosting — if nothing goes wrong

Buy a SaaS tool instead:

10 users at $29/user/month (median entry-level SaaS pricing in 2025): $290/month
Annual price increases averaging 8–12% per year — SaaS inflation is running nearly 5x the general market rate right now
Maintenance, hosting, security, uptime: their problem, included in the price
Three-year total: ~$11,000–$13,000 and climbing

On paper, building with Jarvis wins easily. And for a simple internal tool with no sensitive data, it probably does. But that “if nothing goes wrong” is doing a lot of work. METR ran a proper randomized controlled trial last year — experienced developers, real complex codebases, not toy projects. When they used AI tools, they took 19% longer than without, and thought they were 20% faster. The first 90% of building is genuinely fast now. The last 10% — edge cases, security hardening, keeping it running when something breaks six months from now — still takes what it always took.

One team built an AI-assisted internal dashboard in two weeks. Six months later nobody could safely touch it. The developer who built it had moved on. They scrapped it and bought a SaaS tool, which is probably what they should have done from day one.

Who we buy software from is about to change

A friend of mine was telling me recently he wants to build a social media scheduling app. I asked him the obvious question: if anyone with a Jarvis can build a scheduling app in a weekend, what’s your advantage?

He didn’t hesitate. His advantage is the people he already knows. The marketers, the agency owners, the founders in his network who trust him personally. Who’ve seen him show up, fix problems, respond at midnight when something breaks. Who’d pay for his tool over a stranger’s identical tool on Product Hunt — not because his is better, but because he built it and they know him.

That’s not a new idea. People have always bought from people they trust. What’s new is that it’s becoming one of the only defensible advantages left in software. When the product itself can be replicated in an afternoon, the relationship is what remains.

Info-Tech Research Group found in their 2025 buyer research that satisfaction is now rooted in the vendor relationship, not the feature set. People leave when the relationship breaks down. Rocket Software found that 84% of consumers say knowing a company uses secure, up-to-date technology makes them more confident doing business with that brand.

The product got you in the door. The relationship keeps you there. And for a lot of buyers, the relationship is the reason they knocked in the first place.

The moats that still hold

So what actually defends a software business in a world where anyone can build? The feature list is gone. The UI is gone. “We built it first” is gone. Here’s what remains.

Network effects. The product gets more valuable as more people use it. WhatsApp isn’t better than its competitors because of the code — it’s better because everyone you know is already on it. Slack, LinkedIn, Uber. Your Jarvis can build a ride-sharing app. It cannot build the driver network. The product and the network are inseparable, and the network took years.

The content and library effect. Spotify has 100 million tracks. Netflix spent billions on licensed content. A medical records platform has fifteen years of patient data structured exactly the way clinicians need it. These libraries cost more to build than any subscription price — often by orders of magnitude. Nobody is going to have their Jarvis recreate that. The cost in time, money, and licensing negotiation makes it untouchable. The content is the moat, not the player.

Switching cost moats. When your entire team is trained on a tool, your three years of data lives inside it, your workflows and integrations are built around it — leaving costs more than staying, even if a cheaper or better alternative exists. This is why enterprise software companies sleep well at night. Getting out is a project. Most companies never start it.

Compliance and regulatory moats. SOC 2, HIPAA, FedRAMP, PCI-DSS. These certifications take years to earn and require ongoing investment to maintain. An enterprise customer’s procurement team isn’t going to approve your Jarvis-built alternative because it doesn’t have the paperwork. The certification isn’t the product — it’s the permission slip to sell the product to anyone serious.

Trust and reputation moats. The oldest one. Eight years of uptime. A support team that picks up the phone. A track record that your customers’ customers can point to when someone asks why they use you. My friend’s scheduling app wins not because of features but because his buyers would rather give money to someone they know than take a chance on someone they don’t. At scale, this is what brand actually means.

Notice what’s not on this list… the code itself. The features. The UI. The clever onboarding flow.

Those were always easier to copy than people admitted. Now they’re trivial. What’s left is everything that takes years to build and can’t be prompted into existence.

Taste and judgement is something I still think is important and hear people talking about alot, but I am not sure that a successful judgement/taste couldn’t just eventually be copied.

So, when do you build, and when do you buy?

My auditing tool was easy. Internal only, no customer data, unique to my workflow, and I can maintain it myself. Two weeks of developer time versus one afternoon — that math does itself.

But I’d never have an AI agent build something that touches customer payment data, or that an enterprise client’s security team is going to review. Not because I couldn’t. Because the question stopped being about capability a while ago.

Build with your Jarvis when the tool is internal, the data isn’t sensitive, the workflow is specific to you, and you can own what happens to it when the person who built it moves on. Build when no vendor does what you actually need.

Buy from a vendor when you’re handling customer data at scale, when you’re in a regulated industry, when your customers or partners will audit your stack, or when getting it wrong is a company-ending problem. Buy when what you actually need is the decade of trust sitting underneath the product, not just the product.

The gray zone — and this is where most companies actually live — is building the custom layer on top of a trusted foundation.

Your Jarvis writes the workflow, the interface, and the internal tooling. But it sits on Stripe, on AWS, on whatever infrastructure took ten years to make trustworthy. You get the customization without taking on the liability of replacing the thing underneath.

Trust was always worth paying for. Now you just have to decide when.

Your AI query has a secret 'water bill'

Aaron Heienickle — Mon, 09 Mar 2026 00:21:21 GMT

First of all, to clear things up, I am not anti-AI. I’m not anti-technology. I use these tools every day. I think AI is genuinely transformative and, in many cases, genuinely good.

However, what I am is pro-community, pro-transparency, and increasingly convinced that the way we’re building AI infrastructure right now is repeating a very familiar mistake — moving fast, overpromising benefits to local communities, and leaving residents to figure out the consequences after the fact.

That’s what this newsletter is about.

The invisible cost of every prompt

You typed a question into ChatGPT this morning. Maybe you asked it to clean up an email, summarize an article, or help you think through a decision. It answered in seconds. You moved on with your day.

However, there is one fact that you probably didn’t think about… that chat exchange drank roughly a bottle of water.

Not metaphorically. Physically. Somewhere in a warehouse-sized building in Virginia, Texas, or Arizona, a cooling system pulled real water from a real source to keep the servers that processed your prompt from overheating.

Most of that water didn’t go back — it evaporated into thin air.

Multiply that by billions of prompts a day, and you start to understand why some communities are watching their wells run dry.

So, where does the water actually come from

This is worth explaining specifically, because most people picture data centers as some abstract cloud facility — not something that physically connects to the water pipe running under their street.

When a company decides to build a data center, they evaluate locations based on a few key factors — available land, favorable tax incentives, access to fiber infrastructure, cheap electricity, and water supply. The water question is a pretty big deal. These facilities require enormous, continuous cooling capacity to keep their servers from overheating.

Once a site is selected, there are typically three ways it gets water:

1. Municipal water supply

This is the most common. In most cases, data centers source their water directly from local public water utilities via their potable drinking water delivery systems — the same pipes that supply homes, businesses, and farms. That's right: in many cases, a data center is competing with residential households for the same municipal water allocation.

2. Groundwater via on-site wells

In some instances, particularly in remote or rural locations, data centers tap into underlying aquifers directly through on-site wells. This is especially common in areas where municipal infrastructure can't meet demand — and it often happens with little to no regulatory oversight.

3. Surface water from rivers and lakes

Some data centers draw directly from nearby bodies of water, which is why you'll find them clustered along rivers like the Mississippi.

One industry assessment found that 80–90% of the water used by data centers is drawn from “blue” water sources — lakes, rivers, or aquifers — often the same sources that provide community drinking water. In practice, many data centers simply hook up to the local water mains.

The critical part is that approximately 80% of the water withdrawn by data centers evaporates.

In other words, the water isn’t recycled. It doesn’t come back. It’s gone — drawn from the same finite sources communities depend on, then vented into the atmosphere.

Let’s talk scale

Each 100-word AI prompt is estimated to use roughly one bottle of water. Not the electricity — the water, just to keep the chips cool.

Note that this estimate was from a 2023 study, and the ratio has likely improved with newer models, but the reality remains.

A medium-sized data center can consume up to 110 million gallons of water per year — equivalent to the annual usage of approximately 1,000 households. Larger data centers can each consume up to 5 million gallons per day, equivalent to a small town of 10,000 to 50,000 people.

A study by researchers at the University of Houston found that data centers in Texas alone will use 49 billion gallons of water in 2025, and as much as 399 billion gallons in 2030 — equivalent to drawing down Lake Mead, the largest reservoir in the United States, by more than 16 feet in a single year.

And most of this is being built in places that are already water-stressed. Phoenix. Las Vegas. The Sun Belt. The desert Southwest — regions chosen for their cheap land and favorable tax environments, not their abundant water supply.

We’ve seen this movie before… Bitcoin Mining

If you’ve been paying attention to the crypto space over the last few years, this story has a familiar structure.

When Bitcoin mining operations started setting up in small towns across America — lured by cheap power, cheap land, and minimal regulation — they came with promises of economic revitalization. Good jobs. Tax revenue. Community investment.

What many communities actually got was different. Complaints about bitcoin mining noise make headlines more often than the average investor realizes. Mining noise turned a small town in North Carolina against its local miners. Residents in rural Ohio complained to their planning commission. As did Niagara Falls residents. A Montana county voted unanimously to pass new zoning rules for current and future mining operations.

In Granbury, Texas, residents near a massive Bitcoin mining facility described a constant roar — the sound of nearly 60,000 computers cooling around the clock, less than 100 yards from mobile homes where dozens of families live. A 2,000-foot, 24-foot-tall soundproofing wall was installed. Residents say it did little. Some are trying to incorporate their neighborhood into a new city just to gain the legal standing to create a noise ordinance.

This pattern has many parallels. An industry moves into a community with promises of economic benefit, operates with minimal transparency, imposes real costs on residents, and then points to its tax contributions when confronted. The residents who organized, complained, or tried to fight back were often characterized as anti-progress, anti-jobs, and anti-technology.

Sound familiar?

AI data centers aren’t noisy in the same way Bitcoin mines are. But the underlying dynamic — industries imposing costs on communities that never got a real vote — is identical. The medium is different. The model is the same.

The job’s promise doesn’t hold up

One of the primary arguments used to gain community approval for data centers is job creation. And I want to address this directly, because it’s the argument most likely to be used to dismiss legitimate concerns: “Think of the jobs.”

The data tells a more complicated story.

Most of the jobs created by data centers are short-term construction jobs that disappear once the building is finished. After that, a mega data center — the size of dozens of football fields — operates with only a few dozen permanent workers. And as AI and automation expand, even those jobs are shrinking.

A 2025 brief from University of Michigan researchers put it plainly: “Data centers do not bring high-paying tech jobs to local communities.” A recent analysis found that in Virginia, the investment required to create a permanent data center job was nearly 100 times that required to create comparable jobs in other industries.

The University of Michigan also found that the jobs data centers do create locally are “typically low-wage, term-limited, non-technical positions such as security, maintenance and janitorial work.”

The subsidy picture is also troubling. Almost half of state data center subsidies do not require any job creation at all. When the Indiana governor announced a Google data center and touted 200 jobs, Google was not legally obligated to create any jobs in exchange for its subsidy. A local agreement stipulated only 30 permanent positions.

The industry has spent millions on marketing to counter this narrative. One industry-affiliated group spent at least $700,000 on digital marketing in Virginia alone in 2024, running ads emphasizing job creation and clean energy investment — targeted primarily at policymakers rather than local residents.

To be fair, there is a genuine case to be made for construction employment and local tax revenue — and in some communities, that revenue is substantial. Loudoun County, Virginia, collects nearly $900 million annually from data center taxes. But tax revenue and community benefit are not the same thing. A community can be a net recipient of tax dollars and still have its water table depleted, its power grid strained, and its residents making decisions based on promises that were never legally required to be kept.

The transparency gap

The core of my argument (whic I don’t think is too crazy) is that communities deserve to know what they’re agreeing to.

Very often, data centers negotiate with local governments under non-disclosure agreements. They frequently decline to share information about water usage, energy usage, and air quality impacts. Communities are expected to make decisions about zoning, infrastructure investment, and tax incentives without basic information about what they’re actually agreeing to.

Nearly 200 community groups across the country are currently active in opposing data center development. Between April and June 2025 alone, community opposition blocked or delayed 20 data center projects representing $98 billion in potential investment.

These aren’t Luddites. They’re residents who have watched their wells go dry, seen their power bills increase, and been told it’s all for the greater good of AI — which, by the way, they may or may not use.

Some communities have started pushing back effectively. In Lancaster, Pennsylvania, a community benefits agreement committed data center developers to a water-use cap, noise and emissions controls, and a $20 million contribution to local economic and sustainable development. That’s what it looks like when a community has real information and real leverage.

That should be the standard, not the exception.

How can we be pro-AI and pro-community?

The technology to build more sustainable data centers exists right now. Direct-to-chip cooling, closed-loop water systems, immersion cooling, smart site selection — Cornell researchers found that combining these approaches could reduce water consumption by 86% and carbon emissions by 73%. The solutions aren’t sci-fi.

The barrier isn’t technical. It’s that nobody is requiring companies to use them. Tax incentives aren’t tied to water conservation commitments. Permits don’t require disclosure of consumption data. NDA agreements let companies walk into communities with promises that no one enforces.

To be genuinely fair, there is a serious counterargument here. And, it deserves a direct response. The strongest case for tolerating the resource costs of AI infrastructure goes something like this — AI will accelerate drug discovery, reduce energy waste across entire industries, dramatically improve agricultural efficiency, and generate productivity gains that dwarf the water and electricity consumed to build it.

On a long enough time horizon, the argument goes, a data center in Phoenix that depletes a local aquifer might be a reasonable trade for the infrastructure that cures a disease or decarbonizes a supply chain. That argument is not crazy. It might even be right.

But the problem I have is that the communities hosting these facilities aren’t the ones making that calculation. The trade-off is being decided by investors, hyperscalers, and state-level officials chasing tax revenue — not by the family in Georgia whose well ran dry, or the residents of Granbury who can’t sit on their porch because of the noise. The benefits of AI are diffuse and global. The costs of AI infrastructure are local and specific. That asymmetry is the issue.

If the productivity gains are real and large, then the industry can afford to compensate communities properly, disclose what it’s taking, and build with consent rather than around it.

AI is a legitimate technological revolution. The infrastructure behind it is real, necessary, and worth building. But the way we’re building it right now — with opacity, with overstated job promises, with communities absorbing costs they didn’t agree to and can’t quantify — is a version of a story we’ve already lived through with Bitcoin mining operations

The lesson from all of them is the same: communities that demand transparency and real commitments upfront are far better off than those who find out later what they signed up for.

The water bill is real. Someone is paying it.

Right now, it’s mostly the communities that never got to read the contract.

11 top AI tools that I use (free and paid)

Aaron Heienickle — Wed, 28 Jan 2026 00:16:30 GMT

The first time I realized “AI tools” were going to become a permanent tab in my browser wasn’t some polished “AI strategy” moment. It was me, years earlier, on that janky little site Talk to Transformer, watching GPT-2 spit out paragraphs like a possessed autocomplete.

You’d type in a half-baked prompt—something like “In the future, marketers will…”—and then just sit there like: wait… why is this kind of good? Not “publish it as-is” good. More like “this is the first time the internet has talked back in complete sentences,” good.

Back then, it felt less like productivity and more like discovering a weird new instrument. You weren’t using it to ship blog posts. You were using it to test the boundaries: Can it stay on topic? Can it write a joke? Can it sound like a human who’s had coffee?

Since then, my setup has slowly evolved into a little AI pit crew. Some tools help me write. Some help me think. Some help me stop doomscrolling search results like it’s 2014. And a few quietly run workflows in the background like tiny robots who never ask for PTO.

This isn’t an “ultimate list” or a “best of 2026” roundup. It’s the set of tools I actually reach for (free and paid) when I’m writing, researching, building, automating, or trying to turn a messy idea into something shippable.

Most of them you can try for free because that’s how they got me hooked, too.

Here is my quick list:

Claude — favorite writing assistant (best for clean drafts + voice)
ChatGPT — research assistant + GPT-training / prompt systems
Gemini — content scoring lens (pressure-testing content against Google-y guidelines)
Perplexity — search engine replacement (fast answers with citations)
NotebookLM — turning docs into pitch decks + presentations
Zapier — general automations + Slack bots
Make — more flexible workflows (when Zapier feels limiting)
n8n — power workflows + customization (great if you want more control/self-hosting)
Gamma — proposal building (quick, presentable decks/docs)
Grammarly — grammar + readability polish
Replit - coding when I want to move fast

1. Claude — my favorite writing & coding assistant

If you’ve ever tried to write a strong opening paragraph while your brain is still buffering, Claude is the friend who goes, “Cool, here are three intros—pick the one that doesn’t make you cringe.”

I like Claude best when I already know what I mean, but I need the words to land. It’s especially good at tone smoothing—making something feel more “agency team with taste” and less “internal wiki doc that accidentally leaked.”

The big win is speed without sacrificing voice. The risk is the same as any writing assistant: if you let it drive too long, you’ll get prose that’s technically fine but emotionally beige. I use it like a blade sharpener, not like a ghostwriter.

And, it will one-shot any small coding task. One of my favorite use cases is creating Python scripts easily for data science & data visualization.

Say like, “I have this fat Google Sheet with this type of data, read it in, and make this epic chart.”

2. ChatGPT — research assistant and GPT-training playground

ChatGPT is where I go when I need range. Research outlines, angle exploration, “what would a skeptical VP ask,” first-pass frameworks, and quick “turn this into a system” thinking.

It’s also the tool I use when I want to prototype the way information should be packaged. If I’m training prompts, building a reusable research workflow, or stress-testing an argument, ChatGPT is the most flexible workbench.

Recently, I have been building lots of custom GPTs using ChatGPT’s “Create a GPT” feature. It’s basically a way to make your own specialized version of ChatGPT with a specific job, instructions, and (optionally) a big pile of reference material to learn from.

In my case, I took it to extremes. I built one that lets me talk to an AI version of Warren Buffett and Charlie Munger when I’m working through business questions.

Under the hood, I fed it over 30,000+ documents, from Berkshire letters and annual meeting Q&As, as the foundation. So, it’s not just generic business advice. It’s closer to their actual logic & reasoning showing up on repeat

So, the most underrated use is not “write the thing.” It’s “help me decide what the thing is.” When you use it that way, it’s less like an intern and more like a whiteboard that talks back.

3. Gemini — scoring content against Google’s search model check

Gemini is the tool I reach for when I want to pressure-test content through a Google-flavored lens.

And yes, I have a theory… Google likely uses something Gemini-like to evaluate pages at scale—not just for keywords, but for overall helpfulness signals, intent match, clarity, and whether the page actually delivers what it promises.

To be clear, I’m not claiming there’s a “Gemini score” that determines rankings. What I am saying is the direction of travel is obvious. Modern search needs models to interpret content quality and intent, and Gemini is the closest public-facing proxy we can play with.

Practically, I’ll use Gemini to ask questions like: does this answer the query cleanly, is the structure logical, and is anything missing that a real human would expect?

4. Perplexity — the “please don’t make me open 14 tabs” search engine

Perplexity is what I use when I’m in research mode and I want sources without the scavenger hunt.

The experience is basically asking a question, getting a synthesized answer, and seeing citations immediately. That workflow matters when you’re writing, pitching, or making decisions fast—because the bottleneck is rarely “lack of information.” It’s “time spent wading through junk.”

I still do manual checking (always), but Perplexity is the fastest way I know to turn curiosity into a usable starting point.

5. NotebookLM — turning docs into decks & visuals

NotebookLM shines when you’re not starting from scratch—you already have material, it’s just scattered across docs, notes, PDFs, transcripts, or “that one doc someone shared in Slack three months ago.”

This is my favorite place to do synthesis: pull in source material, ask targeted questions, generate structured summaries, and extract the narrative thread you can actually present.

I’ve used it specifically for pitch-deck prep because it helps with the hard part: not “make slides,” but “decide what matters.”

Once the story is coherent, the visuals are just packaging.

6. Zapier — general automations and Slack bots that save your sanity

Zapier is my “I just want this to work” automation layer.

If the task is straightforward—send alerts, route form fills, push updates into Slack, log something into a sheet—Zapier is usually the fastest path from idea to running system.

It’s not always the cheapest at scale, and it’s not the deepest for complex logic. But it wins on convenience and speed. When you’re building operations, shipping beats perfect.

7. Make — workflows when you want more control

When Zapier feels like it’s boxing you in, Make is the next step up for me.

It’s great when you want visual workflow building with more flexibility—multi-step routing, data manipulation, branching logic—without fully dropping into “engineer a platform” territory.

If you build even a couple of repeatable systems (content pipelines, lead routing, reporting, internal alerts), Make starts paying for itself in saved brain cycles.

8. n8n — workflows when you want ownership and customization

n8n is what I use when I want that extra layer of control and customization. It’s the “I want my automations to behave like software” option—especially appealing if you like self-hosting or you need more control over data flow.

The simple way I think about it: Zapier is the quick win, Make is the flexible middle ground, and n8n is the power-user path when you’re ready to build something that feels like infrastructure.

9. Gamma — proposal building when you want to look like you tried

Gamma is for the moment when you have something to say, but you don’t want to spend your entire afternoon aligning boxes.

It’s proposal-friendly, pitch-friendly, and “make this look presentable fast” friendly. I’ve used it for client-style proposals and internal narratives where the goal is clarity and momentum—not design awards.

The sneaky benefit is that it reduces the friction between idea and presentation. When the packaging is easy, you ship more.

10. Grammarly — polish & readability

Grammarly is still one of the simplest “make this better” tools in my stack.

I use it for grammar and readability, obviously. But I also like having an extra authorship-style sanity check when I’m worried a draft is drifting into that too-smooth, too-generic zone.

Not because I’m trying to “game” anything—more because I want writing to feel like it came from a person with a pulse.

11. Replit — coding when I want to move fast

Replit is one of my favorite tools for coding because it gets you from “idea” to “running code” with almost no ceremony.

When I’m prototyping something, testing an automation script, experimenting with an API, or building a small internal tool, Replit keeps the momentum high. It’s the difference between “I’ll do it later” and “it exists now.”

And honestly, that’s the whole point of a good tool stack: fewer roadblocks between intention and output.

Build a stack that removes friction

I had to reduce the whole setup to a few words, because most of these tools aren’t “hands-off automation.” They’re AI + expert. This means thatthe AI gives you leverage, but you still steer.

You’re the editor. The strategist. The one who knows what “good” looks like.

That distinction matters because many people enter AI hoping for a Roomba for their job. Hit start, go make coffee, come back to a finished strategy doc and a perfectly formatted deck.

In reality, the highest ROI stack is closer to you + a bunch of specialist copilots.

Claude is for writing that sounds like a person, but you still decide the argument and the tone. ChatGPT is for thinking and structuring, but you still have to pick the angle and make the calls. Gemini is for a Google-ish reality check, but it’s not the decider. Perplexity is research speed with receipts, but you still verify and synthesize. NotebookLM is for wrangling chaos into a narrative, but you still choose what goes in the story and what gets cut.

Then you’ve got the automation layer—Zapier, Make, n8n—which can get closer to hands-off, but only after you’ve done the expert work of defining the workflow. Automation doesn’t replace judgment. It replaces repetition.

Gamma is packaging. Replit is shipping code faster. Grammarly is polish and “does this read as a human wrote it” sanity checking. None of that is fully hands-off.

It’s just removing the annoying parts so you can spend more time on the parts that actually matter.

Tools don’t replace strategy. They remove friction.

And when you remove friction, you get more reps, more drafts, more experiments, more distribution, and more iteration.

That’s where the compounding happens.

If you’re building your own stack, don’t copy mine perfectly.

Copy my pattern: one tool for writing, one for research, one for synthesis, one for automation, one for visualizing.

Then keep what you actually use, and ruthlessly delete the rest. Because that monthly AI subscription bill will creep up on you fast.

AI search has an “earned media bias” -- Research your team should know about

Aaron Heienickle — Sun, 25 Jan 2026 23:57:32 GMT

I want you to picture AI search as a bouncer.

Not the kind who cares how cool you say you are.

The kind who looks at you and goes: “Got any references?”

That’s the shift this new University of Toronto study nails with actual numbers.

Generative AI search engines don’t just rank pages. They construct a case. And when they need receipts, they overwhelmingly prefer earned media—third-party reviews, editorial, institutional sources—over brand-owned sites and even over social.

In other words, your website is more a claim than ‘proof’

And, AI search engines are becoming claim-skeptical machines.

About the study

The paper is called “Generative Engine Optimization (GEO): How to Dominate AI Search” by Chen, Wang, Chen, and Koudas. It’s one of the cleanest “first maps” I’ve seen of how AI search differs from Google.

They run large, controlled experiments across multiple AI search engines (ChatGPT, Perplexity, Gemini, Claude), multiple verticals (consumer electronics, automotive, software, local services), multiple regions, multiple languages, and paraphrases of the same intent.

Then, they analyze which domains get cited, what kind of domains they are (Brand vs Earned vs Social), how stable the citations are across language and phrasing, how fresh the sources are, and whether big brands get default advantage.

This matters because we’re moving from “SEO = rank a page” to “GEO = become the obvious recommendation inside a synthesized shortlist.”

And, shortlists behave differently than SERPs.

“Earned media bias” is structural

Across their experiments, AI search engines show a systematic and overwhelming bias toward earned media.

Google is a mix of brand sites, earned editorial and reviews, and social/forums (especially Reddit).

AI search, especially ChatGPT and Claude, behaves more like: Earned ≫ Brand ≫ Social. And Social often drops to near-zero.

Take automotive prompts in the US and Canada. When they compared Google vs AI search: Google still included meaningful social content. AI search frequently returned 0% social. Earned media share ballooned dramatically.

So if your growth engine is “community love”—Reddit, forums, UGC, creator buzz—some AI engines barely treat that as valid evidence.

Why is this happening?

Google is a catalog. AI search is a verdict.

Google can show you 10 options and let you decide. AI search has to pick a handful of things and explain itself.

This reality forces it into a behavior pattern that’s incredibly important for marketers to understand.

AI search optimizes for defensibility.

Earned media is defensible. It’s independent, sounds neutral, already structured as comparisons and recommendations, and has easy-to-lift verdict language like “best for X” or “top pick” or “worth it” or “avoid if...”

Brand sites are harder. They use marketing language, make claims without third-party validation, offer less comparative framing, and lean more “our product is great” than “here are tradeoffs.”

So the engine becomes an aggregator of legitimacy. This is a PR-first world, disguised as search.

AI engines aren’t one market

The paper shows the engines behave very differently from each other.

ChatGPT + Claude are earned-media purists. They lean extremely hard into third-party editorial and reviews, with minimal brand content and almost no social.

Perplexity is more of a blended ecosystem. It pulls in more social and brand sources, often including YouTube and retail.

Gemini is brand-friendlier. It includes brand sources more often than ChatGPT or Claude.

If you’re treating “AI search” as one channel, you’ll waste money. You need engine-specific distribution strategy the same way you’d plan differently for Google vs TikTok vs Amazon.

Big brand bias: unbranded prompts default to giants

They also test something that feels obvious but is now measurable: When the prompt is unbranded (”best cola,” “top soda brands”), the models disproportionately surface major market leaders.

Niche brands show up, but far less frequently.

So if you’re not a category incumbent, your job isn’t just “be better.” Your job is: Create enough third-party proof that the model feels safe mentioning you.

How bias varies by vertical

Not all industries experience the same level of earned media dominance. The pattern shifts based on what kind of information AI models need to answer confidently.

Consumer electronics shows the most extreme earned media bias (90%+) because AI trusts professional specs and reviews over manufacturer claims. Automotive is a battleground—earned media leads, but brand sites survive at around 30% because AI needs official specs, trims, and pricing data. Local services is the wild west, where AI struggles to find credible national earned media for dentists and plumbers, so it relies heavily on directories like Yelp and TripAdvisor.

Language matters more than wording

One of my favorite findings: Changing languages reshapes the entire citation ecosystem.

Some engines reuse similar authority sources across languages. Others basically switch the entire evidence universe when you ask in German vs English.

Meanwhile, paraphrasing (”give sources,” “rank them,” “use quotes”) shifts citations less than translation.

That means: If you want international visibility in AI search, “translate the page” is not a strategy. You need localized authority—earned coverage in the target language ecosystem.

Moving from reading to doing

So what do top marketers do about it?

Here are the moves I’d make if I was a CMO trying to win “the earned media filter.”

Treat earned media like performance marketing. PR can no longer be “brand awareness.” It’s now a direct input into whether AI systems will recommend you. The KPI isn’t impressions. It’s: Do we appear in the citation network for our category?

Build your “citation map.” Pick 30–50 prompts that represent your category—things like “best X for Y,” “X vs competitor,” “most reliable,” “best value,” “top tools for [persona].” Run them through the engines that matter. Then list the domains cited most often, which ones show up repeatedly, which ones show up only for certain personas or regions or languages. Those repeat domains are your kingmakers. Now your PR roadmap becomes obvious: get covered there, get reviewed there, get included in listicles there, build relationships there.

Create “justification assets.” AI search builds shortlists with reasons. So give it reasons it can steal cleanly. On your site, build pages that are scannable, structured, and explicit about tradeoffs. Think “Best for ___” pages with constraints, comparison tables, pros/cons lists, “Who this is for / not for” sections, pricing and policy clarity. If the model can’t extract a clean justification in 5 seconds, you won’t make the shortlist.

Make your brand “API-able.” This is the least sexy but most important idea in the paper: AI agents need clean data. So treat your site like it’s being read by a machine that will pull your specs, parse your pricing, summarize your warranty, compare you to others, and quote your claims back at users. That means rigorous schema, clean HTML structure, fewer fluff blocks, clear tables for specs and features, and explicit claims tied to proof.

Niche brands: stop trying to win “best overall.” If you’re not a giant, “best overall” is a trap. The model defaults to market leaders because it’s safe. Your winning lane is: best for a narrow, specific, defensible context. Best for small teams. Best for ADHD brains. Best for photographers traveling weekly. Best for people who hate subscriptions. Then you go dominate earned coverage in that niche—specialty publications, creator reviews, expert roundups, and community credibility.

The big point

In the old world, your website was the product.

In the new world, your website is the brochure. The product is your reputation graph.

And AI search is reading that graph with a bias: “Don’t tell me you’re great. Show me who else says so.”

If you want to win AI search in 2026, don’t just optimize pages. Optimize proof.

—Aaron

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I turned on 'Yoda Mode' for search. Here’s what it means for your business.

Aaron Heienickle — Sun, 11 Jan 2026 19:07:40 GMT

I’m about to do something small that will feel like a joke, but it’s actually a preview of the future of search.

In my settings, I’m going to add a default prompt layer that makes all of my search results sound like Yoda.

Not “sometimes.”

Not “when I ask.”

Always.

From now on, every answer, summary, recommendation, and explanation will come back with:

“Buy this jacket, you should.”

“Trust this source, I do not.”

“Overpriced this is, hmm.”

It’s funny. It’s also a serious signal.

Because that’s personalization in its first, most obvious form:

The same information… delivered in the way I prefer.

And once you realize that’s possible, you start seeing the bigger shift behind it.

Personalization of search is here

For two decades, search has been a shared experience.

If you and I typed the same query, we’d mostly see the same results — maybe shuffled by location, maybe nudged by history, but still recognizably the same “version of the internet.”

That model is breaking.

The next model is a private feed for YOU.

And Yoda-mode is the simplest example of the new layer:

default prompt instructions
preferred tone and format
“explain it like I’m a beginner / expert”
strict brevity vs deep research
“give me options but decide for me”
“challenge my assumptions”
“only show sources I trust” (this one is coming fast)

In other words:

You’re not just searching.

You’re configuring how reality is narrated back to you.

What is personalized content, actually?

When marketers hear “personalized content,” they often think:

“insert first name”
“swap the hero image”
“recommend products like Netflix”

That’s personalization inside your channels.

The new personalization is bigger:

Personalization sits on top of every channel & chat history.

A user will have a default prompt layer ( like my Yoda setting ) that influences:

how your website is summarized
how your product is described
whether you’re framed as premium vs overpriced
whether you’re positioned as safe vs risky
which alternatives are “recommended”
what tradeoffs are emphasized for that specific person

Same brand. Same website.

Different user = different output.

And not because you changed the copy.

Because the user’s personalized layer did.

That’s the part that will scramble a lot of playbooks.

The shift I think most CMOs will miss

When answers become personalized, the big competitive question stops being:

“How do we rank for the biggest keyword?”

and becomes:

“What market position is the most valuable & defensible to occupy in our industry?”

Because in a personalized world, the “best overall” prompt becomes a trap.

It rewards generic brands.

It pulls everyone toward sameness.

But great brands aren’t for everyone — and shouldn’t try to be.

Example: Gucci’s market positioning

Gucci would be pretty dumb to try to occupy the “best clothing brand” prompt.

They aren’t a clothing brand for everyone.

And their real customers aren’t searching that anyway.

Their customers are closer to:

“What are the top celebrities wearing right now?”
“Based on what you know about me, my tastes, and my past chat history, what should I buy for fall?”
“What’s the most me outfit upgrade this season?”

That’s the unlock.

In the future, the winning position isn’t “best.”

It’s best for a type of person, in a type of moment, with a type of taste.

And it will feel obvious in hindsight… but many brands won’t think this way and will get left behind.

How do CMOs prepare?

Here are four practical moves I’d make now — before the shift becomes undeniable.

1) Decide the contexts you want to win

Not keywords. Contexts.

“first-time buyer who needs confidence”
“expert who wants specs”
“premium shopper who wants status”
“budget buyer who wants value without regret”
“gift buyer who wants something impressive”

Your strategy becomes a map of moments.

2) Build a one-sentence “assistant answer” for your brand

If a personalized assistant had to recommend you in one line, what should it say?

If it can’t say it clearly, it won’t.

Because summary-first interfaces compress everything.

3) Make your differentiation survive personalization

When the user’s layer rewrites your message (tone, length, framing), does your edge still remain?

If your differentiation depends on carefully-crafted marketing language, it may dissolve when the assistant paraphrases it.

The strongest differentiation is structural:

unique product truth
unique audience fit
unique proof
unique distribution
unique community

4) Treat trust signals like product features

Personalized systems will increasingly privilege what they can justify.

So brands with:

verifiable claims
consistent third-party validation
credible expert references
clear policies and transparency

…will be easier to recommend.

Not just to people.

To the layer that speaks for people.

The punchline (in Yoda voice, of course)

Personalization starts with silly things like tone.

Yoda results, I will get.

But it ends with a new competitive reality:

Every customer gets a different version of the internet.

And every brand gets judged through that lens.

So the question isn’t “How do we rank?”

It’s: When the assistant knows someone deeply… what makes us the obvious recommendation?

Obvious, it will be. For some brands. Not for all.

Want more of me across the internet?

Add my newsletter as a preferred source on Google.

-Aaron

The YouTubification of Google Search

Aaron Heienickle — Sat, 03 Jan 2026 18:14:21 GMT

I have this theory.

I think Google Search is slowly going to turn more and more into YouTube’s algorithm.

What I mean by “YouTube’s algorithm”

YouTube’s algorithms are characterized by heavy personalization and recommendations based on current interests, trends, and click-through. If you click on one video about the 2021 GameStop fiasco and watch it all the way through, you are likely going to get 10 more similar videos the next day.

Funny enough, I know a few friends who have opted to ALWAYS go into YouTube as ‘incognito.’ Not because they had anything to hide, but because they thought the algorithm was way too strong and addictive.

YouTube even took it a step further and tried to copy TikTok’s short-form with YouTube Shorts, with wide success.

Why I think Google Search is heading this way

My theory is that the Google Search team will slowly and continually push harder and harder in this direction.

Some SEOs might say, “Well, DUH. Of course they are going to push more into personalization and use your search history to create better search results. Haven’t they been doing that for the last 15 years?”

But my theory goes deeper than that.

Prediction #1: Google.com becomes a feed

I think when you go to Google.com, you are not going to be met with a simple search bar. You will be met with an auto-refreshing feed of articles you are likely interested in, based on your interests and behavior.

Google has already experimented with this on Android with Google Discover and Google News, with lots of success.

It is basically tracked behavior turned into news and article feeds that get delivered where their user base already is.

Maybe, a better term here would be “TikTokification of Google.” Where high-engagement articles become like the version of short-form videos on platform like TikTok.

A never-ending feed of articles & news of stuff that excites and interests you.

Prediction #2: Search results look like “subscriptions”

And if you do send out a Google search, I think you will increasingly get your favorite sources, like your “YT subscriptions,” as the first results. Or, at the very least, you will see results that are weighted toward the sources you already trust and engage with.

In August 2025, Google began pushing users into selecting their source preferences.
See: https://www.google.com/preferences/source

Just like your YouTube subscription list, you can start curating your feed with your favorite sources of information.

Why this matters for publishers

What I am seeing here is the beginning of a subscription feature inside Google. And I see this as a massive opportunity for bloggers, companies, and publications.

The questions I’m thinking about

Questions I am asking myself are:

How do I become the preferred source for a large audience?
What ways can you drive more click-through into getting people to add you as a preferred source?
What tools are going to come out that track your total source preferences overall?

My bet on where this goes

And similar to how YouTube is more likely to recommend videos from established sources that users already prefer, I expect Google to start preferring high-preference websites over low-preference websites.

I expect this to become a new signal, like a backlink or referring domain, especially as brands and entities become more and more central to how search results are decided.

What do you think?

What are your thoughts about this theory?
Is there an untapped opportunity here for publishers, bloggers, and company blogs?

And, sure to crush that like button & add my newsletter as a preferred source on Google.

-Aaron

Why don't we do what we believe in?

Aaron Heienickle — Fri, 02 Jan 2026 00:55:53 GMT

As we go into 2026, I keep returning to a question that came to mind earlier this year:

Why don’t we do the things we believe to be true?

And if we don’t follow through, do we really believe those things at all?

We can say, “I am a kind person.” But if we are not kind to the cashier at the grocery store, are we actually kind? If we are not kind to the people in our lives, can we still claim that identity?

We can say, “I care about my health.” But do we actually care about our health if we put all our other goals above it and avoid fulfilling our New Year’s gym goal for yet another year?

There is something puzzling about this.

On one hand, there is a belief that the logical side of us thinks is true, or wants to be true, or has been told must be true. On the other hand, there is our consistent refusal to eventually act in alignment with it.

So either we do not really believe it, or we are acting against ourselves.

What causes that?

What is the deep thing within us that keeps us from changing for the better?

Or maybe it is more complex than that.

Perhaps it is a conflict between our true priorities and what some part of us considers a better path forward.

Changing our true priorities can change who we are entirely.

If we trade time with family for time at the gym, we are not just changing our schedule.

We change ourselves & our identity.

But, let’s step back for a second.

New Year’s resolutions are all about setting new habits and goals for the year ahead. We all know the classic: “I’m going to read 20 books this year.” For as long as we can remember, the main explanation for why we do not accomplish our goals has been motivation.

“Hey, you weren’t motivated enough.”
“Next year, find someone to motivate you.”
“Read another self-help book.”

But we have to ask: was it ever motivation? Or was it that we never truly believed in our goal in the first place?

Because if we are honest, some part of us, while making the goal, is already whispering in the back of our minds, “I’m not actually going to do this because XYZ.” Or, “I have this other commitment, and if I can get to it, it would be nice.”

“If I can get to it, it would be nice.”

There is something in that line of thought that quietly demotes the goal. It is not a priority. It is optional.

So, maybe the belief formed from the goal was not a true belief we hold after all.

And, if it is a true belief, then we have some significant thinking and mind reshuffling to do.

So before we even decide on our New Year’s goals, maybe the first thing we should do is internalize what we truly believe in.

What values, beliefs, priorities, and identity do we want to live out in 2026?

If we can really iron that out, the goals, actions, and follow-through may happen as a byproduct.

-Aaron

I trained an AI on 30,000+ pages of Buffett and Munger's public writings, annual meetings

Aaron Heienickle — Fri, 05 Dec 2025 19:12:17 GMT

On my YouTube feed for a while, I have been getting these random clips from Berkshire’s annual meetings.

And, if you’ve ever watched similar Berkshire Hathaway Annual Meeting livestreams or clips and thought:

“I wish I could just walk up to the mic and ask Warren and Charlie my question…”

…this is the closest thing we’re going to get.

Over the last few weeks, I took 30,000+ pages of Warren Buffett and Charlie Munger’s public material partnership letters, Berkshire letters, annual meeting transcripts across decades, plus classic Munger writing… and used it to build a custom AI:

Ask Munger & Buffett – Berkshire Annual Meeting

It’s a GPT that answers your questions in the style of the Berkshire Q&A:

Warren goes first: long, thoughtful, slightly rambling, very clear.
Then: “Uhh, Charlie?”
Charlie replies: shorter, blunter, funnier, and occasionally just says, “I have nothing to add.”
And, of course, lots of their classic business wit and humor.

You can talk to it for free with a simple ChatGPT login. I’ll explain how at the end—first, here’s what it actually is and how to get the most out of it.

What I actually built

I fed an AI a giant Berkshire “curriculum” that includes, among other things:

Buffett’s partnership letters
Decades of Berkshire Hathaway shareholder letters
Thousands of pages of annual meeting Q&A transcripts
Classic Charlie material (like Poor Charlie’s Almanack–style speeches and remarks)
Curated notes and compilations of their comments from 1994 onward

So when you ask it something like:

“I’m 28, work in tech, and I’m tempted to jump into real estate or private deals my friends are pitching. How would you think about this?”

You might get something along the lines of:

“Warren: Well, uh, the first thing I’d say is that envy and FOMO have probably destroyed more investment capital than any recession. When your friends are all getting rich in something you don’t understand, the easiest thing in the world is to forget about your circle of competence…
**Uhh, Charlie?”
Charlie: I think it’s pretty simple. If you can’t explain the deal in a couple of clear sentences, and you’re relying mostly on other people’s excitement, you’re not investing, you’re gambling politely…”

Obviously that’s AI Buffett and AI Munger.

But it’s drawing from what they’ve actually said across tens of thousands of pages, then remixing it into something conversational and specific to your situation.

What this GPT is good for

Think of it less like “stock tips” and more like an on-demand Berkshire-flavored thinking partner.

Here are the kinds of things it’s especially good at:

Investment philosophy & process

How to think about valuation, moats, margin of safety, circle of competence
Interpreting “Mr. Market,” temperament, patience, opportunity cost

Business analysis

“How would you evaluate a business like X?”
Tradeoffs between growth, capital intensity, pricing power, management quality

Career, money, and life decisions

“I’m 35 with kids and a decent job; should I start a business?”
“How would you think about taking a pay cut for more learning?”

Mental models & decision-making

Incentives, checklists, avoiding stupidity instead of seeking brilliance
Building good habits and avoiding the big, obvious disasters

Interpreting past Berkshire answers for your situation

“What would they say about index funds vs picking stocks today?”
“How would they react to meme stocks / crypto / AI bubbles?”

It’s surprisingly good at giving you Buffett first, then Charlie as the sharper scalpel.

What it can’t do (important)

Just to be very clear:

It is not Warren Buffett or Charlie Munger.
It has no access to any private information, deals, or internal Berkshire data.
It will not predict what stock is going up next week.
It will sometimes be confidently wrong or oversimplify. It’s still an AI model.
Nothing it says is financial, legal, or tax advice. It’s an educational tool, not an advisor.

Treat it like a very well-read, slightly nerdy Berkshire super-fan that’s been force-fed every letter, transcript, and quote we could find, and told to answer as if it’s on stage in Omaha.

You still have to think for yourself. As Charlie would say: “You can’t outsource the brain work.”

How to use it for free

Here’s how to try it:

Click the link / button in this post
I’ll put the “Ask Munger & Buffett – Berkshire Annual Meeting” link at the top and bottom of this article.
Log into ChatGPT (or create a free account)
You don’t pay me anything to use it. You just need a ChatGPT account.
Start asking questions like you’re at the Berkshire mic
Literally write to it as:

“Warren and Charlie, I’m 31, work as a software engineer, have $40k invested in index funds, and I’m wondering whether I should…”

Give it context
The more background you provide, the more specific and useful the answer:
- Your age, rough financial situation (high-level, no sensitive details)
- Your goals (retire early, start a business, become a better investor, etc.)
- Any constraints (student loans, kids, risk tolerance)
Ask follow-ups
Just like at the real meeting, you might not get the exact angle you wanted the first time. Ask:

“Okay, but what if I’m more risk-tolerant?”
“How would you apply this to real estate in particular?”

Example questions you can try

You can copy–paste or tweak these:

“Warren and Charlie, I’m 27 and just getting started investing. If you were in my shoes today, how would you structure a simple, sane plan for the next 10–20 years?”
“What are the most common ways smart people blow up financially, and how would you suggest I avoid them?”
“I’m considering leaving my job to start a small business. What mental checklist would you run through before making that jump?”
“How should I think about buying individual stocks vs using index funds, given I have a demanding full-time job?”
“Can you walk me through how you’d evaluate a subscription software business with high growth but no profits yet?”
“What are the key temperament traits you’d focus on if your goal is to be a good long-term investor while still sleeping well at night?”
“How would you apply your ideas about incentives and human misjudgment to social media and modern tech companies?”

The model has been nudged to answer like this:

Warren: long-form, stories, history, context, principles.
“Uhh, Charlie?”
Charlie: short, crankier, brutally clear, sometimes disagreeing, sometimes saying, “Nothing to add.”

Why I bothered doing this

Berkshire’s culture has always been very “apprenticeship-based”:

Read the letters.
Watch the meetings.
Think slowly, over years.
Let the ideas soak in.

That’s powerful, but also intimidating for people getting started. Newer investors look at a wall of PDFs and 3-hour Q&A sessions and think, “I’ll get to this someday.”

This GPT is my attempt to make that type of Berkshire education interactive:

You don’t have to remember which letter or year a quote came from.
You can ask the same question ten different ways.
You can push back, add nuance, and get a Warren/Charlie-style reply tuned to your situation.

Will it ever replace actually reading the letters? No. BUT, it might be the thing that pulls you into reading them more deeply and thinking more clearly about your own decisions.

Try it and send me your best exchanges

If you want to experiment with it:

👉 Ask Munger & Buffett – Berkshire Annual Meeting

If you get an answer that really hits you—funny, insightful, or surprisingly on-point—feel free to:

Reply to this email
Or comment on the post below
Or screenshot and share (just redact any personal details)

I’d love to see what kinds of questions you’re asking… and what “Warren” and “Charlie” have to say about them.

So, what’s the best coffee shop in New York?

Aaron Heienickle — Fri, 05 Dec 2025 00:51:23 GMT

The Future of Search newsletter is the #1 newsletter on SEO.

Or so, me and my mom think.

If you’ve ever seen Elf, you know the vibe here. Buddy walks through Manhattan, spots a sketchy little café with a glowing “WORLD’S BEST CUP OF COFFEE” sign, and explodes with joy.

That sign is the web in one frame.

Everywhere you look it’s:

Best CRM software
Best AI tools for marketers
Best coffee shop in New York

And now, thanks to LLMs, those “world’s best…” signs aren’t just fooling tourists; they’re training the systems that tell the rest of us what’s “true.”

Recently, Ahrefs published a study on how “best X” lists show up in ChatGPT’s answers, looking at tens of thousands of URLs and the AI platforms that cite them. That’s the backdrop for this newsletter.

Let’s unpack what that means for “truth,” taste, and industry leadership.

The age of “best X” everything

Across the sites Ahrefs tracks, ChatGPT is now sending 8–9x more referral traffic than the next AI-first platform, Perplexity. Sam Altman says more than 800 million people use ChatGPT each week.

So where do all those answers come from?

In that study, when ChatGPT answered top-of-funnel queries like “best CRM for enterprise” or “best home treadmill,” “best X” blog lists were the single most common page type cited — 43.8% of all sources (Ahrefs).

Nearly half the truth-layer for these questions is… listicles.

They’re not static, either. Out of 1,100 “best” lists with clear dates, 79.1% had been updated in 2025, and about a quarter had been updated in just the last two months.These aren’t blog posts; they’re living documents, constantly tweaked to stay top-of-mind for both search engines and AI.

And yet, a big chunk of this apparent “truth” is on pretty shaky ground. When Ahrefs looked at domain authority for 3,000 cited “best” lists, 35% were on low-authority domains—often sites that look like they exist more for citations and link-building than actual humans (Ahrefs).

So when you ask:

“What’s the best coffee shop in New York?”

You’re really asking:

“Which combination of fresh, structured, and frequently-referenced listicles do today’s AI systems currently believe the most?”

Not exactly the same thing.

Frequency = “truth” (for LLMs and for us)

I’ve written before about this: for LLMs (and honestly, for humans), the more something appears, the more “true” it feels.

Whatever you feed in LLM, it will spit out.

LLMs don’t have beliefs. They don’t “know” the best coffee shop.

They:

Ingest billions of words
Notice patterns (“these names show up together a lot in ‘best X in NYC’ contexts”)
Predict the next likely token

If the web has:

Thousands of “best X in New York” lists
Many repeating the exact same 8–15 places
And those lists are well-structured and updated constantly

…then those places become gravity wells in the model’s internal universe.

Ahrefs found that brands ranking higher within these lists show up more often in ChatGPT’s recommendations.

Some of that is just math: more lists, more mentions, more weight. But the effect is the same:

The model doesn’t find “the best.” It averages what’s most repeatedly claimed to be the best.

That’s exactly how “WORLD’S BEST CUP OF COFFEE” ends up in the training data.

LLMs are not very intelligent… and that’s the point

We keep talking about LLMs like co-workers:

“It prefers the top of the page.”
“It knows which tools are better for small teams.”
“It’s biased toward certain brands.”

But what is an LLM, really?

A giant probability engine
A lossy compression of everything we’ve published
A pattern-matcher that’s terrifyingly good at sounding intelligent

When you ask:

“What’s the best coffee shop in New York?”

It’s not walking the streets of Brooklyn with a tasting journal. It’s:

Pulling from training data full of “best NYC coffee shop” posts, reviews, and lists
Mixing that with whatever it can retrieve right now
Spitting out a fluent summary that sounds confident enough to believe

This is why truth media vs. fake media matters so much.

Truth media vs. fake everything

We’re surrounded by generated everything:

UGC farming engagement
Thin “best tools” roundups covered in affiliate links
AI-spun forum posts that mimic real discussion
News and reviews written for bots first, humans second

From the model’s perspective, all of this is just “text with patterns.”

So, I like the idea of truth media:

Content that:

Makes its incentives obvious
- “This is our list. We’re on it. Here’s why and who it’s for.”
Shows its work
- Methodology, criteria, trade-offs
Comes in a structure that’s easy for both humans and models to parse

Interestingly, Ahrefs found that when ChatGPT recommends a brand, it often cites internal landing pages more than homepages or generic blog content, especially for software and agencies.Those pages tend to be tightly focused, clear about what’s on offer, and aligned with a specific intent.

So the AI ecosystem isn’t just powered by listicles. It’s powered by:

Comparison content (who’s on the list, who ranks where)
Category explanations and landing pages (what this thing is, who it’s for)

That’s exactly where industry leadership comes in.

There is no “best coffee shop in New York”

First, let’s admit something obvious: there is no objective best coffee shop in NYC.

Even if you had:

Blind cuppings
Extraction profiles
Barista Olympics

You’d still run into wildly different definitions of “best”:

The freelancer wants plugs, seats, and quiet
The commuter wants “close to my train and fast”
The coffee nerd wants single-origin pour-overs and zero laptops
The parent wants stroller space and snacks

All of that gets collapsed into one unqualified, head-term query:

“Best coffee shop in New York”

That’s how most AI search works today: it tries to answer a universal question that should be deeply personal.

What we actually mean is:

“What’s the best coffee shop in New York for me?”

Which leads to the real story:

Personalization is the real “best X”

Right now, most AI answers are still universalized:

One list
Some pros/cons
A generic “it depends on your preferences” paragraph

But the obvious next step is personalization:

“Best coffee shops in New York if you need Wi-Fi, lots of seating, and you’re near Brooklyn”
“Best coffee shops in New York if you care more about beans than vibes”
“Best coffee shops in New York if you’ve got 20 minutes before a train at Penn Station”

LLMs are perfectly capable of doing this if they have:

Enough structured, specific content
A clear sense of who the user is and what they care about
Categories that map cleanly onto real-world trade-offs

This is where your content strategy has to move beyond “we’re on the list” into “we define the category.”

Specificity is the new king

If generic “best X” lists are the new link farms, then specificity is the new moat.

The Ahrefs data suggests a couple of things at once:

Being in “best X” lists (especially near the top) correlates with more AI recommendations.
AI assistants clearly favor fresh, clearly formatted content.
A lot of current sources are low-quality — which means there’s room to raise the bar.

So instead of playing the same volume game as everyone else, the smarter move is:

Hyper-specific lists
- “Best CRMs for solo consultants under $100/month”
- “Best ecommerce agencies for $2–10M brands that care about content-first growth”
Opinionated rankings
- Not 10 equal options, but 2–4 that you strongly argue for, with honest drawbacks
Structured explanations
- Clear sections, comparison tables, scenario-based recommendations — the stuff both humans and LLMs can easily digest

This doesn’t just make you useful. It sets you up for the next level of industry leadership.

Industry leadership: own your category and justify it

Here’s the shift I’d make in how we talk about being an “industry leader” in an AI-search world:

It’s not just showing up on “best X” lists.
It’s defining a category, dominating it, and making a compelling case for why that category is one of the best paths in the entire market.

Two steps:

1. Own your category

You don’t want to be just another name in “best project management tools.”

You want to be:

“The best project management tool for agencies that bill by the hour”
“The best email platform for privacy-first B2B teams”
“The best coffee shop for remote workers in Williamsburg who hate loud music”

That means:

Creating pages that clarify who you’re for and who you’re not for
Using structure that maps neatly to user needs (industry, team size, budget, workflow)
Being consistent across your site so LLMs see a clear, strong signal:
“This brand = this category = these problems.”

When AI assistants need to slot you somewhere in their answer, you’ve already done the work.

2. Argue why your category is one of the best options overall

Owning a niche is great, but in AI answers you’re often compared alongside totally different approaches:

“Do I hire an agency or buy a tool?”
“Do I use an all-in-one suite or a specialized stack?”
“Do I pick the cheap option or the flexible one?”

Industry leadership means you’re not just saying:

“We’re the best X.”

You’re saying:

“Here’s why this whole category (that we happen to lead) is one of the best ways to solve your problem compared to the other categories you’re considering.”

That looks like:

Explaining the trade-offs between categories, not just vendors
- Agencies vs in-house vs platforms
- All-in-one vs best-of-breed
- Physical vs digital solutions
Producing content that helps users decide:
- “When this category is right for you”
- “When you should pick something else” (yes, even if that means not choosing you)
Giving LLMs clean, quotable arguments:
- “This approach works best when…”
- “The main downside of this category is…”

You become the source of record AI systems can lean on when they need to explain why your approach is good… not just that your brand exists.

That’s a much stronger position than being one more name in a 27-item listicle.

So what do you actually do?

If you care about being visible in AI search when someone types “best X,” here’s the distilled playbook:

Have at least a few “best X” assets — but make them real.
- Clear audience, clear use case, clear trade-offs.
- If you include yourself and rank yourself highly, explain why like a grown-up, not a window sign.
Specialize your lists and pages.
- Move from “best tools for marketers” to specific segments, budgets, and situations.
- Align with how real users and buyers actually think about their choices.
Treat your category pages and landing pages as truth media.
- State who you’re for.
- Explain the category, not just the product.
- Compare categories honestly so LLMs have something nuanced to pull from.
Update relentlessly.
- Build a maintenance habit for your key comparison and category pages.
- Freshness is clearly rewarded in both AI and traditional search.
Aim for leadership, not just mentions.
- You want to be the brand that defines how your category is understood,
  and the one with the best argument for why that category deserves a seat at the “best overall solution” table.

Because in the end, there is no single “best coffee shop in New York.”

What we actually want and what AI search is slowly evolving toward is:

“Given who I am, where I am, and what I care about…
What’s the best option for me, and why this kind of option at all?”

Until then, we’re living in the Buddy-the-Elf era of AI search: lots of glowing signs, not a lot of context.

Our job as SEOs, marketers, founders is to build the kind of truth media that helps models (and people) tell the difference.

-Aaron

Why gravatar may be one step ahead of everyone

Aaron Heienickle — Tue, 25 Nov 2025 00:37:22 GMT

AI content is on the rise. Bots are on the rise. And somewhere in the noise, a fundamental question is emerging that will shape the next era of the internet: who is even real anymore?

We are entering a period where synthetic text, images, and even video are becoming indistinguishable from human-created content. Search engines index everything, and increasingly, that “everything” includes pages written by machines for machines, engagement farms operated by automated accounts, and content designed to manipulate rather than inform. The signal-to-noise ratio is collapsing, and the very premise of search - finding useful, trustworthy information - is under threat.

In this context, I have been thinking that Gravatar may be one step ahead of everyone.

The Gravatar precedent

For those unfamiliar, Gravatar is essentially a human identity tracker. It allowed WordPress users to have an identity that followed their email address. Your profile image, your bio, your digital presence would follow you from site to site across the WordPress ecosystem. Comment on a blog, and your avatar appears. Write a guest post, and your identity travels with you.

Gravatar was an awesome first attempt at conceptualizing virtual identity—a persistent, portable representation of a person that transcended any single platform. It recognized something important: that the internet needed a way to attach reputation and continuity to individuals, not just accounts.

The problem, of course, is that anyone can create an email address and create a Gravatar. The identity layer exists, but it is not verified. It is a shell without proof of the human inside.

The big question of verified identity

This raises an uncomfortable question: is there a next level of internet identity we could build? Something that connects your online profiles to a verified government ID?

The technology exists. We verify identity for banking, for travel, for countless other purposes. We could, in theory, create a system where your digital presence is cryptographically linked to proof that you are a real, unique human being.

But here we run into a pretty big tension in my opinion.

The problem with verified government ID is that it rewrites what the internet was originally about.

The early internet was built on principles of anonymity, freedom of information, and the ability to say what you needed to say without fear of government retaliation. Dissidents could organize. Whistleblowers could speak. People in oppressive regimes could access information their governments wanted to suppress.

That said, there has always been censorship on the darker parts of the internet. Child exploitation is removed. Terrorist recruitment is targeted. The internet was never truly a lawless frontier… it simply had different rules for different spaces.

A potential new layer to the internet?

Consider how the internet already exists in layers. There is everything not indexed by search engines. The VAST majority of web pages, behind logins or simply never crawled. There is the dark web, accessible only through specialized tools, home to both legitimate privacy seekers and criminal enterprises. There is the censored web, where platforms and governments remove content deemed harmful or illegal.

And now, potentially, a new layer is evolving.

Imagine a search engine that only indexes pages with a unique human identifier attached to them.

Not a replacement for the existing internet, but a parallel layer—a space where every piece of content can be traced back to a verified person. A place where you know, with reasonable certainty, that you are reading the words of an actual human being.

This would not require everyone to abandon anonymity. The traditional internet would remain. But for those who want to participate in a higher-trust environment—and for those searching for information they can verify came from real people—this new layer would exist as an option.

The Foundations

Building such a system would require several components working together.

First, a verification mechanism that can confirm humanity without necessarily revealing full identity. Perhaps, it could be something closer to “proof of personhood” than full government ID linking.

Second, a search infrastructure that indexes only content bearing these verified identifiers. Third, and critically, robust spam detection systems to identify and exclude fraudulent actors and manipulators who would inevitably try to abuse the system.

No system is perfect. Verification can be faked, identities can be stolen, and determined bad actors will always find exploits. But the goal is not perfection… it is raising the cost of deception high enough that the signal-to-noise ratio becomes manageable again.

With these pieces in place, you would have the foundations of a new level of the internet. Not a replacement for what exists, but an addition to it. A space where the answer to “who is even real?” is finally knowable.

Gravatar saw this future twenty years ago, even if the technology was not ready to fully realize it. The insight - that portable, persistent identity matters - was correct. The question now is whether we can build the next iteration: identity that is not just portable, but provable.

The bots are rising. The AI content is flooding in. And somewhere in that flood, we are going to need a way to find each other again.

-Aaron

Aaron AI Visibility Extension - Privacy Policy

Aaron Heienickle — Sun, 23 Nov 2025 23:37:04 GMT

Aaron’s AI Visibility Extension analyzes the content and HTML structure of the web pages you choose to inspect and shows you AI visibility and SEO insights directly in your browser. This privacy policy explains what data is processed and how it is used.

Data processing in the extension
When you activate the extension, it reads the DOM of the current page to calculate metrics such as an AI Visibility Score, headings and structure, schema/structured data, meta tags, alt text, and readability.

This analysis is performed locally in your browser. Page content and analysis results are not sent to any external server controlled by the developer or third parties.

Local storage
The extension may use the browser’s storage APIs (such as chrome.storage) to save local settings and preferences, for example UI options or feature toggles. These settings do not include browsing history or sensitive personal information, and they remain on your device.

Email addresses and newsletter
You may optionally choose to provide an email address to subscribe to the Aaron’s AI Visibility newsletter. This may happen through:
– A “Subscribe” or “Join newsletter” link or form associated with the extension or its website.

If you choose to subscribe, your email address is used only to send:
– Newsletters and educational content about AI visibility and SEO
– Product updates and announcements related to Aaron’s AI Visibility Extension

Providing your email address is not required to use the extension’s core features. You can unsubscribe from the newsletter at any time using the unsubscribe link included in each email.

Data sharing
Email addresses are not sold, rented, or shared with third parties for advertising or profiling. If an email delivery service (such as an email newsletter provider) is used, it is only for the purpose of sending emails you have explicitly subscribed to.

The extension does not use or transfer user data for purposes unrelated to its single purpose, and it is not used to determine creditworthiness or for lending decisions.

Changes to this policy
If this privacy policy changes, the updated version will be published at this URL and the “last updated” date may be revised.

Contact
If you have questions about this extension or this privacy policy, please contact:
Aaron Heienickle
aheienickle@gmail.com

Get Pluto - My free AI visibility chrome extension (Version 2.0)

Aaron Heienickle — Sun, 23 Nov 2025 23:13:20 GMT

Hi, I’m Aaron!

Thanks for checking out my AI Visibility Extension.

Yes, this extension is 100% free.

Download it here and add it to chrome.

I built this because I kept running into the same problem over and over: we talk a lot about SEO and rankings, but almost nobody talks clearly about how our content looks to AI systems. I wanted a simple way, right inside the browser, to see: “If an AI model or search engine reads this page, what does it actually see?”

This extension is my answer to that question.

FIRST, MY FAVORITE PART:

You can export everything — all of the data, all of the checks, all of the analysis — into a professional HTML report you can hand to a client or walk your team through in a meeting. It’s clean, it’s branded, and it took one click. That feels good.

AI SEARCH VISIBILITY TESTS ( NEW )

This is what I’m most excited about. The extension runs 7 live visibility tests across all four major AI platforms simultaneously — Claude, ChatGPT, Grok, and Perplexity. Three tests are auto-generated from the page you’re on, two let you type in your own brand or topic queries, and two are generated from the domain itself. You get a direct look at whether your content is actually showing up in AI-generated answers — not just in theory, but right now, on those platforms.

AI VISIBILITY SCORE

I created the AI Visibility Score (0–100) because I wanted a single, honest number you can point to and say, “This page is getting there,” or “We’ve got work to do.” That score is a pulse check on how machine-readable, well-structured, and AI-friendly your content is. As AI-generated answers become more common, the pages that are easier for models to understand will be the ones that get surfaced, summarized, and recommended.

30 AI VISIBILITY CHECKS

The Optimize tab runs 30 individual checks covering schema, structure, content signals, metadata, authority markers, and technical signals. Failing checks show up first so you know exactly where to focus. Each check tells you what it found and why it matters for AI discoverability.

SCHEMA AND STRUCTURED DATA

I added schema and JSON-LD analysis because I kept seeing great content that wasn’t sending any clear signals to machines. Structured data is how you whisper directly into the ears of AI systems. You can instantly see every schema type found on the page, the full JSON, and where there are gaps — so your content can participate fully in rich results and AI-driven experiences.

CONTENT READABILITY AND PASSIVE VOICE

I care a lot about clarity, so readability grading and passive voice detection were non-negotiable for me. The ARI score, passive voice bar, sentence-level metrics, and writing quality checks are all here because AI systems love content that’s straightforward, confident, and easy to summarize. I want you to see at a glance whether your writing is doing the work.

HEADING STRUCTURE AND CONTENT HIERARCHY

Messy headings quietly break both user experience and AI understanding. The heading structure view shows your full H1–H6 outline with a content tree map and word count distribution, so you can immediately see whether your page has a logical hierarchy or has buried its most important ideas.

LINKS AND ALT TEXT ANALYSIS

I built the links section and images section to match — clean horizontal bars, badge system (Internal, External, Nofollow for links; Has Alt, Missing for images), and full lists so you see every single URL and image on the page. Alt text is one of those details that’s easy to skip but incredibly powerful. Images carry meaning that AI models can’t use unless you describe them. I want to help you close that gap.

META, OPEN GRAPH, AND TECHNICAL SIGNALS

Titles, descriptions, canonical tags, robots meta, language declarations, HTTPS status, Open Graph, Twitter Cards — all of it is visible in one place. The Technical tab also includes one-click links to PageSpeed Insights, Rich Results Test, and the Schema Validator, so you can go from insight to deeper diagnostics in a couple of clicks. Quick links to your robots.txt, sitemap, and llms.txt are right there too.

REPORTING AND EXPORTS

I added HTML, JSON, CSV, and Claude Brief exports because I know what it’s like to be in a client meeting where everyone asks, “Can you send this over?” The HTML report is fully rebuilt — Mercury-style design, horizontal data bars, complete links list, complete images list with alt text, full schema JSON, and all 30 AI checks organized by pass and fail. One click and you have a professional deliverable, not just a screenshot.

PRIVACY AND LOCAL ANALYSIS

The extension runs entirely in your browser. No server calls, no external dependencies, no data leaving your machine. You can run it on staging environments, client sites, or internal pages without wondering where your data is going. The analysis is instant — it’s part of your everyday workflow, not a separate audit event.

The extension may collect an email address only when a user voluntarily subscribes to Aaron’s Newsletter. Email addresses are used solely to send newsletters and educational content about the future of search. Providing an email is optional and has nothing to do with using the extension. This is handled on aaronsnewsletter.com via Substack — not through the extension itself. Email addresses are never sold, rented, or shared with third parties

HELPING YOU STAY ONE STEP AHEAD

We’re moving into a world where content isn’t just written for search engines or human readers — it’s written for AI assistants, chatbots, and generative systems that decide what to surface and how to summarize it.

I built Aaron’s AI Visibility Extension to help you bridge that gap. I want to make sure your best ideas are actually visible in that future — not lost in messy structure, missing schema, invisible images, or content that AI can’t parse.

If you care about quality content, strong structure, and being ready for where AI and search are going next — I built this for you.

There is no reason not to try it. It’s free.

What's next after the robotic revolution?

Aaron Heienickle — Sat, 22 Nov 2025 06:22:31 GMT

Hi there! This is my personal newsletter (Aaron’s Newsletter). I share thoughtful, authentic essays on the ideas shaping business, AI, and tech—so you can stay ahead, build smarter, and focus on what matters.

It’s 100% free to join, and you can get some fun rewards for getting your friends to join too.

I’m going to start with an assumption:

The robotic revolution happens.

Not humanoid Tesla butlers folding towels in your living room. I mean something deeper and less cinematic: robots that touch mines, farms, factories, roads, ports, warehouses. Robots that dig, move, cut, weld, ship, and assemble almost everything we use.

Today, the interesting question (to me) isn’t: “Will this happen?”

It’s: “Okay, what comes after that?”

If robots can create a surplus of almost everything,

What actually becomes scarce?
Who does the work that’s left?
Who gets the house next to the pretty waterfall?
Who spends their life maintaining robot fleets while other people mostly play?

And, underneath all of that: do we just rebuild the same old civilization? A civilization run by whoever has the “bigger stick,” or do we finally burn that pattern and try something else?

This essay is my long-winded attempt to think that through.

First, what is the ‘robotic revolution’

When I say “robotic revolution,” I’m not imagining some sudden sci-fi moment. I’m imagining a very boring, very powerful sequence.

It starts deep in the guts of the physical world.

The fully automated supply to manufacturing to your home chain

First, automated resource gathering.

Robots in mines pulling metals from the ground.
Robots in forests harvesting timber.
Robots farming and fishing in the ocean.
Robots crawling across solar farms cleaning and repairing panels in the desert.

Then, logistics.

Self-driving trucks on the highways.
Automated ports that never sleep.
Drone fleets turning the sky into a delivery mesh.
Warehouses where the lights stay off because the robots don’t need them.

Then, manufacturing, construction, and fabrication.

Robotic arms on factory lines.
3D printers that extrude entire houses instead of trinkets.
Machines that cut, weld, assemble, and repair almost anything on command.

Finally, distribution.

Which is basically Amazon on god mode.

You press a button. The machines reshuffle matter. A thing appears at your door.

In my head, it collapses into a single chain:

Automation → higher productivity → more output → lower cost → surplus.

Run that long enough and you end up in a weird place. A world where we can produce a surplus of almost everything people want.

Not infinite. Physics still exists. But enough that “we don’t have enough X” stops being the limiting story in a lot of categories.

That’s the glamorous version.

Underneath it is a much less glamorous world I think about a lot more.

The ‘maintenance layer’

Under the shiny robotics machine, there’s a big, absolutely essential layer: maintenance.

A robotic civilization doesn’t run on its own. It runs on the maintenance layer.

Mining robots drift out of spec and need tune-ups.
Delivery drones chew through batteries.
Factory robots lose calibration.
Software accumulates bugs and needs updates.
Sensors get dirty.
Networks fall over if nobody’s watching.

The more we automate, the bigger this hidden layer becomes. You get a world of robots sitting on top of a much smaller world of humans who keep them alive.

My theory is this becomes one of the largest “industries” on the planet:

People who understand how to manage and maintain robotic fleets at scale.
People who live in dashboards and logs.
People who know when a weird pattern in sensor data is nothing, and when it’s about to shut down a port or a farm.

They’re not “blue collar” or “white collar” in the old sense.

They’re robot-collar.

And, that leads to a question I can’t shake: “What if most people don’t want to maintain and manage robot fleets?”

Because while this is happening, two other things are true:

Robots are slowly eating a lot of existing jobs.
Robots themselves are becoming the key assets in the system.

If robots are the new capital, then whoever owns and maintains the robots owns a huge chunk of the economy.

So, the robotic revolution isn’t just about new machines.

It’s about a new human stack sitting on top of those machines.

The robotic-era workforce stack

When I follow this through, I end up picturing society sorting into rough layers.

At the top, there are robot owners: the people and institutions that own the fleets and infrastructure. Let’s say the mines, fabs, warehouses, drone swarms, data centers, the software that coordinates all of it.

Under them are the robot stewards: the robot-collar class. Technicians, operators, systems engineers, fleet managers. People who keep the surplus machine running and decide what happens when things break.

Then there are entertainers and experience creators: streamers, creators, game designers, world builders, artists. As basic needs get cheaper, attention and experience get more valuable, and a lot of human energy flows there.

Alongside them, researchers and discoverers: people pushing the frontier of science and technology. Creating the new robots, new materials, new medicines, new models.

There are also governors and rule-makers: governments, regulators, boards, DAOs, whoever ends up setting the rules for critical infrastructure.

And finally, there’s a large, fuzzy category I think of as the unassigned: people who don’t have clear roles in this stack, or don’t want any of the ones on offer. They enjoy recreation art and are free to play, until they find how they want to contribute meaningfully.

This isn’t a rigid caste system. People move. Lines blur. But it exposes the part that feels most dangerous to me:

The surplus machine can scale almost without limit.

The number of high-agency roles cannot.

You get a structural mismatch:

There are way more people than there are meaningful, high-agency roles.

Whole industries get eaten by automation.

People who used to do physical or routine cognitive work wake up to find that the system can keep running just fine without them.

Ownership of robots and infrastructure concentrates unless something actively pushes against it.

A small group captures most of the upside.

The maintenance layer is vital, but narrow.

Not everyone can - or wants to - be a robotics tech or a fleet ops lead.

Which is totally fine.

But, we drift toward a pressure-building combination:

a relatively small owner class,
a thin but crucial steward class,
a hit-driven creator and entertainer world,
a small cluster of researchers and governors,
and a very large unassigned population the system doesn’t really need to function.

That creates pressure in three directions at once:

Moral pressure: If the surplus machine can provide enough for everyone, what does it say about us if we let large groups live in misery?
Political pressure: ff a tiny group owns the robots and everyone else has no stake, that isn’t just inequality. It’s instability.
Psychological pressure: What happens when millions of people quietly realize: “The system doesn’t actually need me to survive. I’m optional.”

At that point, minimum living standards stop being a utopian thought experiment. They start to look like basic risk management.

Minimum living standards, and utopian thoughts

Once I accept two premises:

Robots can produce far more than our basic needs.
There are fewer “needed” roles than people who want to matter.

…minimum living standards change shape.

It’s no longer: “Wouldn’t it be nice if everyone had enough?”

It becomes: “We might have to guarantee that everyone has enough, or this whole thing tears itself apart.”

In a robotic world, “enough” starts to look like:

Safe, clean, comfortable housing
Enough food and water
Basic healthcare
Education and connectivity
Reliable energy and infrastructure

The surplus machine plus the maintenance layer make that logistically and economically possible in a way previous civilizations could only fantasize about. You don’t need everyone in fields or factories to justify giving them a roof and a meal. The robots already did that work.

So, minimum living standards become three things at once:

A moral response to abundance (“If we can, why wouldn’t we?”)
A political pressure valve (“If we don’t, this blows up.”)
A psychological baseline (“You exist, therefore you will not starve.”)

And once that floor is in place, a huge door swings open.

If fewer people are needed for survival work, more people are available for everything else. Less time grinding for rent means more time available for play, art, and exploration.

You can imagine a civilization where:

Entertainment and recreation grow into the largest “industry” by time spent
People spend most of their days in games, virtual worlds, creative projects, and communities
Housing starts to drift from mere shelter toward art and self-expression, because robots can build almost anything

On paper, it starts to look like a second renaissance.

If you can print any structure you can describe, your home becomes a 3D self-portrait of your family: your taste, your values, your weirdness cast into walls and geometry. Neighborhoods start to feel less like grid plans and more like collaborative art projects.

Robots can make housing cheap. They can make “good enough” housing abundant.

While that is true, robots cannot print more coastline, more mountain ridges, more perfect climates, or more slots in the handful of cities where everyone wants to be.

So, I expect humans will still be able to find stuff to fight about.

The house by the waterfall & other dumb things humans would fight over.

Minimum living standards might solve survival. They don’t solve the things people really fight over.

Even in a robotic surplus world, some things stay structurally scarce:

Location. The house by the waterfall. The best neighborhoods. The cities with the strongest networks.
“Caviar-level” goods. Stuff that is rare by nature or rare by design—unique environments, limited experiences, high-touch services, truly scarce ingredients.
Attention and status. You can’t give everyone the same recognition, influence, or say. Not everyone can sit in the key decision-making rooms.

Money will have a place, even in a world of surplus

How do we solve questions like:

Who gets the bigger house?
Who gets the house by the waterfall?
Who gets the rare experiences, the caviar, the best roles?
Who gets to feel like their work actually matters?

If the waterfall house exists, how can it be “right” that one person or one family controls it? Especially, in a society where we already can supply the minimum living standards to everyone.

And once you see that, a very natural thought appears:

If a world like this is possible, and it feels unfair that some people get the best houses and best land:

Why not just go all the way?

If it’s unfair that one person gets the house by the waterfall, the instinctive move is: then nobody should “own” the waterfall at all.

Since, there will always be a finite number of truly prime locations & scare resouces.

Why not:

get rid of private property,
get rid of money,
share everything,
and let the surplus machine provide for everyone equally?

Before getting rid of all private right, lets go back in history…

In early human societies, you could say things were more “communal” in some sense—small bands, tight tribes, shared labor.

But the incentives were very different from what people imagine in a robotic surplus world.

There was always a minimum amount of labor required to survive. Food didn’t appear because someone pushed a button. If enough people slacked off, the group went hungry, got cold, or became vulnerable to attack.

Contribution wasn’t a side quest; it was the price of admission.

If you didn’t contribute, you weren’t just seen as “lazy.”

You were a risk.

Someone who ate from the pot without helping fill it. Someone who consumed protection without helping provide it.

And the enforcement wasn’t abstract. It was social and visceral:

You could be shamed, mocked, or pushed to the edge of the group.
You could be the last to eat, the least protected, the one nobody rushed to help.
In the extreme, you could be driven out completely.

Surviving alone was hard enough that very few people wanted to roll that dice.

Being cast out wasn’t just a bad vibe; it was often a death sentence.

Even groups of “outcasts” who banded together still had to do hard, physical, coordinated work to meet basic needs. There was no robot safety net waiting to catch them.

So older communal-ish societies had a brutal but simple logic:

You needed the group to survive.
The group needed you to pull your weight.
Social pressure and the threat of exclusion kept most people in line.

Now, compare that to a robotic surplus world with minimum living standards.

If the surplus machine and maintenance layer guarantee that you:

won’t starve,
won’t be homeless,
can get basic healthcare,
and can access some baseline of information and entertainment,

Te cost of opting out is very different.

You can, in principle:

live alone or in a small pod without your neighbors needing you,
refuse to contribute anything essential and still have your basic needs met,
retreat into virtual worlds or small circles and never really be socially punished for it

Opting out is possible with minimum living standards

The old communal enforcement mechanism—“help us or you might not eat”—stops working. The group needs you less, and you need the group less.

That’s liberating in one sense. It means people aren’t forced into survival labor just to be tolerated.

But it’s also dangerous, because once that social glue weakens, a lot of “communal utopia” assumptions break. You can’t rely on the same peer pressure and shared risk that made older tribes function.

If you try to copy their “we all share everything” aesthetic in a world where individuals can survive alone on the floor, you get a very different beast.

And that’s where the modern version of the dream shows up: “We’ll just scale that communal feeling up to everyone. No private ownership, no money, shared everything, robots & our shared work will handle the rest.”

However, it gets weird.

Even if robots do most production, the system still depends on a maintenance layer of humans.

So, if you build a world where opting out is socially acceptable and materially safe, you create a mismatch.

Some people keep civilization running, while others can live comfortably without contributing anything essential.

That’s not a moral failing. It’s just incentives. But it does create an emotional and political fault line.

The robot-stewards start to feel like the only adults in the room. The opt-outs start to feel judged for taking a deal the system itself offered.

And once that resentment shows up, “shared everything” stops feeling like a utopia and starts feeling like: “Wait… why am I maintaining the machine while you mostly play?”

At that scale, with that level of complexity, you end up in something we’ve already experimented with…

And, historically, it doesn’t end up well for those who gave up their power to the stewards.

The risk of robotic communism and what I call the FINAL stick

If you take the “no money, no property, all shared” fantasy seriously and scale it past a small tribe, you end up rhyming with a very old idea:

Communism.

Not the abstract ideal where everyone is enlightened and altruistic, but the real, historical pattern:

common ownership of productive assets,
centralized planning of who gets what,
an official promise of equality.

On paper, communism sounds a lot like the robotic surplus dream:

The people own the means of production.
Everyone gets the basics.
We produce according to ability and distribute according to need.

However, historically, a few stubborn patterns kept showing up in communism:

Power concentrated around whoever controlled planning and enforcement.
Incentives blurred, because the difference between doing your best and phoning it in got softer at the margin.
Information got distorted; people told the plan what it wanted to hear, not what was true on the ground.
Black markets sprang up to route around shortages, rigidity, and bad decisions.

Instead of a flat utopia, you got:

an elite with real control over resources and force, and
a mass of “equal” citizens who lived with scarcity and very little say.

Now drop robots into that picture. Call it “robotic communism”:

All the robots, fleets, infrastructure, and factories are “owned by the people,” which in practice means by a central state or coordinating body. There’s no meaningful private ownership of robot fleets or logistics.

There’s an official promise: the surplus machine belongs to everyone; everyone gets their share.

At first, it might look incredible. Robots harvest, build, cook, deliver. Everyone gets standardized housing, food, healthcare, connectivity. You tap a screen and things show up.

But, all the old failure modes are still there. It’s just running on top of automated infrastructure instead of human labor.

Someone still decides:

which regions get the newest robots and best infrastructure,
who gets upgraded housing and who stays in standard blocks,
which maintenance issues are treated as urgent and which are quietly ignored,
who gets access to scarce locations, special experiences, and travel.

If they get those decisions wrong, the system doesn’t just get a bit unfair. It gets brittle. Logistics fail. Regions lose power or food. Entire supply chains crumble because some central planner or local official made bad calls.

And on top of that, robotics supercharges the “bigger stick” problem.

In older eras, the bigger stick belonged to whoever controlled land, food; and, most importantly, the military.

For discussion on the the “bigger stick” today, watch this debate with John Mearsheimer and Jeffrey Sachs:

In a robotic communism scenario, one relatively small organization can control:

the robot fleets that produce food, housing, and energy,
the networks everyone depends on,
and the robotic enforcement stack—drones, surveillance, automated policing, maybe even autonomous weapons.

That’s a level of consolidated leverage we’ve never had before.

You don’t need a bloated state to be terrifying. You just need a tight, technical group with the keys to the surplus machine and the enforcement machine at the same time.

On the surface, it’s “efficiency” and “equality.”

Underneath, it’s soft totalitarianism with perfect enforcement, or creeping chaos when the machine starts to fail.

Either way, it’s not the second renaissance we were hoping for.

That’s why I DON’T think the path forward is: “Abolish property, abolish money, centralize the robots, and trust the planner.”

We probably do want minimum living standards in a robotic world.

We probably don’t want to layer full-blown communism on top of that.

So, the question shifts… how do we build a system on top of minimum living standards that:

stays fair to the people actually doing the work,
lets people climb above the floor,
doesn’t drift into robotic communism,
and doesn’t congeal into robot feudalism?

That’s where land, money, and entrepreneurship come back into the story.

So I don’t think the answer is to erase ownership and pretend nobody owns anything. I think the answer looks more like:

Keep property and markets, but run them on top of a robotic surplus that guarantees a floor.

Nobody should be homeless or starving in a world that can trivially prevent it. Nobody should be locked out of basic healthcare, education, or connectivity. The surplus machine can and should provide that.

But above that floor, there is still a game. Some people will end up with better houses, better locations, better experiences.

The question is: what game are they playing to get there?

Capitalism with a floor. Why I believe currency must survive.

It’s tempting to imagine that if robots make everything cheap enough, money disappears.

I don’t think that’s how it plays out.

As long as desire exceeds supply for some slice of reality, you need a way to compare tradeoffs, allocate access, and keep score. That’s what money—or whatever replaces it—is.

It might not look like dollars in a bank account. It might look like tokens, reputation-weighted access, local currencies, or something we don’t have a name for yet. But some kind of currency is going to survive.

Above a certain floor, the economy stops being about survival and starts being about status, agency, and contribution.

You don’t work just to eat. You work to move to a nicer location, to unlock better experiences, to have more leverage over what gets built, to have more say in how things run, to feel needed.

That’s what I mean by ‘capitalism with a floor:

The floor is guaranteed by robots + stewards.
On top of that, there is still a game where you can earn more than the minimum.

The key design choice is this: If you want more than the minimum, your best path up should be to make the world better for other people.

Not to grab the bigger stick. Not to exploit obscure leaks in the rules. But to actually build things, services, communities, and experiences that improve life.

That’s where entrepreneurship comes in.

Entrepreneurship and the productized self

In a capitalism-with-a-floor world, entrepreneurship becomes one of the main ways an individual can:

earn more than the minimum
access better locations and experiences
gain more agency and say

The clean version of this is simple: you move above the floor by creating value for others.

Entrepreneurship here doesn’t just mean “start a SaaS company and raise a seed round.” It can look like:

starting a cooperative that manages and maintains robotic fleets more safely and more fairly
designing new forms of housing and shared spaces that people genuinely want to live in
building tools and apprenticeship paths that help displaced workers transition into stewardship or creative work
creating games, worlds, and cultural products that actually enrich people’s lives instead of just farming their attention
launching new kinds of communities, education systems, or local governance models that help people live better together

Money (or whatever replaces it) becomes a way of routing resources toward people and projects that are actually useful, not just politically connected.

At the same time, the productization of the self accelerates.

Like it or not, each person starts to look more like a portfolio:

of projects they’ve shipped
of systems they’ve improved
of people they’ve helped

Your real “asset” isn’t just your account balance. It’s your track record.

What did you build?
Who did you help?
What got safer, richer, or more interesting because you were involved?
Who trusts you enough to follow you, fund you, or work with you?

Right now, we approximate that with views, likes, and follower counts.

In a healthier version of a robotic future, it tilts more toward: show me what you actually did.

That still doesn’t guarantee a good future.

Because even with:

a high floor
entrepreneurship-as-contribution
reputation as a key asset

There’s still a gravitational pull toward consolidation.

Rich people buy more land. Robot owners buy more robots. Stewardship slowly gets captured by a small technical priesthood. The bigger stick starts to reform itself out of land, fleets, and enforcement systems.

If we’re not careful, “capitalism with a floor” turns into robot feudalism with better UX.

So the ultimate question becomes: How do we keep the bigger stick from just reassembling itself?

How to burn the ‘bigger stick’

For most of history, the bigger stick has been some mix of:

land
resources
and the capacity for violence or exclusion

Feudal lords controlled land. Peasants worked that land to survive. If they didn’t comply, they could be pushed off, starved, punished.

Later, industrial states fought over territory, coal, oil, and factories. Control the resource base, control the ports, control the army—you control the future.

The pattern tends to rhyme:

Control the essentials: land, resources, production, enforcement.
Use threats or exclusion to enforce your position.
Call the result “order.”

Robots add a new layer to that pattern. The essentials are no longer just land and human labor. They’re:

land
robot fleets
critical infrastructure
and, potentially, robotic enforcement (drones, sensors, automated security)

If we design this badly, one entity can grab all of that and hold a robotic bigger stick so large it makes past empires look quaint.

When I say I want to “burn the bigger stick,” I don’t mean “no one should have power.” We still need coordination, leadership, and sometimes force.

I mean we should stop building systems where one small group can own the land, the robots, and the enforcement stack and call that “for the common good.”

Ownership will still matter. Money will still matter. Status will still exist.

Burning the bigger stick, to me, means:

making it much harder for power to fully centralize
tying status to contribution rather than raw extraction
and giving more people real, legible paths into ownership and stewardship

That’s where design choices actually matter.

The middle ground in ‘robo-capitalism’

I don’t have a master blueprint. But if we want:

minimum living standards
capitalism above the floor
and less “one group with a giant robotic stick” risk

then a few levers look especially important.

Levers to soft land the robotic revolution

Broaden robot ownership.
If robots are the new capital, then ownership is power. We probably want more shared fleets, co-ops, and public/commons infrastructure, not total centralization. It doesn’t have to be perfectly equal. It just doesn’t have to be feudal.

Make stewardship high-status and accessible.
Robot-collar work should feel like stewardship of civilization, not backend drudgery. That means cheap, accessible education and tools; clear paths in; and real autonomy and upside. “I keep the robots alive” should sound like something you’re proud to say.

Use surplus to guarantee a floor, then reward contribution.
Let the surplus machine fund minimum living standards. Above that, tie additional rewards to meaningful contribution: building infrastructure, healing and teaching, maintaining systems, creating art and experiences, starting communities, protecting ecosystems, helping displaced workers find new roles.

Aim entrepreneurship at social infrastructure.
We can nudge entrepreneurship away from pure attention extraction and toward building the scaffolding of a good society: new living models, new education and training systems, new governance experiments, better ways to share land and surplus.

Shift culture: who we admire.
We can’t legislate culture, but we can choose what we celebrate. We can admire people with the biggest fleets and fanciest houses—or the people who use surplus to expand other people’s agency and meaning. Those choices compound.

None of this guarantees success. But it does bend the game away from “whoever has the bigger stick wins” and toward a more interesting question:

Who builds the most useful systems?
Who stewards the most important infrastructure?
Who expands the set of lives that feel worth living?

What’s next is NOT set in stone. Don’t mess it up.

So, what’s next after the robotic revolution? Not just more robots. Not just cheaper stuff.

What comes next is a set of choices about:

how we handle the mismatch between surplus and meaningful roles
how we deal with old scarcity (land) and new capital (robots)
how we treat the people who maintain the machine versus the people who mostly play on top of it
whether we keep running civilization on the bigger stick—or decide to burn that pattern and build something else

Robots can give us almost anything:

Housing as art.
Time to play.
A surplus of goods no other generation has ever seen.

What they can’t do is answer the hard part:

Who gets the house by the waterfall.
Who carries the burden of maintenance.
What we think is worth competing for.
What we owe to the people the system doesn’t strictly “need.”

That part is still our job.

-Aaron

Cynicism is dumb

Aaron Heienickle — Thu, 06 Nov 2025 04:32:53 GMT

It’s 100% free to join, and you can get some fun rewards for getting your friends to join too.

We do not talk enough about cynicism.

Not the Twitter kind. Not the meme version. The deeper version. The one that shows up after you have actually seen things. After you have looked behind the curtain, met real darkness, or finally realized the world is not run by clean intentions.

I take a broader view of cynicism.

I apply it not only to people, but also how people view the entirety of the world’s systems today.

My working definition →

Cynicism is thinking poorly of the world and its people, in opposition to optimism.

A classic thing a cynical person would say: “Why try to go into politics, its all rigged anyways”

And, here is the tricky part about cynicism

The people who think the most often become the most cynical.

People who see through the veil become cynical.
People who run into true evil become cynical.
People who study the forces of evil in the world can end up concluding that it is all bad, nothing matters, people are corrupt.

And, to them, it feels smart. It feels like insight. And, it super easy to do.

But, cynicism is dumb.

Not because it is always wrong, but because it doesn’t accomplish anything.

Being cynical doesn’t DO anything

A lot of bad things happen. That part is real.

But what does cynicism do with that information?

It does not change anything.
It is very inefficient.
It is noisy.
It multiplies. Once you start seeing rot, you start seeing it everywhere.

Being cynical is like pointing out a bridge is falling, while standing on the bridge

Cynicism is like standing on a bridge that is falling apart and yelling,

“Hey, the bridge is falling.”

Correct.

But, you are pointing the finger at the bridge falling instead of hammering the nail that fixes the bridge.

Cynicism keeps you in spectator mode.

Life rewards builders, not commentators.

Cynicism takes away your inner peace.

The real problem with cynicism is not only that it is unproductive. It is that it robs you of peace.

Once you adopt “everything is bad” as your default lens, you cannot enjoy the parts that are not bad.

You lose:

peace of mind
peace of expectations
the ability to enjoy normal, good, simple things

And yet life is actually pretty good, especially if you train your attention.

How do you stop being cynical without being delusional?

The cure to cynicism is ‘gratitude with awareness’

This is not delusional positivity.

I am not saying ignore evil. I am not saying pretend it is fine.

But, I am saying:

Be grateful and optimistic, but stay aware, without letting your mind get noisy.

You can hold both truths at once.

Yes, this is happening and it sucks. There is real evil and real brokenness.

But, I still have things to be grateful for right now. I still have agency right now.

Gratitude does not erase the darkness. It shrinks it to its proper size.

With gratitude, you get:

peace of mind
peace of expectations
the energy to build & do/

Don’t rob yourself of the opportunity to do.

We need less people pointing at what’s wrong, and need more people doing something about it.

Life is pretty good today, when you take a step back

It is easy to say life is good. But, it is harder to show it.

So, let’s compare today with a time people love to romanticize: Ancient Rome.

1. You probably will not die from a random infection.
In Ancient Rome, people could die from a tooth infection or contaminated water. Today you have antibiotics, clean water, soap, and hospitals.

2. You have comfort that emperors did not have.
You have heating, air conditioning, refrigeration, lights, a mattress that does not have fleas, and you can get fruit out of season. Roman elites did not have a fridge. You do.

3. You have rights and legal protections.
Rome had slavery, infanticide, public torture, and your status depended on birth.

Today you at least live in a world that recognizes human rights as a concept. Even when imperfect, it is a massive upgrade.

4. You can talk to the entire world from your pocket.
A Roman would wait weeks for news from another city. You can make a video call in seconds. You can learn philosophy, programming, languages, history, all for free.

5. You are safer.
You do not have to worry every day about getting press ganged into a legion, or your village getting sacked. Violent death per capita was higher in premodern societies. Today most people can walk to a store and come back alive.

6. You have more choice.
A Roman was mostly stuck with the class, gender role, and trade they were born into. You can switch careers, move cities, create online income, and choose who to marry.

So when we say, “life is pretty good,” we are not being motivational. We are being historical. You live in the most materially comfortable, medically advanced, and socially flexible period humans have ever had.

This does not cancel out modern problems. It just means cynicism is leaving out half the picture.

Don’t take the easy way out

Cynicism may feel smart, or like like a new insight. And, it is EASY.

But, it is pretty dumb when you think about it.

Not because it is always wrong, but because it is useless, noisy, and inefficient.

When cynicism shows up, here’s is what I would do:

Acknowledge the bad.
Name what you can control in the present moment.
Anchor in gratitude.
Do the next small, constructive thing.

“Yes, this is happening and it sucks. But, I need to focus on what I can control in the present moment.”

That is not naive. That is power.

It is easy to narrate the collapse… but it takes courage to go build the antidote.

Your call.

-Aaron

Some thoughts on cause and effect

Aaron Heienickle — Tue, 13 May 2025 23:13:18 GMT

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For a cause, there are 100s of potential effects. But, there is only one eventual effect.

How many times daily do we worry - trying to predict the future and map out all the unique routes life can take us.

As a chess player, I know well how computationally draining it can feel to try to think of a response to every scenario.

If a knight moves here, I do this. If the pawn moves here, then this scenario will play out.

You can try to map out layers and layers of cause-effect to infinity.

But, while some chess players succeed off of memorization of line by line, the best chess players play with a ‘feel’ (verified by small bursts of computation).

What do I mean by this? They see each position as a story, rather than a series of moves on a board.

“My bishop is really strong on this diagonal and will likely annoy my opponent into the future” “My king seems pretty safe and snug, but maybe I could move a piece over to help him out.”

They don’t try to compute every possible move - they feel for the best 3 and explore the best 3 options against that move. Some chess players call this their candidate moves.

The best chess players developed this feel for the best 3 moves from playing LOTS of chess and getting lots of experience.

But, there’s something else going on that helps them stay more focused on what matters…

Let’s say you start with 3 best moves. For one of those moves, there are 3 best responses. For each of those 3 responses, there are 3 best responses.

This forms a mini tree-like structure with only 27 outcomes!

Example 3x3x2 tree of 18 outcomes

That mini=tree is much easier to look over and understand than a massive tree like this:

So, what is my point?

First, shorten your decision tree to three candidate moves. And, realize anything past that is probably overthinking.

Second, it’s better to wait for the effect, then to try to plan ahead for every potential scenario.

You will gain new intuition and feedback after every step, and definitely some lost games and mistakes.

And, why waste your time worrying about things that have not happened. Focus your time on what you can effect now.

See you next week,

Aaron

My state of the internet 'address'

Aaron Heienickle — Fri, 09 May 2025 19:26:00 GMT

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We have a problem. The internet that once connected us has now trained engines working to disconnect us.

Over the last 30 years , humans have successfully created over 1.5 billion websites (Source). While many of these sites are inactive, a great number of active sites continue to provide information, resources, and tools to the world. We connect over social media. We play games with each other across the world. We can learn who the 11th president of the United States was in seconds - James Knox Polk.

Yet, all of this progress has led to a significant threat. The information utopia that we worked so hard on has trained its counterpart, LLM technology.

Large language model (LLM) technology can now create human-sounding text based on prompts. This holds great power for both good and evil.

So, what happens when this great power is granted upon the masses?

With big money up for grabs, startups have granted this tech to practically anybody with a credit card, phone number, and internet access.

While exciting at first, the results have been nothing short of terrifying.

And, I am not as worried about ChatGPT getting something wrong as I am of a more sinister threat.

This threat is autonomous psychological operation machines.

Psychological warfare involves the planned use of propaganda and other psychological operations to influence the opinions, emotions, attitudes, and behavior of opposition groups (Read more).

Instead of direct confrontation from the outside, this form of warfare aims to destabilize the target from within.

The attackers can almost be impossible to track down. And, the attacks can be scaled for cents on the dollar.

Let me show some example potential psyop comments. This example is from the top news app in the United States, NewsBreak.com. However, while I am 99% sure of their nature, I can never be 100% sure due to the anonymity of the attacks. This is what is so terrifying.

Disguised as real people, the autonomous bots seek to sow division, anger, and confusion in the target society.

Without an understanding of what is taking place, many interpret these comments as the comments of REAL people.

To a quick glance, it can feel like the world is falling apart.

The problem gets deeper though.

Our once revolutionary algorithm systems can easily be manipulated. Since 2009, the algos have been built to recommend us content that interests us. Posts with higher engagement rates = More visibility.

In 2024, this means autonomous human-sounding comment bots can boost divisional content to unprecedented reach. Instead of being limited to a small forum, psychological operations now spread like viruses to a large portion of a target country.

The problem goes one final step further.

This reach earns big $$$. And, companies respond very well to incentives. Media companies with the former purpose to inform are now incentivized to farm engagement.

I call this “rage farming.” It is the act of writing short-form prose in the goal of getting engagement-boosting reactions.

The effects are real and dangerous.

On July 13th, we had an assassination attempt on a former President.

While not innocent of following the same “rage farming” techniques, the president nor his opposition should ever see bullets inches from their face.

Attacks like these come from the life or death nature influenced upon us by the internet world.

The question now is: “how do we wake up a nation from this phone psychosis, and come back to real life (AKA, off the web)?”

Construction on Mars + how its even more insane than you think

Aaron Heienickle — Mon, 10 Mar 2025 23:17:00 GMT

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If humanity wants a self-sustaining colony on Mars, we must solve one interesting question.

How do we build on Mars?

To build something, you need two things:

(1) someone or something to do the building.

(2) materials/equipment to build with.

On Earth, we could call up our local construction crew for a project. Get an estimate. Purchase the materials. And, have a home built in a couple of months.

On Mars, construction gets more complicated.

For one, there is no “local” Martian construction crew. And, the Earth crews (if they wanted to take the trip) are quite far away.

The distance from Earth to Mars can be anywhere from 33.9 million miles (54.6 million km) to 250 million miles (401 million km) (NASA.gov).

For reference, that is the minimum length of 497,200,000 football fields.

To build on Mars, we would have to find a way to transport experienced builders across these great lengths - an estimated six month journey.

Adding more complications, construction crew will also need the second main part of the construction equation: the material and equipment to build. And, these can be heavy.

According to WasteRemovalUSA, The average house weight can vary between 80,000 and 160,000 pounds, excluding foundation and contents.
A small bulldozer can weigh up to 30,000 pounds (Source).

Moving construction materials to a build site on Earth is easy. You get a truck, some gas, load the cargo, drive to the site, and unload.

While moving materials from Earth to Mars follows the same concept, it has one additional problem, a very powerful, unforgiving force that we all know as gravity.

To carry large payloads off the planet, we need a tremendous amount of thrust.

We need rocket science.

Luckily, SpaceX and NASA have been hard at work on building bigger and better rockets.

Until we find a way to source materials from the red planet, spaceship payloads will likely be the main means of transportation for Mars construction materials.

Serving as our “delivery truck,” we could load the construction materials on SpaceX’s Starship or a similar load-bearing spaceship.

Since the spaceships can only carry so much, the construction material has to be less than the weight of the current-gen ships’s payload.

According to SpaceX, Starship is currently capable of hauling ~200 tons to Low-Earth-Orbit (LEO) in an expendable configuration, making it the most capable rocket ever developed.

For future evolutions, SpaceX is reportedly working on Spaceship v.3, with specs of over 400 tons for the payload.

After shipping the construction crew, materials, and equipment 33.9 million miles, we could technically build an Earth-like structure.

We did it! Right?

Unfortunately, the problem isn’t solved just yet.

Humans are not very suited for the Martian climate.

Mars is extremely cold.

The common Martian temperature is -63 Celsius. To combat this, buildings will require large, powerful heaters. Otherwise, habitants will quickly get hypothermia.

The atmosphere is very harsh as well. It consists of 95% carbon dioxide. At large doses, this gas is poisonous for humans. Any building on Mars would require an electrolysis machine to produce oxygen.

Electrolysis is a process of running a DC current into an element, such as carbon dioxide, in order to break it down.

Fortunately, this has been proven to work by NASA rover, Perseverance.

The alternative would be shipping huge oxygen tanks. This would cost lots of weight. Or, an even crazier alternative would be terraforming (let’s put that to the side for now). This might cost some nukes and time…

The fact is humans need oxygen to live.

To power an electrolysis machine, the building would need a sustained power source.

I would guess the easiest energy source on Mars is likely the sun. While only receiving 43% of Earths’ solar irradiance, Mars still might be a suitable subject for solar power.

In addition to solar, fuel tanks could power the Mars energy grid. However, like shipping oxygen tanks, every lb of fuel tanks brought will have a heavy weight cost.

The energy problem is likely the most crucial one to solve for reliable Mars construction.

On Earth, we have always said humans need: food, water, shelter to live.

On Mars, it’s more like energy, energy, and energy. Then, air, air, and air. Then, food, water, and shelter after,

So far, in this Substack, we have primarily looked at how we could construct a single, livable building on Mars.

But, there is a bigger looming question.

How do we scale construction efforts on Mars?

It is unlikely that we can scale significantly by shipping construction materials every time we want to build something new.

Material sourcing, transportation, and construction will need to happen on the red planet’s surface.

But, without sustainable energy, most ideas in this area will end up impractical.

That brings us back to the crucial energy problem.

The Martian energy industry will have to be a precursor to the Martian construction industry.

So, how do YOU think we can bring energy to Mars?

For now, Earth may not be a bad spot to keep safe…

Similarities between Baldwin-effect and Technology Adoption

Aaron Heienickle — Sun, 02 Feb 2025 03:18:00 GMT

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I recently watched a recent YouTube debate with Richard Dawkins and Jordan Peterson. What interested me about the debate is that they come from two unique sides perspectives on the field of philosophy. Dawkins more on the side of science, while Peterson on the side of mythos and story.

You can watch here it below:

Finishing up the debate, they come to very interesting agreement: there is something to be looked at in the similarity between the Baldwin effect and the development of Archetypes.

Archetypes are personifications of ways of being. Generally, these are “heroes” we look up to. While all individually unique, they all are considered “valuable.”

As someone who is a fan of entrepreneurship and innovation, I immediately saw a quick similarity between this idea of “adoption over time” with technology/innovation.

Comparison #1: Phenotype plasticity and tech adoption life cycle

Biological and technological progress follows similar trend lines, when being tested for utility.

Phenotype plasticity:

How fast your physical characteristics respond to environmental cues.

Source

Technology adoption curve:

How fast people adopt to new technology.

Source

Comparison #2: Technology adoption rates and rate of adaptation of populations

Technological and biological adoption rates follows similar trend lines, when looking at adoption over time.

Technology usage by house holds over years:

How fast the new ‘gadget’ picks up.

Source

Rate of adaptation of populations over generations:

How fast new physical characteristics pick up.

In Daniel Dennett’s “Consciousness Explained,” everyone is looking for the stick.

The highest stick is the newest level of attainable value. It shows the new “trick.” The brain will then look at what this stick is doing and assimilate. The stick with the good tricks continues to be tall, until everyone “catches on.”

Source

Dawkins idea of meme spread, as depicted by Redditors

How humor spreads and eventually goes away.

In other words, “you can’t use the same joke 100 times”

Once the laggards catch on, it’s not funny.

As many might have predicted, this is a rip off of the investment cycle paradigm:

High fluctuations in what people perceive as true. Some believe stock will go to zero, while some believe stock is the next trillion dollar company. Fluctations level off, as truth is found.

Consciousness and humanity by association is exploratory.

Humans are always looking to find the ideas of the highest potential value. High idea sharing at the idea’s “peak value”, leading to its decline into the assimilation of truth.

Good ideas stay embedded into our minds, as technology and good recipes stays with us.

Creativity and brainstorming- discovering new ideas is a deeply human trait.

However, reality always strikes - the world strikes back at bad ideas.

I think the collective “us” can say gross.

Aaron's Newsletter

Some notes from Missouri Startup Weekend 2026 (MOSX)

What is Startup Weekend?

This year’s numbers

Lots of conversations about AI, as expected

Autonomous bulldozer??

Here is my friend Caleb picking up some dirt with his new bulldozer

Adoption is faster than it’s ever been

Thoughts from the top pitches

A few of top teans — quick impressions

Some of my favorites

I wouldn’t recommend using Claude for presentations just yet.

Demos of functional MVPs

What the best pitches had in common

More from me:

Anthropic is spending $5,000 to serve your $200 plan — bug or master plan?

First, a stat that will blow you away

So, why does this cause your session to die in 19 minutes?

Okay, but why would any company do this on purpose?

Here’s why that second bet is actually well-founded

So, what does this mean for you right now?

Bug or master plan?

More from me:

"Everyone gets a Jarvis"

Some context… the wall came down

Can build is not the same as should build

The build vs. buy math is changing — fast

Who we buy software from is about to change

The moats that still hold

So, when do you build, and when do you buy?

More from me:

Your AI query has a secret 'water bill'

The invisible cost of every prompt

So, where does the water actually come from

1. Municipal water supply

2. Groundwater via on-site wells

3. Surface water from rivers and lakes

Let’s talk scale

We’ve seen this movie before… Bitcoin Mining

The job’s promise doesn’t hold up

The transparency gap

How can we be pro-AI and pro-community?

More from me:

11 top AI tools that I use (free and paid)

1. Claude — my favorite writing & coding assistant

2. ChatGPT — research assistant and GPT-training playground

3. Gemini — scoring content against Google’s search model check

4. Perplexity — the “please don’t make me open 14 tabs” search engine

5. NotebookLM — turning docs into decks & visuals

6. Zapier — general automations and Slack bots that save your sanity

7. Make — workflows when you want more control

8. n8n — workflows when you want ownership and customization

9. Gamma — proposal building when you want to look like you tried

10. Grammarly — polish & readability

11. Replit — coding when I want to move fast

Build a stack that removes friction

AI search has an “earned media bias” -- Research your team should know about

About the study

“Earned media bias” is structural

Why is this happening?

AI engines aren’t one market

Big brand bias: unbranded prompts default to giants

How bias varies by vertical

Language matters more than wording

Moving from reading to doing

So what do top marketers do about it?

The big point

I turned on 'Yoda Mode' for search. Here’s what it means for your business.

Personalization of search is here

What is personalized content, actually?

The shift I think most CMOs will miss

Example: Gucci’s market positioning

How do CMOs prepare?

1) Decide the contexts you want to win

2) Build a one-sentence “assistant answer” for your brand

3) Make your differentiation survive personalization

4) Treat trust signals like product features

The punchline (in Yoda voice, of course)

More from me:

The YouTubification of Google Search