DEV Community: Aaron Zara

Two of My Most Significant Clients Came From a Text-Based Browser Game

Aaron Zara — Tue, 24 Mar 2026 07:36:27 +0000

A 20-year lesson in trust, reputation, and why the medium never mattered.

Two of my most significant client engagements in the last 20 years came from a text-based browser game.

No LinkedIn. No referrals. No cold outreach.

A game.

No graphics. No animations. No sound. Just text, numbers, and decisions.

My college friends called it "The Amazing 3D Game."

They weren't being kind.

While everyone else in the computer shop was deep in DOTA or Counter-Strike, I had a browser tab open on the side running turns. I was usually the last one to load into matches, always on alt-tab, squeezing in decisions while the lobby waited.

Everyone knew what I was doing.

I was equally good at DOTA and CS. Good enough that we joined tournaments, and in DOTA my friends trusted me to lead the team. Other groups would come to the computer shop specifically to challenge us, money on the line.

TR just fit differently. DOTA and CS end in 20-30 minutes. TR ran for 2 months per set. Turn-based, so you spend your turns and wait for the next batch. A DOTA lobby was dead time. Might as well use it.

I didn't care how it looked. I was hooked.

The Game With No Graphics

The game was called The Reincarnation. You can still find it at thereincarnation.com.

A text-based strategy game where you manage an economy, build armies, and compete against real players in a persistent political environment that resets every season. Think Heroes of Might and Magic, but on the web, no graphics, and with real humans on the other side.

No graphics means no distractions. Just systems.

And the systems were brutal.

You couldn't attack just anyone. Your Net Power determined your range. You could only hit players within 80-120% of your strength. Every action consumed turns. You generated one turn every five minutes, capped at 200.

Every decision was a tradeoff. Spend now for speed, or wait for efficiency. Push early and risk collapse, or play safe and fall behind.

There were no soft failures.

Lose a battle and you don't just take damage. You lose 10% of your land. Less land means a weaker economy. A weaker economy means you can't support your army. Miss upkeep and your entire army disappears.

No army means no protection. At that point, anyone within range can hit you freely. And they will.

The land loop runs in both directions. Win a battle, gain land, build a bigger economy, support a bigger army. Lose, and the whole thing contracts fast. Collapse in TR isn't gradual. It's total.

The Strategy That Shouldn't Work

I played one of the hardest builds in the game: Eradication.

High risk. Tight margins. Constant exposure.

Most players waited until they had full turn banks before making moves. I didn't. I spent at 120-140 turns instead of the 200 cap. Tighter margins, but the time advantage mattered more than the efficiency. Rush the top within the first 2-3 days of a 2-month set, before the field organizes.

That shouldn't work. It's inefficient. It leaves you exposed. Miscalculate and you don't recover.

But speed created a different kind of advantage.

Once you're at the top, the rules change. There's nobody above you to attack. Only players below. And attacking you becomes expensive. Scouting costs one of their limited hits under Rules of Engagement. It signals intent. It risks triggering a full guild war.

So most players don't check. They assume.

And sometimes what I had wasn't a formidable army. Just enough to push my Net Power above theirs. Pure bluff, dressed up as a threat.

That was the real strategy. Not being unbeatable. Making the cost of finding out too high.

The Machiavelli principle: position yourself where no one would rationally start the fight.

What That Actually Means

I didn't have language for it at the time, but the principle is simple:

You don't need to be the strongest. You need to make verification expensive.

If checking you creates risk, most people won't do it. They'll route around you instead.

I've seen the same pattern everywhere since. In SEO, where perceived authority is often enough to win. In positioning, where clarity beats capability. In business, where the strongest player isn't always the one with the best product, but the one nobody wants to challenge.

I learned that in a text-based browser game.

The Part Everyone Misses

The kingdoms reset every season.

The players don't.

You could rebuild your land, your army, your economy. You couldn't reset your reputation.

If you betrayed someone, they remembered. If you showed up when it mattered, they remembered. If you collapsed under pressure, they remembered.

Because the game ran for years, those reputations compounded. The strongest guilds weren't just the most skilled. They were the most trusted.

Detri and I were on opposing sides for years. Enemies on different guilds, competing for the same server rankings. We coordinated through mIRC back then. No Slack, no Discord, just IRC channels and a lot of late night typing. Then in my last year or two of playing TR seriously, we ended up in the same guild.

Former rivals, now teammates. That transition only works if both sides played with integrity when it mattered.

He's the same person who later worked alongside me on the client side. And years after that, introduced me to the PBBG I still play today.

Your enemy today is your guildmate tomorrow. Protect your reputation like the kingdoms don't reset. Because they don't.

The Guildmate Who Changed Everything

Somewhere in those years, I connected with another guildmate. Singaporean. Sharp. Already running digital operations that were producing real money: affiliate revenue, ranked sites, systems that worked.

He pointed me to WarriorForum and BlackHatWorld. Those became my real education. Not courses. Not school. Forum threads, experiments, wins, losses, penalties. People sharing what worked until it stopped working.

We ran ClickBank projects on the side. Tested traffic strategies. Compared notes on what converted.

He was operating at a level I hadn't seen up close before. And he didn't gatekeep any of it.

That's where I learned digital marketing. From a guildmate in a browser game who decided I was worth teaching.

The Accidental International Team

Around 2014, after I came back to the Philippines from Singapore, that same network turned into something real.

We had client work running. I handled development. Detri, the same guy I'd spent years fighting on opposing guilds, handled SEO strategy. His playbooks were years ahead of what most local practitioners were doing at the time.

Three people. Three countries. Philippines, Singapore, Bulgaria.

No Upwork. No LinkedIn. No formal contracts. Just trust built over years of guild wars, tested under pressure, and transferred directly into business.

I coordinated with my developers in Surigao for execution. We ran like that for two years.

And it worked. Then life moved on. Different directions, different projects. The collaboration wound down the way most good things do: not with a falling out, but with everyone building what came next.

What The Game Was Actually Teaching Me

At the time, it just felt like a game. Looking back, it was a parallel curriculum.

Running Eradication meant living on the edge of collapse. That's where I learned resource allocation under constraints.

Guild wars required coordination across time zones with people I'd never met in person. That's where I learned remote team management.

Season resets punished short-term thinking. That's where I learned to play long games.

And nothing mattered more than showing up when it counted. That's where I learned what reputation actually is.

One more thing became clear over time: the real edge never came from publicly shared strategies. The moment something works and gets broadcast, everyone piles in and the advantage disappears. The people worth learning from shared selectively. Through trust. Quietly.

20 Years Later, It Still Pays Off

Here's the part that still surprises me when I say it out loud.

Two of my most significant recent client engagements came through connections from that same game. Not because we talk regularly. Not because we're close friends.

Because over 20 years, they saw enough.

They saw how I operate. They saw that I show up. That I take on hard roles. That I don't quit when things break.

That's the only pitch that ever mattered.

Why My Agency Is Called GodMode

GodMode was a guild name in the game.

It's also the closest description I have for what AI-assisted building feels like now. Shipping production systems in days. Building platforms that used to require teams. Running execution loops at a speed that didn't exist before.

It feels like a cheat code.

But the pattern isn't new. The iteration loop, the distributed trust network, the long-game thinking. I've been running that system for 20 years. AI just removed the last bottleneck: execution speed.

The name isn't nostalgia. It's acknowledgment.

The Actual Lesson

Most people think networking means conferences, LinkedIn, and warm introductions.

My network came from a text-based browser game my friends called "The Amazing 3D Game."

The medium doesn't matter. The behavior does.

Show up consistently. Perform under pressure. Don't disappear when things get hard. Play long enough for reputation to compound.

Because the systems reset. Markets reset. Technologies reset.

The players don't.

And reputation compounds whether you're paying attention or not.

The kingdoms reset every season. The players don't. The guild is still paying dividends.

Why AI Search Gives Different Answers to Different People (And What You Can Do About It)

Aaron Zara — Mon, 16 Mar 2026 01:38:16 +0000

AI search is probabilistic, not deterministic. That's not a bug. It's how these systems are built.

What Is AEO, Quickly

Answer Engine Optimization (AEO) is the practice of structuring content so AI tools cite your business when answering relevant questions.

Perplexity, Gemini, and Claude are increasingly where buyers research before visiting websites. Instead of a blue link on page 1, AEO places your brand inside the answer itself.

The upside: high-trust visibility. The downside: it's harder to measure and inconsistent across users or sessions. Understanding why is the first step to improvement.

Why the Answers Are Different

1. LLMs are non-deterministic by design

Most language models run with a temperature setting above zero. Temperature controls randomness in response wording. At zero, the model always picks the most likely next word. Above zero, it introduces variation for naturalness.

However, temperature has less influence on which sources get cited than most assume. Bigger variables include retrieval window size, how candidate sources rank before the model sees them, and upstream prompt orchestration. Changing any of these produces different citations with identical query wording and temperature.

2. Platforms A/B test continuously

ChatGPT, Perplexity, and others run constant experiments on users. Different accounts land in different test cohorts: different retrieval configs, prompt templates, and ranking logic. You'd never know you're in an experiment. The platform looks identical externally.

3. The index isn't frozen

For AI tools with live web access (Perplexity especially), the underlying index updates constantly. Two queries minutes apart pull from slightly different crawl snapshots. Pages indexed this morning surface differently than the same pages indexed last week.

4. Account tier and context

Free accounts, paid accounts, and logged-in accounts sometimes route to different model versions, tool access, or context depths, even when labeled identically. "GPT-4o" for free users and "GPT-4o" for paid users may not behave the same internally.

5. Context drift within a session

Previous questions in a chat session influence subsequent responses. Earlier tokens shift the probability distribution for everything following, including source citations. Cold sessions and warm sessions often produce different answers to identical queries, even on the same account, model, and minute.

The Right Way to Think About This

Think of AI search like rolling weighted dice.

Your content doesn't guarantee results. It shifts the probability that your answer appears.

Stop trying to make AI cite you every single time. That's unachievable and produces bad strategy. Instead, think in citation rates.

Your goal: increase the percentage of runs where AI tools cite your content for target queries. Moving from 10% to 40% citation rate is a 4x improvement in AI search presence. That compounds fast as AI search volume grows.

It's similar to SEO in 2008. You couldn't guarantee rank 1 every time, but consistent signals made it far more probable. AEO works identically. You're building probability, not locking in a position.

What Actually Improves Your Citation Rate

Write answers, not just articles

AI models skim for extractable answers. If your main point is buried after three paragraphs of context, it gets skipped.

Lead with the answer. Put the core conclusion in your first one or two sentences. Then explain.

Before:
"In the world of real estate, many factors affect property value, and one of the most significant is the government's valuation system..."

After:
"Zonal value is the government's floor price for land per square meter, set by the BIR. It directly affects transfer taxes and capital gains tax when buying or selling property."

The second version gets cited. The first doesn't.

Add definition anchors

LLMs look for clean, extractable definitions. A short "What is X" section structured as a direct answer, not buried inside a paragraph, dramatically increases the chance that section gets pulled into AI responses.

FAQ sections and definition pages in properly structured HTML get extracted at higher rates than narrative prose. Add FAQ schema markup and Article schema to every relevant page.

Build redundancy across sources

When the same facts about your business appear on multiple independent sources (your website, directory listings, third-party articles), AI retrieval systems treat that as higher-confidence information. One source is a claim. Three independent sources resolving to the same entity start looking like a verified fact.

This is N-point verification working in your favor. The more retrieval sources consistently describing your business the same way, the more confidently an AI model will cite you. Inconsistent or contradictory information across sources introduces ambiguity that models resolve by citing someone else.

Make sure your business name, location, and core services are described consistently and explicitly across every platform you publish on. Not just your website. LinkedIn, GitHub, directory profiles, guest posts: every mention is a data point the retrieval system cross-references.

This matters especially for Philippine businesses. Local content is thin. If you're the only structured, consistent source on a specific topic in a Philippine context, your citation rate will naturally be higher. The bar to become the default answer is lower here than in saturated markets.

Name your entities explicitly, and relate them

Write your brand name, location, and topic in close proximity. "Aaron Zara, a licensed real estate broker based in Santa Rosa, Laguna" is far more citable than "our lead broker." AI needs clearly named entities to attribute correctly. Vague references get dropped.

Go further: connect the entity to an attribute to a value in the same sentence. "REN.PH is a Philippine real estate data platform covering 60,000+ verified property listings and broker profiles" gives an AI model three things: the entity (REN.PH), what it is (real estate data platform), and a specific verifiable fact (60,000+ records). That gets extracted and cited. A sentence saying "our platform has a lot of listings" does not.

Publish and update consistently

AI tools with live web search favor recently indexed content. A page last updated in 2022 competes poorly against one updated this month. Regular publishing, even minor updates, keeps content fresh in the retrieval pool.

How to Measure Your Citation Rate

Since AI answers vary per session, a single check tells you nothing. Build a baseline from repeated testing:

Run the same query 10 times across separate sessions and count citations. That's your baseline citation rate for that query.
Test across platforms separately. Perplexity, ChatGPT, and Gemini behave differently and pull from different retrieval systems.
Retest monthly. You're looking for a trend, not a snapshot.

The tooling for AEO tracking is still early. Most businesses haven't started measuring. That gap is the opportunity. Whoever establishes citation rate baselines now will have a meaningful head start when tooling catches up and the practice becomes standard.

The Bottom Line

AI search is probabilistic, not deterministic. The variation you're seeing isn't a measurement error. It's the system working as designed.

You can't force consistency. You can make yourself the statistically likely answer.

Clean structure. Direct answers. Named entities. Fresh content. Corroborating sources across multiple domains. That's what moves the citation rate.

AEO isn't about gaming a single result. It's about building the kind of content AI systems naturally reach for when your topic comes up. Start measuring your citation rate. That's the only number that matters.

One Person vs. an 8-Person Dev Team: The New Economics of Shipping Software

Aaron Zara — Tue, 03 Mar 2026 02:26:36 +0000

In the past several months, I shipped Ren.ph, a real estate platform with over 60,000 programmatic SEO pages. Broker profiles, barangay-level zonal values, municipal pages, provincial data, educational content, and a License to Sell lookup tool. The kind of project that, on paper, looks like it needs a team.

I built it alone.

Not because I'm some genius developer. I've been writing code for 18 years. But 18 years of experience doesn't let you do the work of five to eight people. What changed was the tooling. I built Ren.ph using agentic AI development, where AI doesn't just suggest code but actively builds, iterates, and ships alongside you. It fundamentally altered the math of what one person can produce.

But I want to be honest about what that actually looked like.

What Ren.ph Would Cost the Traditional Way

Let's run the numbers. If I were staffing this project in the Philippines using a conventional development team, here's a realistic breakdown:

The team you'd need: A project manager to coordinate sprints and manage scope. A senior backend developer for the data engineering, APIs, and page generation system. A mid-to-senior frontend developer for templating, UI, and responsive design. A database engineer to architect the schema across 72 provinces, 1,300+ municipalities, and 33,000+ barangays. An SEO specialist who understands programmatic SEO at scale. A content writer for the educational academy section. A QA tester.

That's six to eight people. In the Philippines, mid-level developers earn PHP 50,000 to PHP 80,000 per month. Seniors range from PHP 80,000 to PHP 150,000+. Blended across the team, you're looking at roughly PHP 350,000 to PHP 500,000 per month in salaries alone.

The timeline: Data modeling and architecture alone, figuring out how to structure BIR zonal values spread across dozens of RDO spreadsheets, PRC broker data, and geographic hierarchies down to the barangay level, would take one to two months. Core platform development adds another two to three months. Data acquisition and engineering runs in parallel for two to four months. Content creation, specialized tools, QA, and SEO auditing add more.

Conservatively, you're looking at six to ten months. Realistically, with the coordination overhead that comes with any multi-person team (standups, code reviews, miscommunication, context-switching), it's closer to eight to fourteen months.

The total cost: PHP 2.8 million to PHP 5 million or more. Through an outsourcing firm billing at $35 to $50 per hour per developer, the equivalent project runs $100,000 to $200,000+ USD.

What Actually Happened

I built the MVP across roughly two months of calendar time. Not two months of continuous work. I was simultaneously running several other businesses, building other software products, and consulting for clients. Ren.ph was one of several things on my plate at any given time.

When I did work on it, I worked hard. In the early weeks of discovering what was possible with agentic AI development tools, I was doing sixteen to eighteen hour days. Not just on Ren.ph, but on everything. The productivity feedback loop was intoxicating. It is not a one-way instruction. It's back-and-forth ideation. You propose an approach, the AI pushes back or suggests alternatives, you refine based on what you know about the domain, and together you converge on a solution in minutes instead of days. Then you immediately want to build the next thing. I kept going because the results were immediate and tangible. Ship a feature. See it live. Ship another.

I did this for several weeks before I recognized what was happening. I was burning out. The tool was so effective at removing execution friction that I had eliminated the natural stopping points that normally force you to rest. Waiting for code reviews, waiting for deployments, waiting for team members to finish their part. Those bottlenecks were gone, and without them, there was nothing telling me to stop.

I had to deliberately restructure my work schedule to make my sprints sustainable. I set hard cutoffs. I forced rest days. I learned that having a tool that makes you exponentially productive doesn't mean you should be productive every waking hour.

After adjusting, I still shipped. The MVP, 60,000+ pages, broker directory, zonal value data, educational content, LTS lookup, was live in roughly two to three weeks of equivalent focused work, spread across two months.

The Math

If we compress my actual hours on Ren.ph, it comes to roughly 150 to 250 hours of work. Call it one person-month of effort.

A traditional team would need 40 to 80 person-months to deliver the same output.

That's a 40x to 80x labor efficiency gain. Not a theoretical gain. An actual, shipped, indexed, live-in-production gain.

What AI Didn't Do

Here's the part that matters most.

AI did not come up with the idea for Ren.ph. It did not know that Filipino brokers have no centralized directory. It did not understand that BIR zonal values are buried behind a frustrating multi-step process: you first need to know the correct Revenue District Office, then download the spreadsheet for that RDO, then find the right District Office tab within the spreadsheet, and only then can you search for your specific street or barangay. Nobody had taken the time to consolidate that into a single searchable platform. It did not recognize the SEO opportunity in barangay-level real estate content. It did not have the domain knowledge to structure a platform that would actually be useful to OFWs buying property from abroad.

I brought eighteen years of software development experience, over a decade in real estate, a PRC broker license, and years of programmatic SEO knowledge. I knew what to build and why it mattered. AI eliminated the gap between knowing what to build and actually building it.

Could a non-programmer do this? Technically, yes. Agentic AI tools are getting accessible enough that someone without a development background could attempt it. But here's the problem: you don't know what you don't know. When AI suggests an approach, a database schema, a page generation strategy, a caching mechanism, a non-technical person has no way to evaluate whether that suggestion is good, mediocre, or a ticking time bomb. They take what the AI gives them at face value. They can't push back on bad architectural decisions because they don't recognize them as bad. They can't ask better follow-up questions because they don't know what questions to ask.

The back-and-forth ideation that makes agentic AI powerful only works when both sides of the conversation bring real knowledge. AI brings speed and breadth. The human brings judgment, domain context, and the ability to catch what AI gets wrong. Remove either side and the output degrades significantly.

AI replaced the team I would have needed to hire to execute my own vision. It didn't replace the vision. It didn't replace the strategy. It didn't replace the domain expertise.

What This Means

I'm not making the argument that every developer is now obsolete. I'm making a narrower, more specific claim: the economics of software development have permanently changed for people who have both domain expertise and enough technical literacy to direct AI tools effectively.

If you know your industry, know your users, and can articulate what needs to be built, the execution bottleneck has collapsed. You don't need to raise capital to hire a team. You don't need to spend months in sprint planning. You don't need to manage people, resolve merge conflicts, or sit through standups.

You need a clear vision, relevant expertise, and the ability to work with AI as a force multiplier.

The people who should be paying attention aren't developers worried about their jobs. It's domain experts in every industry who have been sitting on ideas they couldn't afford to build. The barrier just dropped by an order of magnitude.

And that changes everything.

Aaron Zara is a builder and operator based in the Philippines. Founder of GodMode.ph and builder of Ren.ph, a 60,000-page verified Philippine real estate platform built and operated by one engineer.

Front-Running User Intent: Why Your Data Pages Are Answering the Wrong Question

Aaron Zara — Sun, 01 Mar 2026 05:15:20 +0000

Most data-heavy pages are built around what the data is. Not what the user needs to do with it.

This is the gap that separates a reference page from a tool. And in the age of Answer Engines, that distinction determines whether your page gets cited or gets skipped.

The Setup

Ren.ph's barangay zonal value pages serve thousands of users looking up BIR zonal values across Metro Manila and the provinces. The pages are comprehensive. Street-level data, type distribution, rankings, searchable tables. The full picture.

But here's what the data showed us: people searching for "zonal value San Lorenzo Makati" aren't interested in the zonal value itself. Not really. They want to know how much tax they'll pay when they sell their property.

The zonal value is an input. The tax computation is the output. And our page was organized around the input.

What Front-Running Means

Front-running user intent is simple in concept: put the answer to the user's actual question before they have to go looking for it.

In practice, it requires you to challenge your own information architecture. You built the page around your data model: tables, categories, classifications. That's how you think about the data. But the user thinks in terms of a task they need to complete.

The pattern shows up everywhere:

A salary database where users actually want to know their take-home pay after tax
A nutrition facts page where users actually want to know if the food fits their diet
A zonal value page where users actually want to know their capital gains tax

The data is necessary. But it's not the destination.

The Refactor

Here's what we changed on ren.ph's barangay zonal pages.

Before

The page displayed zonal value highlights at the top, followed by distribution charts, street rankings, and a full reference table. The estimated tax section sat near the bottom: static, using only the barangay-level residential value for a fixed 100 sqm lot. A separate street search existed even further down. Two useful features, disconnected from each other and buried below the fold.

After

An interactive tax estimator sits immediately after the zonal value highlights: the second thing users see. It absorbs the street search into itself. Users pick their street, choose their property type, adjust their land area, and the tax computation updates instantly. The zonal value becomes an input to the calculation, not the headline.

The full reference table stays. The rankings stay. But they're now supporting material, not the main event.

Why This Matters for AEO

Answer Engine Optimization requires Algorithmic Integrity as its foundation. But integrity is necessary, not sufficient. Structure matters too.

When an AI is asked "How much is capital gains tax for a condo in San Lorenzo, Makati?", it's looking for a page that directly answers that question. Not one that buries the answer below six other sections.

Front-running intent isn't just better UX. It's better signal to answer engines. You're telling the algorithm: this page exists to answer this question. The structured data, the schema markup, the heading hierarchy: they all reinforce that signal when the page is organized around the user's actual task.

This is the difference between a page that ranks for "zonal value San Lorenzo" and one that also captures "capital gains tax Makati property," "CGT calculator San Lorenzo," and "how much tax when selling condo Makati." The second set of queries represents users closer to a transaction. Higher intent. Higher value.

The Framework

Front-running user intent follows a simple diagnostic:

1. What data does this page display?
Zonal values per square meter, by street and property type.

2. What does the user actually do with that data?
Multiply it by their lot size, then compute 6% CGT and 1.5% DST.

3. Is the page doing that work for them?
No. The user has to find their street, note the value, pull out a calculator, and do the math themselves.

4. What if the page did it instead?
Then the page becomes the tool. Not a stop on the way to the answer. The answer itself.

Apply this to your own data pages. If your user has to leave your page to complete their task, even if it's just to open a calculator app, you haven't front-run their intent. You've given them a data point and made them do the work.

Implementation Isn't Complex

The important thing to understand: this refactor didn't require new data infrastructure. The street-level zonal values were already loaded on the page for the reference table. The tax formulas are two multiplications. The interactive estimator is a client-side component consuming data that was already there.

The barrier was never technical. It was architectural. We had to stop thinking about the page as a data display and start thinking about it as a task completion engine.

This is a pattern we see repeatedly: the data exists, the infrastructure works, but the interface doesn't match the user's mental model. The fix is often a reorganization, not a rebuild.

What to Take Away

If you're building data-heavy tools, whether in real estate, finance, compliance, or any domain where users look up numbers to make decisions, ask yourself:

Am I showing them the data, or am I finishing their thought?

The page that finishes the thought wins the citation, the bookmark, and the return visit. The page that just shows the data gets used once and forgotten.

Front-running intent is how you turn a reference page into a tool. And tools get used.

This enhancement is live on ren.ph's barangay zonal value pages. Godmode Digital provides Fractional CTO services for firms building data infrastructure with Algorithmic Integrity.

The Killer App of LLMs Isn't What You Think

Aaron Zara — Sat, 28 Feb 2026 13:35:30 +0000

I've been building ren.ph for months now. 60,000+ pages of Philippine real estate data, programmatic SEO, structured data, the works. Claude, Gemini, ChatGPT. I use all three daily. They help me write code, generate content, debug systems.

But that's not the most valuable thing they do for me.

The most valuable thing is thinking.

Not "generate a strategy document." Not "summarize this article." I mean actually thinking through problems, out loud, in a chat window, with an AI that has enough context about my business to push back on bad ideas and build on good ones.

At some point I realized something uncomfortable: Claude probably knows more about my business strategy than my accountant, my lawyer, or any single business partner. Not because I set out to share all that. It just happened, naturally, over dozens of conversations. Every decision I talked through, every problem I worked out loud. It accumulated.

Then I started asking people around me. Business owners. Developers. Marketers. People who use LLMs regularly.

Same answer. Every time.

They're all doing it. Thinking out loud with AI. Working through decisions. Exploring ideas they wouldn't bounce off a colleague because it's too early, too half-baked, or too revealing.

I'm starting to think this might be the most widely used application of LLMs, and nobody's naming it.

Why It Works

Every human relationship has friction attached to honesty. You filter what you say to a business partner because there's a power dynamic. You hold back with your team because you don't want to show uncertainty. You don't call your lawyer to brainstorm at 2am because that's a billable call.

With an LLM, all of that disappears.

No judgment. No competition. No gossip. No leverage. No billable hours.

You can say "here's a half-formed idea that might be stupid" and get a thoughtful response without any social cost.

That's not a feature anyone designed. It's an emergent behavior that happened because the friction dropped to zero and the utility was immediate.

The Part Nobody's Talking About

Here's what I think is actually happening at scale: millions of people are now having their most honest, unfiltered thinking conversations with an AI. The stuff they'd normally process alone in their head, or not process at all, is now being externalized into a chat window.

That's powerful. Some of my best strategic decisions for Ren.ph came from thinking out loud with LLMs. Not because the AI gave me the answer, but because the act of explaining my reasoning to something that asks good follow-up questions forced me to sharpen my own thinking.

But it also means something else.

You're sharing more than you realize. Not in one dramatic disclosure, but in the accumulation of hundreds of small conversations. Your business model, your insecurities about it, your competitive concerns, your financial targets, your half-baked ideas, your real opinions about partners and clients. It all adds up.

I'm not saying stop. I'm saying be conscious of it.

What I've Settled On

I still use LLMs as thinking partners. Daily. It's too valuable not to. But I've started being more intentional about it:

I know what I'm sharing and I'm okay with it. I don't give access to everything just because I can (email, file systems, etc.). I treat the convenience as a tradeoff, not a freebie.

The era of AI as a productivity tool is what gets all the headlines. But the quieter, more significant shift might be this: AI as the first thinking partner with zero social cost.

Everyone I've asked is already doing it. You probably are too.