Nobody is going to send you a memo about this.

There won’t be a company-wide announcement, no awkward all-hands, no policy update in the employee handbook. It will happen the way most uncomfortable economic shifts happen, quietly, through a thousand small decisions that only make sense in hindsight.

Hiring freezes that never quite thaw. Raises that lag inflation for the third year in a row. Job postings that disappear and reappear six months later with a lower salary band. The work gets done. There are just fewer people doing it. And the ones who remain are paid a little less than they expected.

That’s not a layoff. That’s a renegotiation. And it’s already starting.

The Math Companies Are Doing Right Now

Here’s the calculation happening in every CFO’s spreadsheet right now, whether they admit it or not.

A mid-level knowledge worker: analyst, marketer, writer, coordinator — costs roughly $80,000-$120,000 a year in salary alone. Add benefits, office space, management overhead, and that number climbs past $150,000 in total cost.

A good AI stack: Copilot, Claude, Gemini, a few specialized tools — runs maybe $3,000-$5,000 a year per employee.

If that stack handles 40% of what the person used to do, the math changes. Not enough to fire everyone. But enough to ask: do we need to hire the next person? And then: do we need to pay the current person as much?

The answer corporations land on is never spoken out loud. But it shows up in compensation benchmarks that quietly reset downward. It shows up in “we’re restructuring for efficiency.” It shows up in the job description that now lists AI proficiency as required → without raising the salary to match.

What History Tells Us

This isn’t new. It’s just faster.

When spreadsheet software replaced rooms full of human calculators in the 1980s, it didn’t eliminate finance departments. It shrank them, restructured them, and fundamentally changed what a finance professional was worth. The ones who adapted — who learned to use the spreadsheet rather than be the spreadsheet — came out ahead. The ones who didn’t quietly exited.

The same thing happened in manufacturing, in logistics, in customer service. Every time automation absorbed a portion of the work, the humans who remained were expected to do more with the same salary, or accept less for doing the same amount.

AI is running that playbook again. Except this time it’s hitting knowledge workers — the group that always assumed they were safe.

The Productivity Trap

Here’s the part nobody talks about at the company offsite.

When AI makes your team 30% more productive, that gain doesn’t automatically flow to you. It flows to margins. To shareholders. To the argument that the company can now grow without adding headcount.

Goldman Sachs estimated in 2023 that AI could automate tasks equivalent to 300 million full-time jobs globally. They framed it as a productivity story. And it is just not necessarily a worker productivity story. It’s a capital productivity story. The company gets more output per dollar spent on labor. That’s great for the income statement. It’s complicated for your next performance review.

The uncomfortable truth is that productivity gains and wage gains have been decoupled for fifty years. AI doesn’t fix that. It accelerates it.

The New Negotiation

So what does this mean if you’re sitting across from a hiring manager in 2026?

The leverage has shifted. Companies now have a credible alternative to paying market rate for certain tasks, and they know it. The roles most exposed aren’t the ones being automated outright. They’re the ones where AI can do half the job, leaving companies to quietly wonder whether they need a full salary for the remaining half.

The professionals who will hold their earning power are the ones who can answer a different question in that negotiation. Not “what can I do?” but “what can I do that the AI can’t, and what can I direct the AI to do that you can’t afford to lose?”

That’s a very different pitch. And most people aren’t making it yet.

The Bottom Line

The threat to your salary isn’t a pink slip. It’s a plateau.

It’s the raise that doesn’t come. The band that doesn’t move. The headcount that doesn’t get backfilled when someone leaves. It’s the slow, undramatic repricing of human output in a world where a lot of that output just got cheaper.

The $10,000 employee isn’t a robot that replaces you. It’s the AI subscription your employer just bought, that makes them quietly wonder what you’re worth without it.

Start having the answer ready.

Thanks,

Yulia

Originally published at https://theaidialog.substack.com.

Every LinkedIn thought leader is worried about the wrong person.

The narrative is familiar: AI writes code now, so developers are next on the chopping block. GitHub Copilot. Cursor. Claude. The headlines practically write themselves, “Is your software engineer obsolete?”

They’re asking the wrong question.

The real disruption isn’t happening on the engineering floor. It’s happening in the layer above it.

The Middle Manager’s Real Job Description

Here’s what a mid-level engineering manager actually does all day: coordinates information between teams, translates business requirements into tasks, tracks who’s blocked on what, reports upward, shields teams from noise, and follows up on things that should have been done last Tuesday.

Notice anything? None of that is uniquely human. All of it is pattern recognition, communication, and routing — the exact things AI does well.

The developer writes the thing. The manager coordinates the people writing the thing. AI is now very, very good at coordination.

The Numbers Tell the Story

McKinsey’s latest research puts 30% of current work hours in the U.S. as automatable by 2030. But look at which tasks: scheduling, reporting, information synthesis, meeting summaries, status updates. That’s not a developer’s job description. That’s a manager’s.

Meanwhile, demand for software engineers is still projected to grow 25% over the next decade. Demand for middle managers in tech? That projection gets quieter every quarter.

The Champagne Problem

For the last decade, companies hired layers of management to handle the complexity of scaling software teams. It was an expensive but necessary tax on growth. A senior engineer couldn’t spend all day context-switching between Jira, Slack, stakeholder meetings, and roadmap reviews. So you hired someone to absorb that chaos.

AI just made that tax optional.

Tools like Linear + AI can auto-prioritize backlogs. Meeting bots summarize decisions and assign follow-ups. AI project managers can flag blockers before a human even notices them. The coordination overhead that justified an entire layer of org chart is quietly being automated away.

What This Means for Your Career

If you’re a manager who spends most of your time routing information, be honest with yourself: that’s the job that’s disappearing.

If you’re a manager who spends your time making hard calls, protecting your team’s focus, spotting talent, and setting direction — AI makes you more valuable. You’re freed from the coordination tax and left with the work that actually requires judgment.

The developers who are thriving aren’t the ones who ignored AI. They’re the ones who picked it up and doubled their output. The managers who will thrive are the ones who evolve from coordinators into decision-makers.

The disruption isn’t coming for the people who build. It’s coming for the people who mostly just pass messages between the people who build.

The Bottom Line

The org chart is getting flatter. Fast.

AI isn’t killing the builders. It’s killing the middlemen. And in most tech companies, there are a lot of middlemen.

The question isn’t whether your job can be automated. The question is whether, if you stripped away every meeting, every status update, and every Slack relay, anything would be left.

If the answer makes you uncomfortable, that’s the point.

Thanks & bye!

Yulia

Originally published at https://theaidialog.substack.com.

Let’s start with the number that should have broken the internet last week.

Anthropic closed a $30 billion funding round at a $380 billion valuation, the second-largest private financing round in tech history, behind only OpenAI’s $40 billion raise last year. The round was led by Singapore’s sovereign wealth fund GIC and Coatue, with Microsoft and Nvidia also participating.

For context: that valuation is larger than Ford, GM, and Stellantis combined. For a company most people outside tech have never heard of.

So why are serious institutional investors — sovereign wealth funds, Founders Fund, D.E. Shaw — writing nine-figure cheques into Anthropic? The answer tells you everything about where the real money in AI is heading.

The Super Bowl Was a Distraction. The Balance Sheet Is the Story.

You may have caught Anthropic’s Super Bowl ad. The message was pointed: Claude is a space to think, no ads, no distraction, a direct shot at OpenAI, which recently introduced advertising to the free tier of ChatGPT.

It’s a clever piece of positioning. But the marketing is just the surface. The real signal is in the revenue numbers buried in the funding announcement.

Anthropic’s annualised revenue has reached $14 billion, growing more than 10x annually over the past three years. Claude Code alone has a run-rate revenue above $2.5 billion, more than double its level at the start of the year. Customers spending over $100,000 annually have grown 7x in the past year.

These are not chatbot metrics. These are enterprise software metrics.

The Bet: Enterprise Over Everything

Anthropic gets about 80% of its business from enterprises: lawyers, financial analysts, developers, and large corporations paying serious money for a product their employees can trust with sensitive work.

This is the strategic insight behind the funding round. While OpenAI chases consumer scale with a freemium model and advertising revenue, Anthropic is quietly building what looks like the Salesforce of AI, a premium, enterprise-grade platform where the business model is aligned with the user, not against them.

The Super Bowl ad wasn’t just marketing. It was a recruiting pitch to every professional who opened ChatGPT last week and saw a banner ad appear mid-conversation.

The IT Services Shockwave

Here is the angle most coverage is missing.

Alongside the funding announcement, Anthropic has been rolling out Claude Cowork, a desktop application that allows Claude to directly access files, execute multi-step tasks, and operate autonomously within a company’s own infrastructure. You don’t ask it for a formula. You hand it a folder and walk away.

This is a direct threat to the $250 billion IT services and outsourcing industry. The routine, multi-step workflows that enterprises currently outsource to human teams: compliance audits, data migration, customer support triage, basic coding — are exactly the use cases Anthropic is targeting. The fact that Cowork runs locally, on a company’s own infrastructure, removes the data security objection that has slowed enterprise AI adoption.

The investors in this round understand this. The bottom line from analysts is clear: enterprise adoption is driving Anthropic’s revenue, and that’s showing up in the valuation. The question now is whether — and when — it goes public.

What This Means For You

The chatbot era, where AI was a Q&A machine you occasionally consulted, is ending. The coworker era, where AI executes workflows autonomously inside your business, is beginning.

Anthropic’s $380 billion valuation is the market pricing in that transition. The more interesting question isn’t whether Claude beats GPT-5 on a benchmark. It’s which industries are most exposed when a $100k/year offshore workflow gets replaced by a $20/month subscription, and whether the companies running those workflows have noticed yet.

That story is just getting started.

Bye bye!

Yulia

Originally published at https://theaidialog.substack.com.

Do you remember what Sam Altman said in 2024?

He stood on stage at Harvard and admitted, “Ads plus AI is sort of uniquely unsettling to me.” He called advertising a “last resort” for OpenAI.

Well, welcome to 2026. The resort is open, and it is ugly.

This week, OpenAI officially announced they are testing ads in ChatGPT for US users. But that isn’t even the insulting part. The insulting part is that they aren’t just putting ads in the Free tier.

They are putting ads in the paid tier, too.

The new “$8/month ChatGPT Go” plan was pitched as the budget-friendly bridge between Free and Pro. Instead, it has turned into a “Pay-to-still-see-ads” tier. You are literally paying a monthly subscription for the privilege of being monetized.

The “Clean Room” is Gone

For three years, the one massive advantage ChatGPT had over Google was the absence of noise.

• Google: You ask for a toaster recommendation -> You get 5 sponsored links and an SEO-spam blog.
• ChatGPT (Old): You ask for a toaster -> You get an answer.

Now, OpenAI is testing “Pay-Per-Impression” ads at the bottom of answers for Free and Go users. They promise the ads won’t “influence” the answers, but we have seen this movie before. First, the ads are at the bottom. Then, they are “suggested follow-ups.” Then, the model “hallucinates” a preference for the highest bidder.

Why Now? The $14 Billion Problem

Why break the one core promise that made users trust you? Simple math.

Reports leaked this month suggest OpenAI is on track to burn $14 billion in 2026. Subscription revenue isn’t enough to pay for the massive GPU clusters required to fight DeepSeek. They are bleeding cash, and the investors want their return.

So, they are doing the one thing they swore they wouldn’t: turning you into the product.

The Great Migration

Here is the irony: Just as OpenAI introduces ads to “save” their business, they might have handed the market to their competitors.

• DeepSeek: Still costs pennies via API. No ads.
• Gemini: DeepMind CEO Demis Hassabis went on record this week confirming “No plans for ads in Gemini,” specifically to differentiate themselves from OpenAI’s mess.

My Protocol: I am not paying $8/month to see a banner ad for a VPN. I have cancelled my “Go” subscription. For my cheap, quick queries, I’m moving to Gemini Flash. For my heavy lifting, I’m using DeepSeek V3 via API.

OpenAI wanted to be the “OS of Intelligence.” Instead, they are becoming just another ad network.

Apparently there is a way to block the new ChatGPT ads using a custom CSS injector in Chrome. I have not tried it myself though but there are speculations.

Love,

Yulia

Originally published at https://theaidialog.substack.com.

Have you noticed the silence from San Francisco?

For three years, OpenAI sucked all the oxygen out of the room. They shipped features weekly. Sam Altman was everywhere.

But now, in January 2026, amidst the chaos of the DeepSeek launch, where GPT-4 level intelligence became effectively free, the radio silence is deafening.

The industry whispers are asking: “Is OpenAI losing?”

My answer: No. They are leaving the game entirely.

Sam Altman is smart enough to know that you cannot build a trillion-dollar business selling a commodity. He isn’t going to fight a price war he can’t win. While the rest of the market is obsessively benchmarking chatbots, OpenAI is preparing to kill the chatbot.

The Shift: From “Chatting” to “Operating”

For the last few years, the primary interface for AI has been Conversation. You type text into a box; the AI types text back.

The new paradigm for 2026 is Action.

Sources and recent hires suggest that OpenAI’s next major play isn’t just a smarter GPT-5. It is a fundamental shift toward what is internally being called “The Operator.”

An Operator doesn’t just give you advice. It takes over your mouse and keyboard to execute the advice.

• The Chatbot Era: You ask, “How do I book a flight to London?” The bot gives you a list of 10 steps and links to Expedia. You still have to do the work.
• The Operator Era: You say, “Get me to London next Tuesday before noon, economy plus,” and you walk away. The AI opens the browser, navigates the site, handles the 2FA, inputs your credit card, and puts the calendar invite on your phone.

Why This is the Only Move Left

This pivot is the only logical response to a post-DeepSeek world.

If raw intelligence (the “brain”) is cheap, the value moves to Integration (the “hands”).

OpenAI wants to become the operating system layer between your intent and your computer. They don’t want to be the tool you use; they want to be the thing that uses the tools for you. That is a moat that cheap weights and open-source models cannot easily cross.

What It Means for Builders

If your entire business model is “a slightly better UI for chatting with an API,” you are fighting yesterday’s war.

The future belongs to Agentic Interfaces and Browser Control Protocols. We are moving from a world where humans operate software assisted by AI, to a world where AI operates software supervised by humans.

DeepSeek won the battle for the “Brain.” Sam Altman is betting the entire company on winning the battle for the “Hands.”

I am currently testing an early-access “Operator” tool that can control my local desktop environment.

But thats it for now!

Yulia

Originally published at https://theaidialog.substack.com.

We all expected 2026 to start with a bang from Silicon Valley. We were waiting for GPT-5. We were waiting for Gemini 2.5 Ultra. We were waiting for the “Exec Model.”

Instead, the biggest story of January came from a research lab that didn’t exist three years ago.

DeepSeek started 2026 strong. Not because they have the most marketing budget, and not because they have the most famous CEO. They started strong because they understood the one thing the giants missed:

Developers are tired of rationing intelligence.

For the last two years, we have been treating AI like expensive champagne. We save the “good models” for the hard tasks because they cost a fortune. DeepSeek just proved that the champagne can be sold cheaper….much cheaper. By delivering GPT-4 level performance for less than 1/10th of the cost, they didn’t just undercut the competition, they turned intelligence from a luxury good into a utility.

Here is why they are currently dominating the conversation — and many developers’ server logs.

1. They Broke the “Intelligence Tax”

The defining feature of 2025 was the “Compute Moat.” The idea was simple: You need billions of dollars in GPUs to be relevant.

DeepSeek’s architecture (Mixture-of-Experts) destroyed that narrative. By optimizing how the model thinks — activating only the necessary neurons for each token, they have managed to offer GPT-4 level performance for a fraction of the inference cost.

• The Old World: You pay a premium for “Smart.”
• The DeepSeek World: “Smart” is a commodity. You pay for speed.

2. The “Good Enough” Threshold

There is a concept in tech called “Sufficiently Advanced.” Once a camera sensor hits 20 megapixels, normal people stop caring about 21 megapixels. They start caring about battery life.

AI hit that point this month. DeepSeek’s latest release is “smart enough” to write code, summarize documents, and reason through logic puzzles. Is it better than the absolute cutting-edge proprietary model? Maybe, maybe not.

But it is 10x cheaper. And in business, “Equal Quality at 10% Cost” isn’t a competitor. It’s a replacement.

3. Open Weights Win Trust

In 2026, privacy is the new battleground. Companies are realizing they can’t send their proprietary codebases or financial data to a black-box API forever.

DeepSeek started strong because they gave builders the keys. By releasing the weights, they allowed companies to run high-level intelligence on their own infrastructure (or private clouds).

They offered control in an era where everyone else offered dependency.

The Verdict

The reason DeepSeek is winning the start of the year isn’t magic. It’s market fit.

While the giants are locked in an arms race to build a digital exec, DeepSeek built a really, really efficient digital intern. And right now, that is exactly what the market wants to hire.

I’ve been pit-testing DeepSeek V3 against the giants:

- Gemini
- GPT-4o

One of them is winning by a landslide, and it’s not the one you think. I’ll share the full comparison data next week.

Happy New Year!

Yulia

Originally published at https://theaidialog.substack.com.

The problem isn’t that we don’t have enough information. It’s that we have too much.

If you tried to read every important AI update released this week, you would need a 48-hour day. Between the research papers, the product launches, the regulatory updates, and the “thought leadership” threads on LinkedIn, keeping up feels less like staying informed and more like standing under a waterfall.

We are all terrified of missing the “one big thing” that changes our industry. So we compensate by scrolling. We check LinkedIn News in the elevator, skim headlines during lunch, and hoard newsletters we never actually open.

But that’s not learning.

A few weekends ago, I decided to stop fighting the current. I realized I couldn’t filter the internet myself, so I built a robot to do it for me.

Now, at 8:00 AM, I don’t open five different apps to “see what’s happening.” I get one clean, curated notification that tells me exactly what I need to know, filters out the hype, and lets me get on with my day.

Here is how I built my own personal news agent (and how you can too, even if you don’t write code).

The “Anti-Noise” Stack

To build this, I used 3 tools. Total cost? $0.00 for the trial, and pennies per month to run.

1. The Brain: Google Gemini Flash: it’s fast, it’s cheap, and it has a massive context window. I can throw 50 headlines at it, and it doesn’t choke.
2. The Body: n8n: this is the automation platform. If you can draw a flowchart on a napkin, you can use n8n. It connects the dots.
3. The Delivery: (Email/Telegram): I want the news to come to me, where I’m already working.

The Logic: How It Works

Most “news aggregator” apps fail because they just give you more lists. I wanted less.

My workflow looks like this:

Step 1: The Source

First, we need raw data. In n8n, I use the RSS Read node.

I plugged in the RSS feeds for the “high signal” sources I trust (for example Bloomberg Tech).

• The Old Way: I visit too many sites and scroll past ads.
• The Agent Way: The agent checks them every hour for new links.

Step 2: The “Vibe Check” (The Secret Sauce)

This is where the magic happens. If I just summarized everything, I’d still have too much to read. I need a bouncer.

I pass the article titles/descriptions into an AI node with a specific prompt I call “The Vibe Check.”

The Prompt: “You represent a busy tech professional who hates hype. Look at this list of news items.

DISCARD any stories about: Crypto/Memecoins; Generic ‘Company X appoints new VP of Sales’ press releases; Top 10’ listicles

KEEP only stories about: New AI model releases (LLMs, Agents); Major regulatory changes; Hardware breakthroughs

Return only the list of valid articles.”

This single step removes 80% of the fluff before it ever reaches my eyes.

Step 3: The Synthesis

Now that we have the “Survivors”, the 4 or 5 articles that actually matter, we need to know why they matter.

I don’t want a summary; I want a synthesis. I send the surviving articles to Gemini with this instruction:

“Don’t just summarize. Connect the dots. Group these stories by theme. If three different articles talk about ‘Agentic AI,’ combine them into one insight. Output a bulleted list that takes less than 2 minutes to read.”

Step 4: The Delivery

Finally, the agent formats the text and sends it to me on Telegram.

Why Build Instead of Buy?

“Yulia, aren’t there apps that do this?”

Yes. But when you use an app, you are training their algorithm. When you build an agent, you are training yours.

If I suddenly decide I care about “Quantum Computing” next week, I just update the prompt in Step 2. I don’t have to wait for a feature update.

The Result

I built this on a Sunday afternoon. Since then, I’ve saved many hours of doomscrolling.

We spend so much time worrying about AGI (Artificial General Intelligence). But honestly? I’m finding the most joy in “ANI” Artificial Narrow Intelligence. Small, dumb, helpful bots that sweep the floor so I can do the real work.

Want the Blueprint?

I know n8n can look a little intimidating if you haven’t opened it before.

I exported the JSON file for this exact workflow. You can import it into your n8n account, paste your own RSS links, and have this running in 5 minutes.

Respond to this email with “AGENT”, and I’ll send you the file for free.

And of course, Merry Christmas!

Yulia

Originally published at https://theaidialog.substack.com.

If 2024 was the year of “Wow, look what AI can do,” 2025 was the year of “Please, stop giving me new logins.”

I did the math over the weekend. Over the last 12 months, I have signed up for roughly 50 AI services. I have generated avatars, cloned my voice, automated my emails, and let a “finance bot” judge my coffee spending. I have drowned in subscriptions, free trials, and distinct Discord servers.

And now, at the end of the year, I did something radical: I looked at my credit card statement and asked, “Which of these would I actually pay to keep if the price doubled?”

The answer wasn’t 50. It wasn’t even 10. It was 3.

Here is why 90% of the AI tools I tested ended up in the digital graveyard, and the three that earned a permanent spot on my dock.

The “Toothbrush Test” for AI

Most AI tools fail because they are “Vitamins,” not “Painkillers.” They are nice to have, but I don’t panic if they server goes down.

To survive the cut this year, a tool had to pass my version of the Toothbrush Test:

1. Do I use it twice a day? (Utility)
2. Does it save me at least 30 minutes? (Efficiency)
3. Is the output better than what I could do myself? (Quality)

Here are the survivors.

1. The Research Engine: n8n

The “Build Your Own” approach.

I used to have 20 tabs open and three different news aggregators. Now, I have workflows.

The Problem: Most “AI Research tools” are black boxes. You can’t control where they look or how they process information. I didn’t want another subscription that feeds me generic news; I wanted a system I could control.

Why it stayed: n8n isn’t a search engine itself, it’s the factory where I build them. Instead of manually checking websites, I built n8n workflows that scrape specific financial reports, run them through an LLM to extract the key metrics, and Telegram me the summary. It allows me to connect the dots between data sources that don’t usually talk to each other.

• My Killer Use Case: The “Morning Briefing” Agent. I set up a workflow that pulls the latest API updates and tech news, filters out the noise (like crypto scams), and delivers a bulleted list of only what impacts my work. It’s a research engine tailored exactly to my brain.

2. The “Do-er”: Cursor

The tool that builds the other tools.

We spent the first half of the AI boom chatting with bots. We are spending the second half building with them.

The Problem: I have ideas, but I hate the friction of setup. Writing code, debugging environments, and fixing syntax errors used to take 80% of the time, leaving only 20% for the actual logic.

Why it stayed: Cursor is what happens when a text editor actually understands what you are trying to build. It’s not just “autocomplete on steroids”; it’s an engineer sitting next to you. It passed the test because it crossed the line from “helping me write code” to “helping me ship products.” I can write plain English instructions (“Make the mobile view responsive and fix the padding”), and it just… does it.

• My Killer Use Case: I had a messy script for analyzing data that broke every time I ran it. I pasted the error into Cursor and hit Cmd+K. It didn’t just fix the bug; it refactored the code to prevent it from happening again.

3. The Creative Sparring Partner: Gemini

The model with the memory of an elephant.

There are hundreds of writing assistants, but most of them hallucinate or forget what you said three sentences ago.

The Problem: Complex problems require context. If I’m trying to analyze a 50-page regulation document or a year’s worth of blog content, most models choke on the data limit.

Why it stayed: Gemini wins on one specific metric: Context Window. I can feed it entire PDFs, massive codebases, or long chains of previous articles, and it actually “reads” them. It doesn’t just generate generic fluff; it reasons across the massive amount of information I give it. It’s the only tool I trust to handle the heavy lifting of deep analysis.

• My Killer Use Case: The “Devil’s Advocate.” Before I publish a bold prediction, I upload my previous three articles and the new draft into Gemini and ask: “Based on my past arguments, where am I contradicting myself in this new piece?” The consistency check is invaluable.

The Graveyard: What Didn’t Make the Cut

So, who were the losers?

• The “Wrapper” Apps: If your app is just an LLM with a different color interface and a $20/month price tag, I cancelled it. I can build that in n8n now.
• The “One-Trick Ponies”: The tool that only generates PDFs or only summarizes YouTube videos. These features are being eaten by the big platforms (or built in Cursor).

The Verdict for 2026

If 2025 taught me anything, it’s that mastery of a few tools beats access to many.

My advice for next year? Stop looking for the “next big thing.” Pick the three tools that actually solve a problem for you, one to automate (n8n), one to build (Cursor), and one to think (Gemini) → and cancel the rest.

Your credit card (and your sanity) will thank you.

Originally published at https://theaidialog.substack.com.