Why Every Company Started After 2025 Will Be AI-Native (And How to Build One)

Write A Catalyst and Build it into Existence.

Article 1 of 5: The AI-First Company Playbook

AI-native companies achieve more with fewer humans, better economics, and less chaos.

I need to tell you about a conversation that made me completely rethink what it means to build a company.

Last month, I was having coffee with Rachel, a veteran entrepreneur who’s built and sold three companies over the past fifteen years. She’s smart, experienced, and knows what it takes to scale a business from zero to eight figures.

“I’m advising a 24-year-old founder right now,” she told me, stirring her coffee with a distant look. “Three-person team. They’re doing $40K MRR. Growing 20% month-over-month.”

“That’s impressive,” I said, thinking about how long it took her first company to hit those numbers.

“That’s not the impressive part.” She leaned forward. “Here’s what’s making me feel old: they have no plans to hire anyone. Not for the next year, at least. Maybe not ever.”

I thought she was joking. You can’t scale a company without hiring, right? That’s just… how companies work.

“Their ‘team’ isn’t three people,” Rachel explained. “It’s three humans and seventeen AI agents. The agents handle customer support, content creation, sales outreach, data analysis, product QA testing, and most of their marketing. The humans handle strategy, product development, and customer relationships.”

She paused, letting that sink in.

“They’re not using AI as a tool to make their team more efficient. AI IS their team. And here’s what keeps me up at night: they’re going to demolish companies like mine that are still hiring humans for everything.”

That’s when I realized: we’re not just adding AI to existing business models. We’re witnessing the birth of an entirely new species of company.

And if you’re starting a business today without understanding this shift, you’re already behind.

What “AI-Native” Actually Means (And Why It Matters)

Let me clear something up immediately: AI-native doesn’t mean “uses AI tools.”

Every modern company uses AI tools. They use ChatGPT for writing. They use AI image generators. Maybe they even have some automation running.

That’s not AI-native. That’s AI-assisted.

Here’s the difference:

AI-Assisted Company:

Built like a traditional company
Uses AI to make humans more productive
AI is a tool in the toolbox
Scales by hiring more people
AI adoption is gradual and optional

AI-Native Company:

Built with AI as the foundation
Uses humans to guide and improve AI systems
AI is the infrastructure, not a tool
Scales by adding more AI capabilities
AI is embedded in every process from day one

Think of it like this: AI-assisted companies are like adding a GPS to your car. Helpful, but the car still fundamentally works the same way.

AI-native companies are like Tesla. The entire vehicle is designed around electrical systems and software from the ground up. You couldn’t just retrofit a traditional car to become a Tesla.

That’s the shift we’re experiencing right now.

The Company I’m Building Right Now (A Real Example)

Let me show you what this looks like in practice.

Two months ago, I started a new business: an AI-powered market research platform for B2B companies. Here’s how my team is structured:

Human Team (3 people):

Me: Strategy, product vision, key customer relationships
Developer: System architecture, workflow optimization, infrastructure
Marketing strategist: Positioning, messaging, growth strategy (15 hours/week, contractor)

AI Agent Team (11 specialized agents):

Research Agent: Gathers market data from multiple sources
Analysis Agent: Identifies patterns and insights in data
Writing Agent: Generates reports and presentations
Quality Control Agent: Fact-checks and validates outputs
Customer Support Agent: Handles inquiries and onboarding
Sales Agent: Qualifies leads and schedules demos
Content Agent: Creates blog posts, social media, case studies
SEO Agent: Optimizes content and tracks rankings
Email Agent: Manages customer communication sequences
Monitoring Agent: Tracks system performance and errors
Optimization Agent: Suggests improvements based on data

Current metrics:

47 paying customers
$23,400 MRR
Growing 28% month-over-month
Customer support response time: 4 minutes average
Content output: 40 pieces per week
Operating costs: $3,200/month (including AI API costs)
Human time investment: ~90 hours/week total (across all three people)

Now here’s what’s wild: my previous company with similar revenue had 12 full-time employees and $89,000/month in payroll and overhead.

This company generates nearly the same revenue with 3 humans and $3,200 in AI costs.

The profit margins aren’t just better. They’re from a different universe.

But that’s not even the most important part. Here’s what really matters: the AI agents get better every week without training programs or performance reviews. They learn from every interaction, every piece of feedback, every data point.

My human team used to spend 40% of their time on repetitive tasks. Now they spend 95% of their time on strategy, creativity, and building relationships — the things humans are uniquely good at.

This isn’t the future. This is happening right now, in January 2026.

The Five Characteristics of AI-Native Companies

After studying dozens of AI-native companies and building one myself, I’ve identified five defining characteristics. If you’re building a company right now, these should be your north star.

1. AI is in the Architecture, Not the Application Layer

Traditional companies: “We have a sales team. Let’s give them AI tools to help with emails.”

AI-native companies: “We have an AI sales system. Let’s add human oversight to handle complex negotiations.”

See the difference?

In traditional companies, AI is added on top of human processes. In AI-native companies, AI IS the process, and humans are added strategically where judgment is essential.

I saw this firsthand last week. A traditional marketing agency I know hired an AI consultant to “implement AI.” After three months, they had ChatGPT subscriptions for everyone and some automation workflows.

An AI-native marketing agency I advise? Their entire content production system is AI-first:

Client brief comes in
AI agent analyzes requirements and competitive landscape
AI generates multiple content options
Human reviews and selects best direction
AI produces final content
AI handles distribution and tracking
Human checks in weekly to optimize strategy

Same output. The traditional agency needs 8 people. The AI-native one needs 2 humans and 6 AI agents.

The difference isn’t what AI does. It’s how deeply AI is embedded in the foundation.

2. Scalability Comes from Systems, Not Headcount

Here’s how traditional companies scale:

Revenue grows → Hire more people → Train them → Hope they perform well → Manage them → Repeat

Here’s how AI-native companies scale:

Revenue grows → Add more AI capacity → Optimize prompts and workflows → Scale infinitely without hiring

Last month, my company had a surge of new customers, 17 signups in one week instead of our usual 5–6.

In my old company, this would have been chaos. Hiring, training, onboarding, scrambling to serve everyone well. It would have taken 2–3 months to stabilize.

In my AI-native company? I increased my Claude API tier and added two more server instances. Cost: $180/month extra. Implementation time: 45 minutes.

The AI agents handled the increased load without breaking a sweat. Customer satisfaction actually went UP because response times stayed fast.

This is the superpower of AI-native companies: your capacity to serve customers can scale 10x overnight. Try doing that with human teams.

3. Data Compounds Instead of Getting Lost

Traditional companies have a data problem. Knowledge lives in:

People’s heads
Scattered documents
Email threads
Tribal knowledge
Meeting notes that nobody reads

When someone quits, they take years of institutional knowledge with them.

AI-native companies have a completely different relationship with data. Every interaction, every decision, every outcome feeds back into the AI systems. Knowledge compounds automatically.

In my company:

Every customer support conversation trains the support agent
Every piece of content we create informs the content agent
Every closed deal teaches the sales agent what works
Every bug report improves the QA agent

The agents don’t forget. They don’t need coffee breaks. They don’t quit and take knowledge with them.

Three months of operation has given my AI agents the equivalent of 2–3 years of human learning. And the gap keeps widening.

This isn’t just efficiency. This is a fundamental competitive advantage that traditional companies can’t replicate.

4. Speed is the Default, Not the Exception

I had a customer last week request a new feature. In my old company, here’s what would have happened:

Customer request logged
Product team meeting (wait 3 days for everyone’s schedule)
Requirements discussion (2-hour meeting)
Technical feasibility assessment (2–3 days)
Priority ranking against other features (another meeting)
Sprint planning (wait for next sprint cycle)
Development (2–4 weeks)
Testing and QA (1 week)
Deployment

Total time: 6–8 weeks, minimum.

Here’s what actually happened in my AI-native company:

Customer request received
AI agent analyzed the request and technical feasibility (15 minutes)
Sent me a summary with three implementation options (Slack notification)
I reviewed and approved option 2 (5 minutes)
Developer built it with AI assistance (4 hours)
AI agent tested it against 50 scenarios (20 minutes)
Deployed

Total time: 6 hours.

This isn’t an exaggeration. We moved from “customer request” to “feature deployed” in one business day.

Speed isn’t just about doing things faster. Speed is about being able to experiment, learn, and adapt faster than your competition. Speed is about serving customers better. Speed compounds.

AI-native companies operate at a fundamentally different velocity than traditional companies. And once you experience it, going back feels impossible.

5. Profit Margins Look “Impossible”

Let me show you some numbers that will seem fake but aren’t.

Traditional SaaS Company at $100K MRR:

Team: 15–20 people
Monthly payroll: $120K-150K
Office and overhead: $15K-20K
Software and tools: $5K-8K
Total costs: ~$140K-180K
Profit margin: -40% to -80% (negative, still fundraising)

AI-Native SaaS Company at $100K MRR:

Team: 3–5 people
Monthly payroll: $30K-45K
AI and infrastructure: $8K-15K
Software and tools: $2K-3K
Total costs: ~$40K-63K
Profit margin: 40–60% (positive, profitable)

The AI-native company is profitable from day one with margins that traditional companies won’t see until $10M+ ARR.

I’m not cherry-picking. This is the standard model for AI-native businesses.

My own company at $23K MRR:

Human costs: $18,000/month
AI and infrastructure: $3,200/month
Other tools: $800/month
Total costs: $22,000/month
Net profit: ~$1,400/month

We’re profitable in month three. My previous company didn’t hit profitability until year two.

This isn’t about being cheap. It’s about fundamental economics. When your marginal cost per customer is measured in API calls instead of human hours, everything changes.

The Three Types of AI-Native Companies Emerging Right Now

Not all AI-native companies look the same. I’m seeing three distinct models emerge, each with different characteristics and opportunities.

Type 1: The Micro-Giant

Characteristics:

1–5 person human team
$500K-$5M annual revenue
Serves hundreds or thousands of customers
Operates like a much larger company
Extremely high profit margins (60–80%)

Examples I’m watching:

Solo founder running a $2M/year legal document automation service
3-person team with $800K ARR AI-powered market research platform
2-person company doing $1.4M/year in AI content repurposing

Opportunity: This is the model for solo entrepreneurs and small teams who want location independence and high income without building a traditional company.

Challenge: Limited by founder attention and strategic decisions. Can’t scale beyond a certain point without adding human leadership.

Type 2: The Hybrid Workforce Company

Characteristics:

10–50 person human team
100–500 AI agents doing specialized work
$5M-$50M annual revenue
Looks like a traditional company from outside
Operates with 1/5th the headcount of competitors

Examples I’m tracking:

Marketing agency with 25 humans, 200+ AI agents, competing against 150-person agencies
SaaS company with 40 employees serving customers that typically require 200+ person support teams
Consulting firm with 15 partners and AI handling 80% of delivery work

Opportunity: This is the model for building venture-scale companies with traditional VC funding but AI-native economics.

Challenge: Managing the hybrid workforce requires new leadership skills. Many traditional managers struggle.

Type 3: The AI-First Platform

Characteristics:

Built to enable other people to build AI-native companies
Provides infrastructure, tools, or marketplaces
Network effects accelerate growth
Potential for massive scale

Examples emerging:

Platforms that let anyone deploy AI agents for specific tasks
Marketplaces connecting AI capabilities with business needs
Infrastructure companies making AI-native easier to build

Opportunity: These could become the next generation of foundational companies (like Stripe, AWS, Shopify).

Challenge: Highly competitive space. Requires technical depth and fast execution.

Each model works. The question is which one aligns with your goals and resources.

The Unfair Advantages AI-Native Companies Have

Let me be blunt about something: if you’re starting a company today and you’re NOT building it AI-native, you’re choosing to compete with one hand tied behind your back.

Here are the specific advantages you’re giving up:

Advantage #1: Speed to Market

I can test a new product idea in 2–3 days instead of 2–3 months.

Last week, I had an idea for a new feature. I described it to my developer, he built it with AI assistance in 6 hours, we tested it with 10 customers, got feedback, and iterated twice. All within 48 hours.

Traditional companies are still in the “scheduling a meeting to discuss feasibility” phase.

Speed isn’t just nice. Speed is survival in 2026.

Advantage #2: Zero Hiring Bottleneck

Growing companies face a brutal reality: hiring is slow, expensive, and risky.

3–6 months to hire each person
$50K-150K per employee annually
30–50% chance they don’t work out
3–6 months to get them fully productive

AI-native companies? Add a new capability in a week. No interviews. No onboarding. No risk of a bad hire.

Want to add 24/7 customer support in French and Spanish? Traditional company needs to hire, train, and manage a multilingual support team. AI-native company? One afternoon of prompt engineering.

Advantage #3: Perfect Institutional Memory

Your best sales rep quits and takes their closing techniques with them. Your star support agent leaves and customers notice the quality drop. Your product manager moves on and nobody knows why certain decisions were made.

AI-native companies don’t have this problem. Every successful interaction trains the AI. Every good decision becomes part of the system. Knowledge compounds instead of disappearing.

I can onboard a new human team member in hours instead of months because the AI agents already embody our institutional knowledge.

Advantage #4: Infinite Scalability

Hit the front page of Hacker News and get 10,000 signups in a day? Traditional company: panic, all-hands-on-deck, scramble to serve everyone, quality drops, some customers have bad experiences.

AI-native company: Spin up more infrastructure, watch the AI agents handle it, customers get fast responses, quality stays high.

I experienced this last month. Viral Twitter thread brought 400+ demo requests in 24 hours. My AI agents handled every single one. Personalized responses. Scheduled demos. Answered questions. Perfect execution.

Try doing that with human team members who need sleep.

Advantage #5: Economics That Seem Magical

Let’s say you want to 10x your revenue.

Traditional company:

Need to 10x your team (roughly)
10x your office space
10x your HR complexity
10x your management overhead

AI-native company:

Same core team (maybe 2x)
10x your AI infrastructure (costs scale linearly)
Same management overhead
Profit margins stay high

Going from $100K to $1M MRR in a traditional company might require going from 20 employees to 150+.

In an AI-native company? Maybe 5 people to 15.

The math is just completely different.

How to Actually Build an AI-Native Company (The Framework)

Alright, enough theory. Let’s talk about how to actually do this.

I’m going to give you the exact framework I used to build my AI-native company and that I’m using to advise three others.

Step 1: Start with the End in Mind

Before writing a line of code or building a single workflow, answer these questions:

What human work are you eliminating entirely? Not “making more efficient.” Eliminating. If the answer is “none,” you’re building an AI-assisted company, not AI-native.

What can only humans do in your business? Strategy? Key relationships? Creative direction? Be specific. Everything else should be AI-first.

How will you 10x without hiring? If your scaling plan involves “hire more people,” you’re thinking wrong. Rethink the architecture.

I spent three days on this exercise before building anything. It saved me months of building the wrong thing.

Step 2: Map Your AI Agent Architecture

This is where most people screw up. They think about tasks, not agents.

Wrong approach: “We need AI to help with customer support, content, and sales.”

Right approach: “We need a Customer Support Agent that handles inquiries, a Knowledge Agent that maintains our documentation, a Quality Agent that reviews responses, a Routing Agent that escalates to humans when needed.”

Think in terms of specialized agents with clear responsibilities, not general-purpose AI that does everything.

My company has 11 distinct agents. Each has:

A specific role and responsibility
Clear inputs and outputs
Defined success metrics
Escalation criteria (when to involve humans)

Draw this out. Literally. I used Miro to map the entire agent architecture before building anything.

Step 3: Build the Minimum Viable Agent System

Don’t try to build everything at once. Start with the three agents that will have the biggest impact.

For most companies, that’s:

Customer support agent
Content/communication agent
Data analysis agent

Build these first. Get them working. Learn from them. Then expand.

I built my first three agents in two weeks. They were rough. They made mistakes. But they worked well enough to validate the model.

Then I added one new agent every 2–3 weeks as I learned what was needed.

Step 4: Create the Human-AI Interface

This is critical and underrated: you need clear protocols for how humans and AI agents work together.

In my company:

AI agents handle 95% of routine decisions autonomously
They flag 5% for human review based on confidence scores
Humans make strategic decisions and handle edge cases
All human decisions train the AI for next time

We have a Slack channel where AI agents post things that need human review. Response time target: 2 hours.

This creates a natural feedback loop where the AI gets better at knowing what it can handle and what needs human judgment.

Step 5: Implement Continuous Learning

Here’s what separates mediocre AI-native companies from great ones: obsessive focus on learning loops.

Every week, we review:

What decisions did AI agents make?
Which ones were correct?
Which ones needed human intervention?
Why did those need intervention?
How do we train the AI so it handles those better next time?

This isn’t optional maintenance. This is your competitive advantage compounding.

We’ve reduced human intervention from 12% of cases in month one to 4% in month three. By month six, I expect it to be under 2%.

That’s not just efficiency. That’s the AI agents getting genuinely better at their jobs.

The Mistakes That Will Kill Your AI-Native Company

I’ve watched seven people try to build AI-native companies in the last four months. Four failed. Here’s why:

Mistake #1: Treating AI Like Interns

This is the #1 killer. People build AI systems but still think of them as assistants that need constant supervision.

Wrong: “The AI drafted this email. Let me rewrite it completely.”

Right: “The AI drafted this email. It’s 85% good. I’ll tweak the closing paragraph and hit send.”

If you’re spending as much time reviewing and fixing AI output as you would creating it yourself, your prompts are bad or your expectations are unrealistic.

AI agents should be autonomous 90%+ of the time. If they’re not, rethink your approach.

Mistake #2: Building Too Much Custom Technology

I see developers get excited about building proprietary AI systems from scratch. Bad idea.

Use existing tools and infrastructure:

n8n for workflow orchestration
Claude/GPT-4 for intelligence
Standard APIs for integrations
Off-the-shelf solutions wherever possible

Your competitive advantage isn’t your technology. It’s your agent architecture, your prompts, your learning loops, and your market positioning.

I built my entire company on n8n, Claude, PostgreSQL, and standard APIs. Zero custom AI models. It works beautifully.

Mistake #3: Not Measuring Agent Performance

You need metrics for every agent. Not vague feelings. Actual numbers.

For my customer support agent:

Response time (target: < 5 minutes)
Resolution rate (target: > 80% without human intervention)
Customer satisfaction (target: > 4.5/5)
Escalation rate (target: < 5%)

I review these weekly. When an agent underperforms, I debug why and improve it.

Without metrics, you’re flying blind.

Mistake #4: Keeping Human Processes

This one’s subtle but deadly. You build AI agents but force them to work within human-designed processes.

Example: A company built an AI sales agent but made it follow their existing 7-step human sales process. The AI could have closed deals in 3 steps, but they forced it to match human workflows.

Rethink the process for AI. Don’t just automate existing workflows.

Mistake #5: Trying to Replace Everything Immediately

Start small. Get wins. Build confidence. Expand.

The companies that fail try to replace their entire operation with AI on day one. It’s overwhelming, breaks things, and creates chaos.

The companies that succeed pick one area, nail it, then expand systematically.

What Success Looks Like: Three Real Examples

Let me show you three AI-native companies that are crushing it right now.

Example #1: The Solo SaaS Founder

Background: Developer who built a data enrichment tool for sales teams

Structure:

1 human (the founder)
8 AI agents (research, data processing, quality control, support, sales, content, monitoring, optimization)

Revenue: $42K MRR Costs: $7K/month Profit: $35K/month Time investment: 25 hours/week

Key insight: He doesn’t try to build everything. He focuses on product vision and key partnerships. AI handles literally everything else.

His support agent handles 94% of inquiries without him. His sales agent qualifies leads and schedules demos. His content agent publishes 5x per week.

He works 25 hours a week and makes $420K/year.

Example #2: The AI-Native Marketing Agency

Background: Two experienced marketers who saw traditional agencies struggling with margins

Structure:

4 humans (2 founders, 1 strategist, 1 designer)
23 AI agents (client research, content creation, SEO, social media, email marketing, analytics, reporting, etc.)

Revenue: $95K MRR Costs: $31K/month
Profit: $64K/month Clients: 28 (average traditional agency with 4 people handles 6–8 clients)

Key insight: They positioned as “AI-powered” from day one. Clients know AI does the execution. They pay for strategy and results.

Average project that takes traditional agencies 40 hours takes them 8 hours of human time + AI execution.

They’re 5x more efficient and can charge premium prices because their results are better.

Example #3: The AI-First SaaS Platform

Background: Three engineers who built a customer research platform

Structure:

12 humans (engineering, product, sales, customer success)
47 AI agents (various specialized research and analysis capabilities)

Revenue: $340K MRR Costs: $118K/month Growth: 35% month-over-month Customers: 240+

Key insight: They serve customers that traditionally required 1:1 human analysts. Their AI agents provide the same quality analysis at 1/10th the cost.

Customers who used to pay $5K-10K/month for human analysts now pay $1,495/month for AI-powered analysis. The AI is faster, more consistent, and available 24/7.

They’re not competing on being cheaper. They’re competing on being better.

All three of these companies are less than 2 years old. All three are profitable. All three are growing faster than traditional competitors.

This is the AI-native advantage in action.

The Timeline: When Will This Become Standard?

Here’s my prediction for how this unfolds:

2024-Early 2025 (Recent Past):

Early adopters started building AI-native companies
Traditional companies began serious experimentation
MCP and advanced AI orchestration became available

Late 2025:

First wave of AI-native success stories went mainstream
Traditional companies realized they were falling behind
Job descriptions started shifting dramatically

2026 (Right Now):

AI-native is rapidly becoming the expected approach for new companies
Traditional companies scrambling to transform or facing disruption
Talent actively migrating toward AI-native companies
Major corporations announcing massive AI transformation initiatives

2027:

AI-native becomes the default for new companies
Traditional companies either successfully transformed or being acquired/disrupted
“AI-native” stops being a special category. It’s just how companies work
First wave of AI-native unicorns achieve billion-dollar valuations

We’re in the acceleration phase right now. Early adopters have proven the model. Now it’s going mainstream fast. The window for being early is closing, but there’s still massive opportunity.

What This Means for You

If you’re reading this, you’re probably in one of these situations:

Situation 1: You’re about to start a company

Don’t build it like a traditional company. Design it AI-native from day one. It’s not harder, it’s different. And the advantages are massive.

Situation 2: You’re running a young company (< 3 years old)

You can still transform into AI-native. It’s easier now than it will be when you have 50 employees and entrenched processes. Start immediately.

Situation 3: You’re running an established company

This is harder but essential. You need a transformation roadmap (which I’ll cover in the upcoming “AI Transformation Roadmap for Traditional Businesses” series). Start with pilot programs.

Situation 4: You’re an employee wondering where this leaves you

Learn AI orchestration, prompt engineering, and systems thinking. The jobs aren’t disappearing, they’re transforming. The people who understand how to work with AI agents will be in massive demand.

Regardless of your situation, ignoring this shift isn’t an option.

What’s Coming Next in This Series

This article showed you WHY AI-native companies are the future and WHAT they look like. But that’s just the foundation.

In the rest of this series, we’ll go deeper:

Article 2: “Architecting Your Business for AI: The 5-Layer Automation Stack”

How to design your company’s AI architecture
The five layers every AI-native company needs
Technical implementation details
Real examples you can copy

Article 3: “Hiring in the AI Age: When to Hire Humans vs. When to Build Agents”

Decision frameworks for human vs. AI
The new roles that are emerging
How to interview and evaluate differently
Team structures that work

Article 4: “The AI-Powered Company Dashboard: Real-Time Intelligence for Every Decision”

Building executive dashboards that update themselves
Predictive analytics and early warning systems
Moving from reactive to proactive management
Specific implementations in n8n

Article 5: “Exiting an AI-First Business: Why They’re Worth 3–5x More”

What makes AI-native businesses more valuable
Due diligence and documentation requirements
Case studies of successful exits
How to position your company for acquisition

Follow me to get notified when the next article drops next week.

And after this series, I’m writing “The AI Transformation Roadmap for Traditional Businesses”. A complete guide for existing companies that need to transform before they get disrupted.

The Bottom Line

Let me tell you something nobody else will say clearly:

If you’re starting a company in 2026 and building it like companies were built in 2022, you’re building a dinosaur.

Not because you’re dumb. Not because you’re lazy. But because the game has fundamentally changed.

AI-native companies have:

5–10x better profit margins
10x faster execution speed
Infinite scalability without hiring
Compounding knowledge that never leaves
Competitive advantages that traditional companies can’t replicate

These aren’t incremental improvements. This is a different species of company.

Three months ago, I was skeptical too. “Sure, AI helps, but you still need people to actually run a business.”

I was wrong.

Today, I run a $280K/year business with three people that would have required 15+ people three years ago. And we’re more profitable, faster, and better than we could have been with a traditional structure.

The technology exists. The playbook is being written. The opportunity is massive.

The only question is: will you build an AI-native company, or will you watch from the sidelines while others do?

I know what I chose.

What about you?

What’s your biggest question about building an AI-native company? Drop it in the comments — I read and respond to every one, and they shape what I cover in upcoming articles.