Hamzaa Shergill

How I’d start an AI company today if I had to do it all over again in 2026.

90% of AI startups still fail in their first year.

Worse:
60–70% of “AI wrapper” startups are sitting at exactly $0 in revenue.

Not low revenue. Zero.

Here’s the brutal test I’d run before writing a single line of code:
if OpenAI or Anthropic shut down your API key tomorrow, does your company die with it?

If yes, you didn’t build a business, you built an unpaid distribution arm for someone else’s model.

Swipe through for the 5 decisions I’d make differently this time:

→ Pick a vertical so narrow it feels uncomfortable (not “AI for healthcare”: patient intake for single-doctor clinics)

→ Build a real moat: proprietary data, deep workflow integration, owned distribution — not a thin layer on GPT

→ Price for outcomes, not seats. Per-seat SaaS is dying. $2T is already gone.

→ Stay lean. Solo founders are running $3M+ ARR companies with a $300/month agent stack instead of a payroll

→ Know exactly what VCs are checking for now because “we use AI” doesn’t cut it anymore
The bar is higher than it’s ever been.

But the opportunity is bigger too, 53% of all global VC funding is flowing into this category right now.

Comment “Build” to get the complete guide in DM👇

SpaceX jumped in to give a heads on competition to OpenAI, Google and Anthropic!

SpaceX agreed to acquire Cursor maker Anysphere for $60B in stock, just days after its IPO.

This is not just “Musk buys an AI coding app.” It is SpaceX/xAI trying to own the full stack: compute, models, and the developer interface where agents actually get used every day.

The hidden angle: AI coding tools are becoming strategic infrastructure.

If Cursor becomes part of xAI’s stack, the battle shifts from “which model is smartest?” to “who owns the workflow where developers generate, review, and ship software?”

Why it matters: builders should watch this as a warning shot.

The most valuable AI surfaces may not be chatbots. They may be the daily tools that quietly capture user behavior, code context, enterprise spend, and training signal.

What do you think about this move by Elon Musk?

If you are an AI Product Founder or Building with AI, your life depends on Token Usage!

Founder Notes Page # 2:

If you don’t optimize your token usage, you’ll run out of Fuel sooner than you can imagine.

So what is a token?

You are not paying per idea.
You are paying per token.

A token is not exactly a word.
It is a chunk of text, and those chunks quietly shape your margins, latency, and product limits.

If you are building with AI, you need to understand:

what a token actually is
why output tokens cost more
how context windows change product design
why cheap prompts can become expensive habits
how to estimate usage before launch
The founders who win with AI are not just better at prompting.
They are better at counting.

Save this for later, and send it to a founder who is shipping with AI without tracking token cost.

Lets understand it here👇

Most founders think they need more leads.

They don’t.

They need a system that turns strangers into customers.

Because people don’t wake up one day and decide to buy from you.

They move through stages.

First, they discover you.

Then they learn from you.

Then they start trusting you.

Then they consider buying.

Then they buy.

The problem is that most businesses try to skip half of this process.

They post an offer to people who don’t know them.
They run ads to people who don’t trust them.
They pitch before they’ve built credibility.

And then wonder why conversion rates are low.

Here’s the framework I use:

👉 Attention → Get discovered
👉 Education → Teach something valuable
👉 Authority → Prove you know what you’re talking about
👉 Lead Capture → Turn attention into an audience
👉 Nurture → Build trust over time
👉 Intent → Show people how you solve their problem
👉 Offer → Present a clear path forward
👉 Conversion → Close the sale
👉 Expansion → Turn customers into advocates

Every piece of content should move people one step deeper into the funnel.

Not every post should sell.

Not every email should educate.

Not every ad should convert.

Each stage has a job.

When you understand that, marketing becomes predictable.

The businesses growing the fastest aren’t necessarily the ones with the most traffic.

They’re the ones with the best systems for moving people from:

“I’ve never heard of you”

to

“Take my money.”

That’s the real game.

Comment “Funnel” to get the complete Playbook in your DM.

Claude Code just changed what “AI coding” means.

It’s not one assistant writing functions anymore.

It’s a swarm of agents that plans, fans out across hundreds of parallel workers, reviews its own output, and merges only verified work.

Here’s what happened and how to use it yourself👇

OpenAI just revealed why most AI agents will never get better.

The answer isn’t a bigger model.

It’s a system most builders aren’t even tracking.

Here is the Feedback Loop that makes AI Agent incredibly powerful👇

xAI may have just made one of the smartest AI distribution moves of the year.

Not because of a new model.

Because it turned a consumer subscription into an agent backend.

Today xAI announced that X Premium+ and SuperGrok users can connect Grok directly into Kilo Code — the open-source coding agent used across VS Code, JetBrains, and the terminal.

No separate API key required.

At first glance, this sounds like a small integration update.

It’s not.

The real signal is much bigger:

AI companies may stop competing only on model intelligence…

…and start competing to become the default engine inside agent workflows.

That changes the game entirely.

For the past 2 years, the AI race mostly looked like:

• benchmark scores
• context windows
• token pricing
• latency
• model releases

But agents are changing where value accumulates.

Because once developers start using autonomous coding agents daily…

The model powering those agents becomes deeply embedded into their workflow.

And embedded products are very hard to replace.

That’s why this move matters.

xAI is effectively saying:

“Your consumer subscription is now enough to power real agentic workflows.”

That’s a strong distribution strategy.

Instead of forcing developers to:

• create API accounts
• manage usage billing
• configure infrastructure
• generate keys
• monitor spend

…xAI is lowering the friction dramatically.

And lower friction usually wins adoption.

This is also important because it changes the economics of AI access.

Historically, there was a clean separation:

Consumer subscriptions → chat usage
API access → developer workflows

That wall is starting to disappear.

If consumer plans become usable inside coding agents and automation systems…

API pricing pressure increases.

Because suddenly the cheapest path into agent workflows may not be APIs.

It may be subscriptions.

That’s potentially a much bigger shift than people realize.

But the most important lesson here is about distribution.

The companies that win the agent era may not be the ones with the loudest model launch.

They may be the ones that get integrated everywhere developers already work.

Inside:

• VS Code
• terminals
• IDEs
• browsers
• workflows
• automation systems
• open-source agents

Distribution inside the workflow becomes the moat.

And open-source agents are becoming incredibly important in that battle.

Because developers trust tools they can modify, inspect, and extend.

Which means whoever becomes the default model layer inside those ecosystems gains massive exposure.

For builders, this is the part worth paying attention to:

The next AI winners may not own the entire stack.

They may simply become the default intelligence layer inside existing ecosystems.

That’s a much faster path to adoption.

And it’s why this xAI move matters more than it first appears.

Scrape Data from Internet without getting blocked 👇

Day 7 of 100 days of Mastering AI: Run AI Models Free on your Machine!

Running frontier AI locally is becoming a real option, not just a hobbyist flex.

Open-weight models like DeepSeek V4 and Llama 4 are closing the quality gap while giving builders something closed APIs cannot: control.

No token meter. No vendor lock-in. No sensitive data leaving your machine.

The tradeoff is hardware, setup, and knowing when local inference actually makes sense.

This carousel breaks down where local AI wins, where cloud models still matter, and how tools like Ollama make self-hosted AI practical for builders.e