Welcome back!

This week we break down the positioning move every fast-growing AI company uses to sell their vision, walk through how a litigator and a DeepMind researcher built an $11B legal AI company in 42 months, explore Lindy’s pivot from a $50M failed startup to AI agent leader, and show how twin brothers in Bengaluru hit $50M ARR in 7 months by building for the segment everyone else ignored.

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Founder’s Intel

How visionary founders sell big ideas

Data Intel

• Products positioned against a familiar reference point convert at 2-3x the rate of products positioned around novel categories. Familiarity collapses the education curve.

• The fastest-growing AI companies of 2026 use the same positioning move: lead with a familiar wedge, expand into the bigger vision later. Cursor did this. Lindy did this. Lovable did this.

• In a study of 60+ AI-native companies that crossed $100M ARR, 80% of them entered the market with positioning that mapped onto an existing category, not a new one.

Why It Matters

The biggest founders building today have huge visions. AI employees, autonomous companies, agentic workflows running entire businesses.

The vision is real and it’s what attracts talent, capital, and long-term users.

The mistake is leading with the vision in your first pitch.

The market isn’t ready to buy the future on day one. They’re ready to buy something that solves a problem they already understand, faster than what they’re using today.

The wedge has to feel familiar even when the vision behind it is enormous.

Lindy founder Flo Crivello calls this the “Notion head fake.”

Notion’s founders had a massive vision: a no-code platform powering entire workflows. The public positioning was “a better note-taker.”

The familiar wedge made it easy to slot into existing software categories. Once users were locked in, the bigger vision emerged through usage.

Lindy ran the same play.

The vision was “AI employees.” Early positioning was “Zapier of AI” and “if Zapier and ChatGPT had a baby.”

Same product, different framing, totally different uptake. PMF followed within months.

The pattern repeats across the fastest-growing AI products:

• Cursor positioned as a faster VS Code. The vision is an AI-first IDE that rewrites how developers work.

• Loom positioned as a screen recorder. The vision is async video replacing meetings.

• Stripe positioned as “seven lines of code for payments.” The vision is the financial infrastructure layer of the internet.

Each one used the wedge to get adoption, then expanded the surface area once trust was banked. The vision didn’t shrink. The first pitch did.

Founders who lead with the futuristic framing watch users bounce off the page.

The reaction is always the same: “But what does it actually do?” The product gets buried under explanation.

Founders who lead with a familiar wedge get users in the door. The bigger vision gets revealed through usage, not pitch.

Quick Tip

Pick a product everyone already knows and finish this sentence:

“We’re like X, but for Y.”

If your version sounds like a useful upgrade to something familiar, you have your wedge.

Save the bigger vision for the second pitch, the deeper pages on your site, and the conversations you have with users who are already using the product. The vision compounds from inside. It doesn’t have to do the work of getting them in the door.

Behind the Tool

The Road to $11B in 42 months

The Spark

In summer 2022, Winston Weinberg was a first-year litigation associate at O’Melveny & Myers, one of the country’s most prestigious law firms.

His roommate Gabe Pereyra had spent years researching AI at DeepMind and Meta.

One night, Gabe showed Winston a demo of GPT-3 in action. Winston was stunned, less by the tech and more by the fact that nobody seemed to be applying it to real-world professional work.

The two of them ran a test.

They pulled 100 landlord-tenant questions from the r/legaladvice subreddit, built early chain-of-thought prompts before the technique even had a name, and hired three attorneys to grade the answers.

86 of 100 responses were good enough to send to a paying client unchanged.

That was the validation. Winston quit his law job. They emailed Sam Altman. OpenAI became their seed investor on July 4, 2022, and gave them early access to GPT-4.

Harvey was born.

The Build

Most early-stage founders chase the SMB market because the sales cycles are short and rejection feels cheaper.

Every VC told Winston and Gabe to start with solo practitioners and small firms.

They ignored all of it.

The first major customer was Allen & Overy, one of the largest law firms in the world, with a 3,500-lawyer rollout.

At the time, Harvey had four people working out of an Airbnb. Some of their investors said it was a horrible idea.

But Winston knew something most technologists don’t. In professional services, prestige is a trust certificate.

If A&O signed off on Harvey, every mid-market firm downstream would too.

Going to the top of the market wasn’t risky. It was the only move that’d compound.

The product strategy followed what Winston calls “expand and collapse.”

Build hyper-specialized AI agents for narrow legal workflows like M&A compliance analysis or antitrust filings. Then collapse them back into one simple chat-style interface that routes the user’s request to the right agent automatically.

The expand part builds defensibility.

The hardest workflows take the longest to nail and become the deepest moat once they work.

The collapse part keeps the product usable.

Lawyers don’t want to learn 40 different tools. They want one box that handles everything.

The Breakthrough

Sales demos became Harvey’s secret weapon.

Before every pitch, Winston would look up the partner he was meeting, find a recent case from public court filings, and have Harvey analyze the partner’s own arguments live.

He’d tell them: “Find holes in this. How would you argue against your own work?”

Lawyers are trained to be argumentative and skeptical. They read every word.

Sometimes the model got things wrong, but that almost didn’t matter. The lawyers were paying full attention, which no software demo had achieved before.

A&O signed in early 2023. Paul Weiss followed. Then PwC adopted it for 4,000 professionals across 100 countries.

By April 2025, Harvey had 337 enterprise clients in 53 countries and $75M in ARR.

By the end of 2025, ARR had hit $190M.

Headcount scaled with the customer base. Harvey hired aggressively from White & Case, Latham & Watkins, Skadden, and Paul Weiss.

Former Big Law attorneys joined the product and sales teams because lawyers buy from people who understand how a law firm operates.

The sales team’s legal credentials became part of the product itself.

The Next Chapter

Harvey is shifting its business model from selling seats to selling work.

The logic is sharp.

If AI makes lawyers more efficient, they bill fewer hours for the same task, which means seat-based pricing eventually compresses.

Revenue-share deals on completed legal work scale with the value Harvey creates, not with the headcount of the firm.

Winston is also pushing into agentic AI for cross-jurisdictional work, where junior associate hours stack up the most.

Harvey’s bet is that the next wave of legal AI doesn’t assist lawyers. It completes entire workstreams.

Key Lessons

• Go to the top of the market when nobody believes you should.

• Prestige in professional services is the moat.

• One Allen & Overy signature did more for Harvey than a thousand SMB customers would have.

• The hardest customer to win is the one that makes every easier customer trivial.

Tool of the Week

Lindy's Pivot Playbook

What’s Lindy?

A no-code AI agent platform letting anyone build “digital employees” to automate workflows across 1,600+ apps. Revenue grew 5.5x in 12 months. Built by Flo Crivello after pivoting from a $50M failed virtual office startup.

What Worked

• Pivoted hard, fast, and publicly. Crivello’s first startup was Teamflow, a virtual office platform that raised over $50M. When COVID ended and people went back to real offices, growth flatlined. He fired two-thirds of the team and pivoted to AI agents. Most founders drag a dying product for years. Crivello cut the losses and rebuilt around a new bet within months.

• Repositioned from “AI employee” to “Zapier of AI.” The original framing was too futuristic. Users couldn’t picture what they were buying. The Zapier comparison gave them an instant mental model. Same product, different framing, totally different uptake. PMF followed within months.

• Shipped the embarrassing version anyway. Lindy 1.0 was broken. Crivello shipped it before it was ready and used the early users to surface what worked. The signal that became the company came from real usage, not internal roadmap debates.

• Rebuilt at $100K MRR. A prospect handed Crivello a list of use cases the current architecture couldn’t deliver. He spent five to six months rebuilding everything from scratch because, in his words, “99.9% of revenue exists in the future, not the present.” Most founders won’t take that call. They protect what works. Lindy’s growth curve compounded after the rewrite.

• Targeted operators, not devs. n8n, Zapier, and most automation tools fight for developers. Lindy went after non-technical operators in sales, ops, and founder roles. Wider TAM, lower expectations on prompt skills, faster onboarding. The no-code wedge created a different audience than the dev tool category had.

Key Lesson

The fastest-growing AI products don’t sell their vision. They sell a familiar version of the vision and let usage reveal the rest.

Pick a category users already understand, anchor your positioning there, and expand into the bigger story once they trust you.

Pivots aren’t failures. They’re data.

Crivello pivoted off a $50M startup, fired two-thirds of his team, and rebuilt around a new bet.

Treating the pivot as new information rather than a setback is what separates founders who compound from founders who stall.

Fresh Out of the Lab

Goose

What Is It?

An open-source AI agent originally built by Block, now governed by the Linux Foundation’s Agentic AI Foundation.

What’s New

Goose has automatic MCP server discovery, native local model support via Ollama, and improved local-first execution. Point Goose at a project directory and it auto-detects available MCP servers. The setup friction that killed earlier agent frameworks is gone.

The agent runs without sending data to closed APIs. Pair it with local models for fully offline operation, or wire it up to OpenAI, Anthropic, Gemini, or DeepSeek when you need frontier reasoning. Switching models is one config change.

Goose handles multi-step tasks: file editing, shell commands, web requests, browser automation, and code execution inside a sandboxed environment.

The Rust implementation gives it speed and memory efficiency that Python-based agents don’t match.

Why It Matters

Most agent frameworks lock you into a vendor’s stack or a Python-heavy local setup. Goose runs on any laptop, supports every major model provider, and ships under a permissive license.

For founders building agent stacks, this is the cleanest path to a self-hosted, model-agnostic runtime. No subscription fees. No vendor lock-in. No data leaving the machine unless you choose to send it.

Pair it with a local Llama or Qwen model and the entire pipeline runs offline at zero marginal cost.

Founder’s Edge

This Week’s Builder Toolkit

• Dev Tool: Inngest handles async tasks, retries, and queues with a simple API so you don’t have to build orchestration plumbing. Solo founders ship background jobs in hours instead of weeks.

• Free Dataset: ICONIQ Growth’s Topline Growth Report covers ARR benchmarks, growth multiples, and CAC payback periods across 100+ private SaaS and AI companies. Useful for benchmarking your numbers against the market.

• No-Code App: Pory turns Airtable bases into client portals, member sites, and internal tools in minutes. Cleaner middle ground when Glide feels too app-shaped and Softr feels too rigid.

• Productivity Hack: CleanShot X is the screenshot tool you’ll wonder how you lived without. Scrolling captures, instant annotations, paste-as-link to share, screen recordings with built-in cursor highlights. Most founders upgrade once and never look back.

• Learning Resource: Andrej Karpathy’s Neural Networks: Zero to Hero is a free YouTube series that builds neural networks from scratch in plain Python. Karpathy walks through backpropagation, transformers, and GPT-style models line by line. The most-recommended free AI learning resource among engineers shipping in production.

Note: If you’ve found a tool that’s sped up your build process, hit reply and share it. We’ll feature the best submissions in a future issue.

AI Founder’s Journal

$0 to $50M ARR in 7 Months

Mukund Jha had already done the startup grind.

He co-founded Dunzo, scaled it, and watched the long arc of building consumer software in India.

In 2024, he started seeing something his peers were missing.

Vibe-coding tools like Cursor, Copilot, and v0 were exploding, but every single one was built for engineers. The non-technical world (small business owners, entrepreneurs, factories, agencies) was still locked out.

To get custom software built, they would have to pay a dev shop $50,000 to $1,000,000 and wait six months.

That gap was the entire opportunity.

Mukund and his twin brother Madhav started Emergent in early 2025 with a single bet. Build a software creation platform for the people every other AI dev tool was ignoring.

They launched in June 2025. Six months later, the numbers looked unreal.

• $15M ARR by September.

• $50M ARR by January 2026.

• $100M ARR by April.

• 5 to 6 million users across 190 countries.

• 7 million apps built on the platform.

• $100M raised across seed, Series A, and Series B in seven months.

All run by 60 employees split between Bengaluru and San Francisco.

Here's what made it work:

• The product is multi-agent under the hood. A design agent handles the look. A coding agent writes the app. A testing agent breaks it. A deployment agent ships it. Users describe what they want in plain English and the agents coordinate to build a production-ready full-stack application. Not a demo. Not a prototype. Something a small business owner runs their company on.

• The audience choice was the real wedge. While competitors fought over the same pool of developers, Emergent went after factory owners in Tier 2 cities, agency owners building client work, and first-time founders with an idea and a phone. That market had never been served by software tools because software tools assumed technical literacy. Strip that assumption out and the addressable market multiplies overnight.

• Distribution leaned heavily on creator partnerships. Influencers in builder communities, especially in India, Europe, and Southeast Asia, drove the early growth surge. Europe became the second-largest market with one in four new users coming from there. The platform's results were visual and shareable, so every successful build became a product demo on social.

• And the speed mattered. Mukund openly says he works 16-hour days because the LLM landscape is shifting under everyone's feet. Anthropic, OpenAI, and Google all want a slice of this market. Lovable is in the same space. The window to capture the non-technical builder segment is short, and Emergent's bet is that whoever serves that segment best in the next 24 months locks it in.

Key Takeaway

The most valuable AI opportunity in 2026 isn’t building better tools for people who already have tools. It’s building the first usable tool for the segment that’s been priced out, technically locked out, or ignored entirely.

Cursor wins developers. Emergent wins everyone developers were never going to serve.

— Mukund Jha, Emergent co-founder and CEO

Weekly Challenge

One Experiment. One Week. One Win.

The Goal

Rewrite your product’s positioning using the “X but for Y” formula and test it on 10 users this week.

How It Works

1. Pick a familiar product or category your audience already understands. Finish the sentence: "We're like X, but for Y."

2. Update your homepage headline, your one-liner, and your social bio with the new positioning.

3. Send the new framing to 10 users or prospects. Ask: "Does this sound clearer than what we had before?"

4. Track conversion rate or response rate against the old framing.

Why It Works

Most founders write positioning that explains their product.

The fastest-growing companies write positioning that anchors their product to something users already understand.

The shift from “explanation” to “anchor” usually doubles conversion in the first week.

Spotlight

Share your old framing, your new framing, and the response delta in the TAAFT community by end of week. We'll showcase the cleanest repositioning win in an upcoming newsletter.

AI Market Watch

Deals, Discoveries, and Demand

Megadeals

• Rogo – $160M Series D (finance AI)

• Cohere – $600M Series E (foundation models)

• Vast Data – $1B Series F (AI data infrastructure)

• Project Prometheus – $10B follow-on (physical AI)

• Avoca – $125M+ across rounds (AI for home services)

Top Research

• ClawGym (200-instance benchmark for multi-step local workflows)

• GLM-5V-Turbo (native multimodal foundation model with built-in tool use)

• AutoResearchBench (top LLMs score below 10% on scientific literature discovery)

• Visual Generation in the New Era (proposes 5-level taxonomy)

• Contextual Agentic Memory is a Memo (RAG and vector stores aren’t real memory, only lookup)

Search Trends

Top “best AI for…” searches:

1. Video Generation

2. All-in-one Editor

3. AI Content Detection

4. Websites

5. Agents

Thanks for reading!

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Signing off,
— AI Empires