Unicorns are dead - long live the zombicorns

Valuations soared, IPOs vanished — and insiders quietly cashed out.But a new kind of company is quietly emerging from the wreckage.

Author

Amos Bar Joseph
April 14, 2025

They were supposed to be our generation’s biggest success stories.

Unicorn startups — rare, bold, and billion-dollar by design.

But somewhere along the way, the myth broke.

Today, we’re not watching unicorns gallop toward IPOs.

We’re watching zombiecorns shuffle toward collapse.

Over the weekend, I went deep into the data — and what I found shocked me.

This isn’t just a funding winter.

It’s the end of an era.

The Bubble Has Burst

I kept seeing the same headlines over and over: layoffs, inside rounds, silent shutdowns.

Then it got specific — Convoy (raised $260M) shut down. Tally ($172M) gone. Bench ($112M) folded. All in the same year.

Everyone blamed “the market.” But I wasn’t buying it. So I pulled data from PitchBook, Carta, Crunchbase, and KPMG — and what I found was undeniable:

A decade-long illusion finally starting to unravel.

Between 2015 and 2024, U.S. venture capital investment exploded from ~$36B/year to over $213B/year — a 6X increase. Startups weren’t just raising more money. They were raising faster, earlier, and with less to show for it.

And the number of unicorns?

Also exploded — from 80 in 2015 to nearly 780 in 2024. That’s a 10X increase in companies valued at $1B+, on paper.

But here’s where the math breaks: tech IPOs didn’t follow the same path.

In fact, they dropped by 50%.

Let that sink in:

  • 10X more unicorns

  • 6X more funding

  • But half as many tech IPOs as in 2015

We scaled hype, not businesses, and so the unicorn model didn’t collapse overnight — it quietly decoupled from reality.

Valuations soared. Liquidity dried up. Startups raised like they were heading for IPOs… but the exits never came.

The Rise of the Zombiecorns

From the outside, everything looked fine. Funding was up, teams were growing, and valuations kept climbing.

But inside? These companies weren’t scaling — they were stalling. They became hype machines - Built to raise, not to last:

  • Hiring before PMF.

  • Burning TAM for short-term wins.

  • Launching features no one needed just to feed the narrative.

And when real growth dried up, they found a new story to sell.

"We're the next billion-dollar exit."

Only they weren’t. And they knew it. Because while IPOs vanished and revenue flatlined, insiders found another way to win:

Secondaries.

Private stock sales jumped as much as 5X between 2010 and 2024.

Founders. Early execs. VCs.

They quietly sold their shares at peak valuation — before reality caught up. And the result?

  • Employees left holding worthless equity.

  • Customers locked into platforms with no future.

  • An entire ecosystem fooled by a valuation shell game.

The companies are stuck.
The cash is gone.
And 2026 is when the music stops.

The Age of the Autonomous Business

The zombiecorn era is ending.
And in its place, a new kind of company is rising.

Lean. Profitable. Transparent.
Not built for fundraising headlines — built for customers, outcomes, and truth.

These companies don’t scale by adding headcount.
They scale by orchestrating intelligence.

They’re not waiting for IPO windows to open.
They’re shipping, selling, learning — with small teams and massive leverage.

We call them autonomous businesses.

At Swan, we’re not just cheering from the sidelines.
We’re living it. Proving it. Betting everything on it.

Just 3 founders, no full-time employees — and a growing army of AI agents.
Our goal? $30M ARR with a new operating system for scale.

No bloat. No spin. No excuses.
Just speed, intelligence, and an obsession with results.

If you’re building this way too — or you want to — let’s talk.

The unicorns are dead.
Long live the swans.

FRAMEWORK OF THE WEEK
The Autonomous Business OS - Behind The Scenes

"How do you actually use AI agents to scale?"

This is by far the most common question I get from founders trying to build autonomous businesses. While everyone's talking about AI agents, few are sharing exactly how they're using them to drive real business results.

So today, I'm breaking down the exact AI agent framework we're using at Swan AI to hit our moonshot goal: $10M ARR per employee with just 3 founders.

Instead of organizing by department or function, we've built personal AI taskforces around each founder's core responsibilities.

This is what this framework has enabled us to achieve in the last 30 days:

  • Generated $1M+ in qualified pipeline

  • Onboarded 25 customers (white glove)

  • Shipped 20+ major features

  • Resolved 500+ support tickets

Let's break down exactly how each founder's AI taskforce turns them from individual operators into full-scale departments.

GTM Taskforce: Converting Social Momentum to Pipeline

The Observer

Description: Scans 15k+ post engagements (mine + competitors) to surface hot ICP leads ready to buy, turning LinkedIn's social signals into our pipeline radar.

Pro Tip: Don't waste time on casual engagers. We only classify leads as "HOT" if they've engaged with at least 3 posts or left meaningful comments. This simple rule increased our conversion rate by 3x.

The Hunter

Description: Turns 5k+ monthly anonymous website visitors into qualified opportunities by identifying, researching, and engaging high-intent prospects in real-time.

Stack: Swan AI

Pro Tip: Exclude landing page visits from your targeting logic. Focus on visitors who show real buying intent by visiting multiple product pages, pricing, or case studies. This cut our outreach volume by 40% while maintaining the same conversion rate.

The Gatekeeper

Description: Filters 800+ monthly access requests down to our ideal customers by researching, enriching, and qualifying each lead in real-time.

Pro Tip: Run two distinct motions based on account value. High-value leads get white-glove demo treatment, while smaller accounts flow into self-serve trials. This doubled our sales team efficiency without sacrificing smaller opportunities.

Product & CS Taskforce: Turning Customer Voice Into Product Velocity

The Concierge

Description: Manages 500+ monthly customer conversations by resolving tickets, onboarding users, and gathering feedback. 70%+ of interactions are handled autonomously.

Stack: Swan AI + Slack

Pro Tip: Build a learning loop into your escalation process. When our AI escalates to the team, we don't just solve the issue - we document the solution in the AI's knowledge base. This way, every manual response becomes automated for future similar questions.

The Analyst

Description: Synthesizes 100+ customer conversations per month into structured feature requests and product roadmap priorities.

Pro Tip: Front-load your AI's data collection process. Create detailed classification templates and example outputs before you start - it's much harder to standardize data after the fact. We learned this the hard way after spending weeks trying to normalize inconsistent call summaries.

The Prototyper

Description: Converts product concepts into production-ready prototypes, cutting our design-to-development cycle from weeks to hours.

Pro Tip: Break down complex designs into micro-iterations. AI struggles with understanding broad visual concepts, but excels at small, specific changes. We ship faster by making incremental tweaks rather than trying to perfect everything in one go.

Engineering Taskforce: Shipping Enterprise-Grade Code at Startup Speed

The Architect

Description: Turns product specs into production-ready architecture, suggesting optimal implementation approaches and generating boilerplate code in real-time.

Stack: Cursor

Pro Tip: Invest time in teaching your AI your standards. Create a detailed guide covering your coding structure, naming conventions, and best practices. Without this context, you'll spend more time correcting than coding.

The Auditor

Description: Transforms 100+ hours of security documentation work into automated FRP/DPA responses, maintaining our compliance standards while our codebase evolves daily.

Pro Tip: Turn your security docs into a searchable knowledge base. We fed our entire SOC2 documentation into a RAG system, enabling our AI to instantly answer security questions with perfect accuracy. No more digging through docs.

Getting Started With Your Own AI Taskforce

The key to making this work isn't buying every AI tool out there. Start small:

1. Pick one founder's most time-consuming tasks

2. Build a mini-taskforce of 2-3 agents

3. Focus on creating clear handoffs between agents

4. Document what works and iterate

You don't need enterprise tools to get started. Begin with accessible platforms like n8n or Relevance AI to build your first automated workflows. Once you validate the impact, you can gradually expand your stack.

Remember: AI agents aren't about replacing humans - they're about unlocking superhuman scale.

Don’t automate away, augment!

A Final Note on Small Teams

Every week, I hear from another founder who's cracked the code on scaling without bloating their team. These aren't just edge cases anymore - they're becoming the new standard.

We're part of a growing movement of builders who believe that small teams, empowered by AI, can achieve extraordinary things.

The playbook is being rewritten, and you're early to this shift.

Got questions about where to start?

📩 Reply to this email - I read every message and love helping founders build lean (yes, my AI agents help me keep up with responses 😉).