START

1.2 billion people struggle with addiction.
There will never be enough clinicians to treat them.

Koby Conrad believes AI changes that equation.

His own story starts much smaller.
High 24/7 from 19 to 24.
He got sober.
Taught himself to code.
Built a side project called Sunflower.

Years later, that side project became a VC-backed company with a much bigger ambition.

Today, Sunflower has:
→ 500,000+ users and counting
→ Grown from 200 to 100,000 monthly active users in under 6 months
→ Reached a $1M run rate in about 10 months
→ Tracked 109 years of sobriety every single day
→ Expanded to users in almost every country, across 5 languages

The product combines consumer software with clinical care.

A consumer app featuring:

An AI sponsor for addiction recovery
Visual sobriety progression tracking
A social network built for recovery
CBT journaling and educational content across major addiction types

Alongside a tele-therapy clinic:

- Talk therapy in California and Texas, expanding nationwide with MAT
- World-class doctors building the most effective protocols for addiction

The mission began with one billion sober days.
The team realized they'd reach that goal sooner than expected.
So they raised the target.
One trillion.

Koby's growth philosophy mirrors the one behind his book, "Channels of Growth":
Don't choose between product and distribution.
Build both.
Because at real scale, only a handful of growth channels matter. Find the one that fits your business—and optimize it relentlessly.

His biggest prediction isn't about sobriety.
It's about work.

At Sunflower, they call it 'Operation Ender's Game'

Map every task.
Automate what AI can do today.
Design for what AI will do tomorrow.
The only role left? fleet operator.

🎙️ Koby Conrad, CEO & Founder, Sunflower Sober on Fondo START pod

Chapters:

00:49 – Koby joins the show
01:10 – The pitch: a sobriety platform built for the age of ASI
01:35 – Sunflower's origin as a learn-to-code project
01:54 – Koby's own story: high 24/7 from 19 to 24
02:31 – From bootstrapped side project to VC-backed company
02:46 – Zero to $1M run rate in 10 months
03:00 – Tracking 109 years of sobriety every day
03:10 – Why the mission grew from one billion to one trillion days
03:52 – The growth framework: product and distribution, never one or the other
05:03 – The only channels that scale: virality, paid, organic audience
06:01 – Doing YC with his brother, from selling eggs to SF Compute
08:00 – Operation Ender's Game and the fleet operator future

Bao's job was automating other people's jobs.

So he automated his own.

His team used to forward deploy the old way — go into customers' offices, sit next to their teams, watch how the work actually got done, then build the automations by hand.

Then they asked the obvious question: why are humans doing all of this?

Now Hessian's AI agents construct a digital twin of your business, map your back-office workflows, and take the work over end-to-end — plugging into whatever you already use.

Slack. HubSpot. Salesforce.

No new dashboard. No workflow migration. No figure-it-out-yourself AI tooling.

The thesis behind it is blunt: the future of enterprise AI isn't selling more SaaS.

Businesses want to adopt AI. They don't know how. Another tool doesn't solve that problem — it adds to it.

So the companies that win won't sell software.

They'll sell outcomes.

🎙️ Bao Nguyen, Co-Founder & CEO, Hessian on Fondo START pod

01:28 — Introducing Hessian's vision for forward deployed AI agents that automate business operations.
01:58 — Why conversations with customers revealed that AI adoption is still a major challenge for mid-market companies.
02:34 — How Hessian evolved from a workflow orchestration platform into an AI-first automation company.
03:15 — The insight that led the team to replace traditional forward deployed engineering with AI agents.
03:57 — Why Hessian integrates into existing workflows instead of requiring companies to adopt another SaaS product.
04:28 — Bao's perspective on why enterprise AI is shifting toward outcome-based implementations rather than software licenses.
05:02 — Early customer results, including significant time savings and eliminating the need for a dedicated AI engineer.
05:34 — Meeting his co-founders at university and getting accepted into Y Combinator on their first application.
06:22 — The difference between building startups in London versus San Francisco, and why speed matters.
07:20 — The biggest lesson from Y Combinator: validating ideas with customers before investing heavily in product development.
08:05 — Thoughts on OpenAI's token offer and the changing economics of AI startups.
08:48 — Who Hessian is built for today and how the company approaches AI-powered back-office automation across different stages of growth.

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Check out hessian.sh

Most founders don't need another lawyer.

They need a better system.

Jessica Hubley spent years in Big Law, served as General Counsel at a venture-backed startup, and built her own practice serving startup founders.

She kept seeing the same problem.

Not that founders lacked intelligence.

They lacked exposure to the legal playbook.

They didn't know what kind of lawyer they needed.
Or what needed to happen next.

So she built Story.

"Story.law is your expert General Counsel - fear no legal!"

Aegis transforms your mess wherever it lives today - bringing AI and human legal experts together

Cap table.
Data room.
Pro forma.
Document intelligence.

Built on top-tier lawyers' logic.

Not software that asks founders to think like lawyers.

Software that brings lawyer logic directly into the workflow.

Because founders shouldn't be paying thousands of dollars for lawyers to operate software.

"A cap table is math. Software is better at math than lawyers."

The goal isn't more legal spend.

It's spending less on lawyers - and getting an equal or better result.

Organized before diligence.

Prepared before fundraising.

Deal-ready before the deal.

The future of startup legal isn't replacing lawyers.

It's making expert legal judgment available exactly when founders need it.

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🎙️ Jessica Hubley, Founder & CEO, Story LLP on Fondo START pod

00:00 — Jessica Hubley, founder & CEO of Story, joins the show

00:11 — What Story.Law is building: automated in-house counsel that also manages human lawyers

00:44 — Why founders come to Story - and why second-time founders get it immediately

01:30 — The bigger vision: custom contracts drafted and signed in under 30 seconds

02:04 — From Stanford Law to Big Law to hitting the glass ceiling

02:47 — GC at a health tech startup, then a nonprofit teaching trafficking survivors to code

03:06 — The insight: a YC/Yale founder and a trafficking survivor had the exact same legal problem

03:33 — The Bookface post that got responses for three years straight

04:45 — The absurdity of paying lawyers thousands a month to operate cap table software

06:35 — Founder legal 101: the three mistakes almost everyone makes at the start

10:56 — The #1 reason YC companies die (it's not product-market fit or funding)

14:45 — When you shouldn't spend money on lawyers yet

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Check out story.law

10 coding agents running in parallel.

Zero idea which one finished.

That was the problem.

Around November, Austin Wang and his co-founder Lawrence Chen stopped opening their IDE.

They weren't writing code by hand anymore

Everything ran through Claude Code and Codex in the terminal

10 panes. 10 agents. 10 copies of the repo.

And two problems appeared almost immediately.

They couldn't tell what each agent was working on.

They couldn't tell when an agent had finished.

So they built cmux.com:

Native macOS Ghostty-based terminal with vertical tabs, notifications, and built-in browser

Each workspace shows the task, branch, ports, and pull request - so you always know what every agent is doing.

When an agent finishes, it moves to the top with a blue notification ring.

Like iMessage...

But for coding agents.

Who's adopting it first?

Staff and senior engineers.

It makes sense.

They're already used to breaking large problems into smaller ones and delegating the work.

The difference now?

Instead of junior engineers, they're orchestrating dozens of coding agents in parallel.

Even Austin and his co-founder don't work the same way.

One prefers worktrees.

The other doesn't.

That's why cmux ships primitives, not prescriptions.

Developers build the workflow that fits them.

Because nobody has figured out the "right" way to work with coding agents yet.

And as those agents write more of the code...

The bottleneck won't be software.

It will be human attention.

 Austin Wang, Co-Founder & CEO, Manaflow on Fondo START pod

00:15 — Introducing cmux and the YC journey

00:00:50 — Pivoting from enterprise AI to dev tools

00:01:25 — Leaving the IDE for the terminal

00:02:00 — The problem that led to Arc-inspired workspaces 

00:02:45 — Notifications for coding agents

00:03:25 — Why developers are moving to terminal-first workflows 

00:04:30 — Why senior engineers adopt cmux first 

00:05:50 — Primitives over prescribed workflows

00:07:00 — The future of autonomous code review 

00:08:40 — Why human attention becomes the bottleneck

00:09:20 — Growing to 14.5K GitHub stars

00:11:15 — Building cmux for speed and performance

Check out cmux.com

Most companies don't need smarter AI. They need AI they can actually deploy

OpenClaw is brilliant but can be brutal to set up. Days of configs. 

Agents messaging random contacts on WhatsApp...

Tensol: one click. Five minutes. Each AI employee runs 24/7 in its own isolated VM with full company context

Connect Slack, GitHub, Sentry, HubSpot, Gmail. Every action logged. Enterprise-grade security built in

When we recorded this ep: ~100 AI employees running. 300K+ Twitter views

Not chatbots. Agents with their own computer for engineering, sales, SDR

🎙️ Oliviero Pinotti, Co-Founder, Tensol (YC W26) on Fondo START

01:33 what Tensol does and how it works
02:07 300K Twitter views and what launch day looked like
02:25 ~100 AI employees deployed within 24 hours
02:44 Why it took off
03:42 The problem Oliviero saw early: Mac Minis, firewalls, configs
03:57 Tensol's answer: one-click deploy and isolated sandboxing
04:37 How Tensol handles enterprise security and prompt injection
05:46 Five minutes vs four days to get up and running
06:59 Most common integrations: Notion, HubSpot, GitHub, email
09:43 Getting into YC after multiple attempts
10:43 The full-circle pivot back to the original vision
14:02 Pricing, trial access, and getting started

Most companies think they have a documentation problem.

They don't.

They have a context problem.

Every company runs on things that were never written down - they just live in people's heads.

That's the insight that led Max Arnold and his co-founders Leon Iwanowitsch & David Korn to start Ontora (YC P26).

Because AI agents can only be as useful as the context they're given.

And most organizations have never captured how work actually gets done.

Who owns what

Why decisions were made

Where critical knowledge lives

The result?

Teams spend more time searching than building.

More time aligning than executing.

More time asking who knows the answer than actually acting on it.

Ontora's AI agents interview employees across an organization and build a context layer that grows as the company grows.

What consultants have traditionally spent months uncovering through interviews, Ontora helps companies discover on an ongoing basis.

Because the future isn't just AI

It's AI that understands your company

🎙️ Maximilian Arnold, Co-Founder, Ontora (YC P26) on Fondo START

‍

0:00 — What Ontora builds: agents that interview employees to map how work gets done
1:32— Why most companies have nothing documented, and why AI needs that missing context
2:48 — The consulting tool that turned into the real problem worth solving
3:14 — Meeting the co-founders at CODE, and the pipeline from Berlin to Stanford
4:19 — How project-based learning turns students into founders
5:21 — Getting into YC on the first application, and skipping the German incorporation flip
6:37 — Going Delaware from day one, and interviewing with Tom Blomfield
7:21 — Helpful over friendly, and the real lesson of living with your co-founders
8:05 — Who Ontora is actually for, and where to follow the journey

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Check out at ontora.com

Every company runs on a hidden workforce.

The people who reset the passwords. Clear the MFA requests. Provision the access. Push the tickets through the systems so the rest of the company can work.

Seb Poole wants to give that work to agents.

Not a better way to track the request. A workforce that completes it.

He's seen the problem from the inside.

His last company, Frankie Health, was acquired by Unmind - a leading workplace mental health provider whose customers include Disney and Uber.

At every company he built, the same drag: ServiceNow and Jira tracked the work, but a human still had to do it.

So he and his co-founder Aleks Tomovski built Modern (YC P26). Seb scaled Frankie Health into Unmind; Aleks built developer platforms at Wix and Canva, including Wix's first low-code platform.

Modern is secure agents that understand your business, learn the right workflows, and resolve tickets end to end across the IT function - inside the tools your team already uses, from Slack and Teams to email, voice, WhatsApp, and Telegram.

The result they're seeing today: 80%+ automation across IT team requests.

Not another ticketing system.

The workforce that resolves the ticket.

🎙️ Seb Poole, Co-Founder & CEO, Modern on Fondo START pod

‍

Check out getmodern.ai

00:30 — Building the future of ServiceNow
00:50 — Why most systems track work but never execute it
01:00 — Modern as system of record and the work itself
01:35 — The operational pain that sparked the idea
01:55 — Standing up a shadow workforce for service desks
02:10 — Grounding agents in the context of an organization
02:35 — How agents can 10x service teams
02:50 — Scaling Frankie into Unmind across 150 countries
03:15 — Why he started over instead of resting on an exit
03:40 — Building a company in the post-LLM era
05:00 — Getting into YC on the third application
06:30 — Why the best IT teams are the real problem solvers

The biggest hedge funds have entire teams building AI. Most asset managers are on their own.

But closing that gap isn't about buying more AI. It's about a problem most people get wrong.

You can test code by running it.
You can't test an investment by buying it.

Software engineering and investing aren't the same problem

In software, we've moved from autocomplete to agents to long-running agent swarms - each step climbing another level of abstraction

In investing, human judgment still has to exist at the object level

So the challenge was never deploying agents

The challenge is giving them the context to be useful

Every source of knowledge

Internal and external

Structured and unstructured

Connected to the people making decisions.

That's what Ian M.J. McInnis and his co-founder Ben Finch built at WithAI (YC P26)

Not another AI tool

A customized environment where agents can work the way investors actually do.

Ian knows the problem firsthand. Before co-founding WithAI, he worked at the center of AI and investing at Bridgewater, studying how AI would reshape markets and knowledge work.

His conclusion after trying the alternative:

"The DIY approach ends in pain"

The future isn't simply managing swarms of agents

It's knowing what to delegate

What to automate

And where human judgment remains essential

That's why WithAI focuses on amplifying investors rather than replacing them

Research faster

Monitor more companies

Surface information that would otherwise be missed

"AI for asset managers, built with you": Security, infrastructure, and white-glove service for harnessing the latest agents

🎙️ Ian McInnis, CEO & Co-Founder, WithAI on Fondo START pod

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00:20 Why AI adoption in investing requires more than simply deploying agents

00:35 Building a customized AI operating environment for asset managers

01:12 Making hedge fund-grade AI capabilities accessible to smaller firms

01:55 From Princeton mathematics to a career in investing

02:35 Becoming one of Bridgewater's internal AI specialists

03:25 The origin story of WithAI and partnering with a longtime collaborator

04:35 Identifying the missing tools investors would need in an AI-driven future

05:05 Lessons from an initial YC rejection and what changed

06:15 Why AI agents are creating immediate value for knowledge workers

07:20 The fundamental difference between software engineering and investing

08:35 How to balance AI automation with human judgment

09:50 Where investors can safely delegate work to AI agents

11:00 Measuring AI-driven investment outcomes and client value creation

12:15 The ideal asset manager profile for WithAI today

13:10 Opportunities and excesses emerging in AI infrastructure investing

14:00 Thoughts on SpaceX, xAI, and technology valuations

15:00 The future of AI adoption across institutional investing

Check out www.withai.co

Shipping isn't the bottleneck anymore. Attention is.

Maanav Agrawal kept seeing the same pattern.

Some of the coolest products weren't getting the attention they deserved

Meanwhile, companies that consistently shared what they were building were generating inbound

He'd lived it himself
He saw it in other industries like real estate
He saw it across his YC batch and among founders generally

The problem wasn't always the product.

It was making sure people knew the product existed.

So he and Co-Founder Jason Zhan built Memoir (YC P26)

A marketer with the soul of an engineer. 

Memoir connects to your product source of truth and understands what changed, why it matters, who should hear about it, and how to tell the story.

A new feature ships
A PR worth talking about gets merged
A meaningful update goes live

Memoir turns it into:

→ Social posts 
→ Blogs 
→ Changelogs 
→ Demo videos 
→ Customer updates 
→ Launch content

All in your company's voice.

They built it for themselves: Engineers who love building

But, like most founders, don't always have time to build in public.

Now they don't have to.

The work speaks for itself - automatically

Memoir turns product velocity into market velocity

🎙️ Maanav Agrawal, CEO & Co-Founder, Memoir on Fondo START 

00:53 "Shipping is not the bottleneck, attention is."

01:19 How Memoir connects directly to GitHub and monitors product changes

01:38 Deciding which product updates are important enough to share publicly

02:03 Keeping founders in control with approval before publishing

02:40 Why some of the best products never get the attention they deserve

03:20  The challenge facing startups without dedicated marketing teams

03:49 Why engineering teams move faster than marketing teams can keep up

05:17 The original YC idea: developer infrastructure and latency optimization

05:58 The realization that marketing was the biggest bottleneck in real estate

06:18 The insight behind Memoir: unify engineering velocity and marketing velocity

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Check out trymemoir.ai

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In 2022, San Francisco leased five million square feet of office space

The entire year

In Q1 2026 alone: more than four million

At the time of this recording (Apr 2026) tenant demand reached 7.5 million square feet

The highest ever recorded.

OpenAI has accumulated nearly a million square feet in Mission Bay

Coinbase, Nvidia, and the Warriors all landed at Mission Rock (It's effectively full - premium space there is almost impossible to get)

Reuben Torenberg has brokered SF startup office deals through every major cycle since 2016

The 3% vacancy boom and record rents

The 38% vacancy COVID collapse

And the AI-driven resurgence happening now

We're probably still early in this cycle

The AI wave showed you can do more with less

And that's changing how founders think about space

🎙️ Reuben Torenberg, Senior Vice President, CBRE on Fondo START

00:36 Why brokering compounds the longer you do it

01:08 SF's pre-COVID boom, then the jump to 38% vacancy

02:18 How AI restarted SF office demand

02:42 Where we are in the cycle, and why it isn't the top

03:25 The leasing numbers that signal a real shift

04:34 Why startups are leasing differently now

05:34 How distressed buildings changed hands at a discount

06:11 How landlords regained leverage

07:38 What landlords actually check before they'll sign you

09:48 Why plug-and-play subleases win with startups

12:18 Advice for founders hunting their first office

13:01 Treating the office as a recruiting tool, not a status symbol

learn more at cbre.com