START

The earnings call gets the headlines. The shareholder vote can change the company.

CEO compensation. Board control. Major corporate transactions.

Some of the biggest decisions at public companies are shaped through shareholder votes - but most people never see the work behind them.

Ryan Nowicki Stewart spent years inside that process.

At State Street, he met with boards and C-suites and helped the firm arrive at voting decisions as a major institutional shareholder.

Later, at PJT, he sat on the other side of the table, advising companies navigating those same investors.

And behind a single decision could be thousands of pages of SEC filings, third-party research and years of internal notes.

Ryan describes the workflow as “a total patchwork.”

So he and his co-founder Tommy - who he’s known since undergrad and started alongside at BlackRock -  built Pennant.

Pennant is building the corporate governance OS for institutional investors.

The platform brings meetings, filings, proposals, voting policies and decision records into one place - helping teams analyze and coordinate proxy voting, apply their own principles at scale and maintain a clear record of how decisions were made.

And because Ryan and Tommy spent years working with the exact people they’re now building for, their product bar is simple:

Would our former bosses and teammates use this day in and day out?

That mindset has shaped how they build.

Instead of guessing what customers need, the Pennant team spends time on the desk with institutional investors, starts with their biggest problem and builds from there.

Know the workflow deeply enough, and your former job can become your startup’s unfair advantage.

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🎙️Ryan Nowicki Stewart, CEO & Co-Founder, Pennant on Fondo START  
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00:57 From BlackRock and State Street to building Pennant
01:27 The hidden governance layer behind public companies
02:05 Meeting Tim Cook as Nike’s compensation committee chair
03:47 How boards think about CEO pay and incentives
05:42 Why shareholder voting matters to institutional investors
06:08 The thousands-of-pages “patchwork” Pennant is replacing
07:18 Building software their former teammates would actually use
08:17 Why Pennant starts with each client’s biggest problem

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Check out getpennant.ai

Most companies ask what they should post on LinkedIn.

Sky Yang is focused on a more valuable question:

How does all that activity actually translate into growth?

At Imagine AI, his team is treating LinkedIn less like a content calendar and more like a measurable growth system.

They study how content performs, who it reaches, and how those interactions connect back to prospects and the deal cycle—then use that data to help companies make better decisions about what to say and who should say it.

It starts before a post is ever written.

Imagine AI works with companies to define their strategy, positioning, audience, differentiation, and KPIs. That becomes context the agent can use to create content grounded in what the company is actually trying to accomplish.

Then Sky looks at distribution differently.

A founder, company account, and growth leader each have their own network. Imagine AI uses that to help companies reach more of the people they actually want to reach instead of relying on a single account to carry the entire strategy.

And it doesn’t stop at engagement.

Imagine AI integrates with CRM data so teams can map content interactions to prospects and follow those touchpoints through different stages of the deal cycle. With enough data, the goal is to build a clearer picture of which content decisions are contributing to revenue.

His team has studied how LinkedIn engagement is distributed and is building around the parts companies can make more intelligent.

Imagine AI is taking LinkedIn from “we should probably post more” to a data-driven growth system companies can actually learn from.

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🎙️ Sky Yang, CEO & Co-Founder, Imagine AI on Fondo START  

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01:12 Imagine AI’s data-driven approach to LinkedIn
01:43 The data behind why some LinkedIn posts take off
02:37 Building content from strategy and positioning
04:08 Reaching more of the right audience through your team
06:18 Connecting content engagement to CRM and revenue
08:47 Why Imagine AI is going deep on LinkedIn
10:42 Using AI to surface the DMs that actually matter
11:54 Relationship data, repeated touchpoints, and smarter outreach
14:03 Sky’s advice for growing on LinkedIn

Check out at imagineai.me

Every AI model. One key. Zero markup.

That’s the starting point. The bigger ambition: help companies turn their AI traffic into better models they own

Silen Naihin helped grow AutoGPT to 160,000 GitHub stars. Now, at Experiential Labs, he’s working to bring model access, evaluation and training into one unified experience. 

The open-source gateway gives companies control over model access. From there, the team is building simulations around their traffic to understand different workloads and recommend models based on three things: cost, quality and speed.

The goal goes beyond choosing a model. It’s helping companies evaluate performance and train specialized models around their own work when they need them.

Silen expects agents to do most of the work. So the product has to serve both: humans who need to understand what’s happening and agents that need to use it.  

Start with access. Build toward ownership.

"The open source AI gateway. Hosted providers, your own keys, and your own GPUs behind one endpoint, at the provider's price."

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🎙️Silen Naihin, CTO & Co-Founder, Experiential Labs on Fondo START  

02:51 Going off the beaten path: TKS, Minerva & dropping out
03:37 Getting into YC & the research behind Experiential Labs
05:00 From 8 H100s and continual learning to building a company
05:58 Why every company will become an AI company
06:42 Cost, quality & speed: the “holy trinity” of AI models
07:00 When fine-tuning beats prompting
08:14 What happens when powerful AI gets incredibly cheap
09:00 Why the future could be a model—or LoRA—per customer
10:00 Open-source models get the tokens; closed models get the spend
10:27 Jevons paradox, DeepSeek & exploding AI usage
11:06 Why OpenAI and Anthropic aren’t the next Yahoo
13:05 Experiential Labs’ bigger vision beyond the AI gateway
13:45 Building AI infrastructure for humans and agents
14:01 Optimizing models across cost, quality & speed

Check out experientiallabs.ai

“I don't believe Claude Code will exist in its current form in six months”

Ben Swerdlow, founder of Freestyle, thinks coding agents are moving from local machines to the cloud, where a single task could get the attention of 20 agents at once.

Each gets a complete copy of your stack, production environment included. Each can spend a week testing and refining its approach. You compare the results and take the best one forward.

The economics aren’t there yet. Ben expects cost per task to fall another 99% over the next four years, making that level of parallel work practical.

Freestyle builds the computers for it: full Linux VMs for tasks that run for hours, days, or weeks. Clone a running machine, memory included, and let agents pursue different approaches from the same starting point. Pause and resume with their state intact.

Inside Freestyle, Ben already gives agents a week to find improvements to its VM technology. Roughly 700 tests and 90 metrics help the team judge whether the work made things better.

He calls this goal engineering: define the outcome clearly, give agents time to work toward it, and measure whether they’re making progress.

"Full Linux VMs for AI Agents"

Built for complex tasks that run for hours, days, or weeks

🎙️ Benjamin Swerdlow, CEO & Co-Founder, Freestyle on Fondo START 

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00:57 Why coding agents could move from local to the cloud
02:22 From harness engineering to goal engineering
03:08 Giving agents a week to improve measurable results
04:13 Why defining the problem becomes the bottleneck
04:48 How early access to o1 changed Freestyle’s direction
05:51 Why frustrated sandbox users revealed a bigger opportunity
07:28 Giving agents a computer instead of building custom tools
08:12 Getting into YC 
11:17 Snapshotting VMs and running agents in parallel
12:24 The economics of letting multiple agents attempt every task
13:26 How GPU supply could drive down agent costs
15:09 Try Freestyle and follow Ben

Learn more at freestyle.sh

For years designers made pictures of a product and engineers turned them into code

AI changed that. Designers can now build with code themselves

But their work is scattered. One of Putri Karunia's users told her he now spends only about 10% of his time in Figma. The rest is split across code, prototypes and code changes for engineers to review

The AI coding tools also work one step at a time. Ask for something, get something back, ask again

Design doesn't work that way. You try a few ideas side by side, keep the best and mix in parts of another

Putri built Lunagraph for this. 

It's a design canvas where everything you place on it is real code

You explore the way designers always have. Then click through the result to see how it actually works and share it with your team as a link

Engineers get working code instead of a picture. They take the component they need and add it to the product

"Design with code, on your familiar design canvas"

Design, explore, and hand off the whole experience. States, interactions, and flows,
all as React code, ready for your engineers or your coding agents.

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🎙️ Putri Karunia, CEO & Founder, Lunagraph on Fondo START w/ Guest Host, Grace Gong, Founder, Smart Venture Media

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01:42 Why the design canvas itself is made of code
03:12 Where designers work now that Figma isn't home
03:46 Why design exploration isn't linear and coding agents are
05:19 How Lunagraph differs from Lovable and Claude Design
06:21 Why a chat box can't describe a shadow
08:21 Why designers don't always need to open production PRs
09:16 Handing engineers working code instead of static designs
10:26 Building for the one-designer startup
10:55 Using Lunagraph daily on paying client work
11:25 Bring your own agent and pricing

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Check out www.lunagraph.com

There are over a billion cameras in the world. They can all see. Almost none of them can tell you what they saw.

Andrey Gizdov has been in computer vision since 2016, when CNNs were all the rage, researching real-time vision models.

He met his co-founder Vishal Urlam at a hackathon. His co-founder had deployed cameras and IoT devices across India's power grid to monitor it.

Then they went to conferences on cameras and intelligence and found the state of affairs grim.

From that point, they knew this was a company that was going to exist.

OpenVector connects what cameras see to what businesses do.

Connect an existing camera and describe a workflow in plain English:

Track a misplaced item.

Check that an SOP was followed.

Detect an unscanned item.

Turn an empty shelf into a restocking task.

When something needs attention, OpenVector can take the next step inside the software a business already uses - creating records, sending requests, updating tasks, or notifying someone to act.

Vision → Language → Action.

Doing that without replacing the existing camera infrastructure is the hard part.

Historically, bandwidth and compute pushed vision systems onto on-prem hardware. And ripping out a customer's existing setup doesn't scale.

Andrey says OpenVector has significantly reduced those requirements with almost no loss in accuracy. Today, the company describes its underlying technology as the world's fastest and lowest-bandwidth VLM engine.

During YC, they sold to major companies, including some of the biggest gas-station operators in the country.

The larger bet is that AI is moving out of the chat window and into the physical world.

OpenVector is building the layer between what a camera sees and what a business does next.

🎙️Andrey Gizdov, CEO & Co-Founder, OpenVector on Fondo START

01:39 Harvard and the path into computer vision

02:42 Vision, language, action systems

03:08 Typed commands into camera workflows

03:17 Warehouse and subway use cases

05:15 Why build it now

05:35 Meeting his co-founder

06:02 The camera conference

06:19 Getting into YC, second try

06:38 How they got the domain

07:45 Biggest YC lesson: sales

08:05 Client ROI and talking price

09:23 Why nobody solved this sooner

10:25 Cutting bandwidth and compute

11:22 Foundation models for vision

11:42 AI moving to the physical world

Check out openvector.com

Quippy lets you rehearse the conversation before you have it.

Pushing back on a boss. A first date. Short practice scenarios, line-by-line feedback on what landed and what backfired, and the app turns your weak spots into drills. Then you do it again tomorrow, and it gets sharper each round because it's learning what you specifically keep getting wrong.

Jonathan Li built the whole thing solo, and he's in the current Fall YC batch.

His bet is on the habit, more so than the content. You can build a genuinely good curriculum for almost any skill and watch it go unused, because the bottleneck was never whether the lessons work. It's whether anyone opens the app on a Tuesday when nothing is forcing them to. Small consistent practice beats heroic bursts, and the heroic burst is what most products accidentally optimize for.

So Quippy is built like a game, not a course.

Consumer is a thin slice of his incoming batch. He thinks value has to trickle down to the consumer eventually, and he'd rather be early.

🎙️ Jonathan Li, Founder & CEO, Quippy on Fondo START w/ Guest Host, @gracegongGG, Founder, Smart Venture Media
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01:15 Two and a half years as a PM at Duolingo
01:50 Leaving, a seven-month detour, and starting Quippy
02:45 Duolingo-inspired gamification applied to social skills
05:40 Why building a skill is a habit problem
05:55 How Quippy builds a personalized curriculum
06:50 Dating and work: where people actually use it
08:30 Two months heads down as a solo founder
10:15 Why consumer distribution comes down to volume of experiments
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Check out quippyapp.com

A CFO at a publicly traded company on variable AI pricing:

"This is introducing my worst nightmare, which is a blank check"

 Claude, Codex, Cursor, Devin, Gemini...

 Every workflow creates usage and every token creates cost, and unlike something like a Gong license, the price isn't fixed.

 That's the problem Eric Chernoff is building Fancysauce to solve: AI Cost Management, extra Fancy.
 
Track usage, monitor ROI and optimize spend across every AI workflow.
 
Fancysauce shows every token and ties it to the team, product, model and business value behind it.

Spend lands in the right bucket, COGS or OPEX.
Then you decide what to do about it.

Eric sees a second shift in how companies organize work.
He calls it "death of the org chart, birth of the work chart"

A human can own a task while AI assists, or AI can own it while a human approves; some tasks go entirely to one side.

Fancysauce maps that ownership across people and AI, with spend by team and project and waste, ROI and per-unit efficiency in real time.

🎙️ Eric Chernoff, CEO & Founder, Fancysauce.ai on Fondo START 

01:57 Every token: tracking usage across Claude, Codex, Cursor, Devin, Gemini + more
03:22 Three lanes of AI spend: in-product AI, internal automation and individual usage
05:34 AI budgets by person, and understanding spend across every tool
06:30 Why the work chart replaces the org chart
07:13 Human-owned, AI-assisted vs. AI-owned, human-approved work
08:42 Why reporting to AI may happen at the task level, not the job level
10:49 The CFO problem: variable AI pricing with no fixed cost
13:14 Retain AI measured human work; Fancysauce measures work flowing through agents
15:01 Why big markets carry companies, and why Eric believes AI is an even bigger opportunity

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fancysauce.ai

Somewhere, someone already has the thing you're looking for. 

A dataset, a patent, a capability, a customer, an answer. Most of the time neither of you ever finds out.

That's the problem Ryaan Aqid built Quirk around, and he first recognized it in Bangladesh, watching workers earning $50–100 a month while Upwork listed work paying ten times more. Nothing separated them except information. The opportunity already existed; the connection never happened.

It runs through everything. Institutions sit on datasets they'll never open, companies shelve technology somebody else is desperate for, and entire markets fail to form because two people who'd have built something never met. Ryaan calls it the largest economy nobody can see: the things that were never made.

Quirk builds infrastructure to dissolve that asymmetry, with agents that do the looking so neither side has to move first. Holding something you won't use? It works out who it's worth something to. Stuck? It finds whoever got past this a year ago, without you having to describe the problem.

"Agents that find deals neither side knew existed."

🎙️ Ryaan Aqid, Founder & CEO, Quirk on Fondo START pod

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01:05 Getting the buyers to say exactly what they needed
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01:30 Building distribution through government relationships in Bangladesh
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02:00 Why he thinks the average person's information gets sold through intermediaries
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02:40 The high school nonprofit in Bangladesh
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03:08 Realizing freelance platforms could 10x a worker's income
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03:25 Moving people up Maslow's hierarchy of needs
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04:05 Leaving Cornell two days after classes started
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05:05 What's next and where to follow along

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Check out www.quirklabs.ai

Spend enough at a luxury house and someone actually calls to ask how the bag is working out. Ilya Valmianski brought that up on the show as the piece of retail everyone else quietly gave up on, less because it stopped working than because people are expensive and most brands can't put a store associate on every order.

Signals makes that level of attention cheap enough to give every customer one.

Today it looks like an AI store associate that reaches customers over iMessage after they buy. It answers sizing questions, recommends exchanges when something doesn't fit, flags when a sold-out item is back, remembers preferences, and works out what someone might want next. It's aimed at the relationship rather than the single sale. Their A/B tests against a holdout put it at roughly $30 of incremental repeat revenue per conversation.

The problem underneath started with a number Ilya dropped early in the episode. Every year roughly $1.2 trillion of apparel and fashion sells online, and about $300 billion of it comes back. His argument is that most of those returns aren't buyer's remorse. People bought because they wanted the product. Then the sizing was off, nobody told them what to do about it, and the only obvious path on the screen said Return.

Signals tries to get there first. Rather than processing refunds more efficiently, it opens a conversation that can turn a would-be refund into an exchange, and increasingly into a customer who keeps coming back.

The bigger idea reaches well past ecommerce. Ilya calls it super-staffing: one customer support rep at a $20 million company spends the day putting out fires, but give that same company a thousand AI associates and the job stops resembling support. Everybody gets someone paying attention to them.

He pointed at healthcare, where he worked before this. In nursing, he argues, the ideal staffing level is closer to 100× the current one.

Brands have never lacked the data to treat you like a regular, only the staff to act on it.

"The AI store associate that turns buyers into regulars"

🎙️ Ilya Valmianski, CEO & Co-Founder, Signals, on Fondo START 

01:25 The $300B ecommerce returns problem

02:23 Bringing luxury concierge service to every customer

02:55 Why AI enables personalized support at scale

03:39 Early traction and YC growth ambitions

04:18 Turning refunds into exchanges

04:42 The economics of revenue retention

05:31 Why proactive support beats return portals

06:47 The concept of "super-staffing"

07:08 Reimagining customer support with AI

08:23 The real secret behind startup success

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Check out returnsignals.com