START

Tech is the biggest story in the world and its media is somehow the least fun to watch

Adil Mania moved from Paris to SF to change that

Now he's building a full media company around one idea: tech doesn't need to be packaged like homework

A live night show. A print magazine. A weekly newsletter. An animated recap

Every major industry learned how to entertain

Now it's tech's turn

🎙️ Adil Mania, Founder, Silicon Mania, on Fondo START pod

01:48 — Moved from Paris to San Francisco two months before the recording

02:47 — Built a startup media company in France, then stopped when it became "boring"

04:00 — Silicon Mania began as a new, "bigger, better, bolder" media idea

05:23 — His philosophy: treat life like an experiment; iterate instead of chasing final wins

11:00 — The recap format started as an iteration on old YouTube-style yearly recaps

11:38 — First recap went from ~2K views to 50K overnight, then kept climbing

12:40 — Weekly recaps validated the format with roughly 250K+ impressions

13:40 — His thesis on media: people don't need automated slop; people need trust

16:14 — Expanding the format into a newsletter with frame-by-frame context

17:00 — Bringing back the monthly format in print through Silicon Mania magazine

18:16 — The night show is his favorite product: live, interactive, and built around the human side of founders

19:54 — His ambition: make tech media mainstream enough that "my mom" can enjoy it

23:03 — The long-term vision: build the "NBC for tech"

Liam Karlsson had no clue why his SEO clients were losing traffic while rankings held

Then his 57-year-old mom asked ChatGPT for new running shoes

Nike answer. Bought the shoe. Was super happy. 

Google was never part of that customer journey...

That was the seed of Rankad.ai

He pitched Co-Founder William Gyltman. They went all in

Track and grow brand visibility across ChatGPT, Perplexity, Gemini, and Copilot. 

Enter your domain, 30 seconds later you're in the app. AI agent optimizes your site directly

Liam Karlsson & William Gyltman, Co-Founders, Rankad.ai at The Residency Demo Day 

🎙️ Fondo START pod w/ David J. Phillips (full ep in comments)

00:25 “We’re helping companies earn more money in AI search”
02:34 AI search is moving fast: new models, algorithms, protocols
03:05 A new source of income beyond Google traffic
03:31 AI search is growing fast, but has not outrun Google yet
05:34 Enter your domain and get inside the app in about 30 seconds
05:52 Visibility, competitor, and company data inside one platform
06:07 AI gives optimization tasks based on scan data
06:15 The agent executes tasks on your site
09:41 SEO rankings held, but traffic still disappeared
09:47 Liam’s mom used ChatGPT to buy running shoes
10:00 “That was like the seed of Rankad”

Amazon sellers do not have a data problem.

They have a decision problem.

They already have the clicks
The conversions
The impressions
The keyword history

What they do not have is a system that knows what to do with it.

So brands pay agencies $5,000 to $50,000 a month - and still lose money on ads

Matthew Chen built Laurence to change that.

A quantitative system for Amazon advertising.
Built for continuous decision-making under profit constraints.
Using existing ad copy, reinforcement learning, and custom models to run ads on autopilot.

When confidence is high, it acts
When data is sparse, it borrows signal from similar keywords

The result: about 40% better performance for customers.

Amazon is the wedge.
Autonomous performance marketing is the bigger vision.

🎧 START pod: Matthew Chen, Founder & CEO, Laurence "Autonomous performance marketing"

00:23 What Laurence does
02:12 How the company found the wedge
02:47 The customer quote that changed the company
03:15 The flaw in the standard Amazon ad model
05:25 The move from Amazon to the wider internet
06:12 How Laurence uses transformers today

Construction doesn't fail on the jobsite. It fails in the drawings. The jobsite just reveals it

Project managers spend half their time scanning plans page by page for conflicts between disciplines. Plumbing through steel beams. Electrical into HVAC

They still miss most of it. Millions in rework when caught in the field

Milind Sagaram built Articulate to catch these issues before construction starts

AI reads the PDFs. Finds clashes across architectural, structural, and MEP sheets. Generates draft issue reports automatically

The surprise: construction teams aren't resistant. They want it more than anyone expected

🎙️ Milind Sagaram, Co-Founder & CEO, Articulate / Helonic.com on Fondo START pod 

00:18 AI for finding drawing issues before construction starts
02:24 The old workflow: manual plan review
02:45 The consequence: rework and delays
03:00 Small issues, massive downstream cost
03:27 Copilot, not replacement
04:04 Why AI belongs in construction
04:39 What surprised him about selling into the industry
05:59 Who Articulate sells to

Agents don’t need bigger models. They need better tools.

Morph trains coding subagents.
Not for humans. For frontier models.

Fast Apply edits at 10,000 tokens/sec.
WarpGrep handles code and log search.

Both keep the main model’s context clean

Because when context gets too large, performance drops.

Now Morph is pushing coding subagents even faster.

One newer model runs at 33,000 tokens/sec: https://docs.morphllm.com/sdk/components/compact

🎙️ Tejas Bhakta, Founder & CEO, Morph

01:30 Fast Apply + WarpGrep
02:26 Context fills up around 100k
02:38 Keep the main model context clean
03:31 “You can’t scale human attention 100x”
07:28 Founders are missing how to value human attention
08:30 New model at 33,000 tokens/sec
10:08 Better, faster, and cheaper than the frontier

Pamir Ehsas spent years as outside counsel serving startups. 

He saw the same problem on repeat

Simple legal work took weeks. Pricing was opaque. Lawyers kept starting from scratch instead of using AI

So he built Arcline. 

AI generates the first draft. Elite lawyers from the best firms, and schools (Harvard, Oxford etc.) do the final revision

Up to 80% of the work gone. Same-day turnaround (Try getting that from a traditional law firm) 

50+ venture-backed startups already onboard when this ep was recorded

Most legal AI tries to replace the lawyer. Pamir replaced the busywork

02:09 “Fondo, but for legal” / AI-native legal for startups
03:12 Sold AI to law firms first; then pivoted to end users
04:52 Why Arcline uses experienced lawyers, not junior lawyers
05:43 Faster, higher-quality work — including complex matters
07:00 Big Law was slow; incentives were broken
07:36 Why going direct creates the data loop to improve AI
08:34 AI does the grunt work; lawyers create trust
11:08 What startups actually come to Arcline for
12:26 The long-term vision: legal advice at your fingertips

An employee used his corporate Gemini account to generate fake receipts for reimbursements

Not because he was lying. He just didn't have the real ones

That's when the CISO realized they needed Oximy

Ask an enterprise how many AI tools they use. They say 10. The real number is probably 40+

Most of the risk isn't malicious. People just want to get things done faster. So customer lists end up on free tools with no DPA

The first instinct is to block everything. But people bypass restrictions anyway

The real question: how do you say yes to AI without losing control?

That's Oximy. A control layer for enterprise AI adoption. Track usage, manage spend, enforce governance

🎙️ Naman Ambavi, Founder & CEO, Oximy on Fondo START pod

00:19 — What Oximy helps enterprises understand and manage
02:24 — From India to the Bay, and the thesis behind Oximy
03:29 — Track adoption, cost, and risk controls
03:52 — “They say they’re using not more than 10… the number goes roughly over 40”
04:22 — Compliance, retraining risk, and why oversight matters
05:10 — Most misuse starts with harmless intent
06:23 — The gap between Silicon Valley and corporate America
07:35 — How to say yes to AI without losing control
10:25 — Why the pain shows up most clearly at 1,000+ employees
12:59 — Why banning AI is the first instinct — and why it doesn’t last

Attorneys are trained to spot issues. That’s literally what law school teaches.

Show them your product, and the first thing they’ll say is: “the margin is off on this.”

Every hour they spend learning software is an hour they’re not billing.

Raffi Isanians knows that because he lived it.
Kirkland. Gunderson. Years inside private equity and venture work.

That’s why Mage Legal has a simple standard: if a lawyer opens the product with no instructions and can’t figure it out, "we’re failing"

Comprehensive AI coverage across the entire data room:
red and yellow flags, diligence memos, disclosure schedules, redline comparison. 1,500 documents in tens of minutes. 

All async.

05:15 - Puts a TOS into ChatGPT 3.5, comes back 85-90% there

06:20 - Every hour learning a tool is an hour not billed

08:37 - The product worked, nobody wanted it

09:00 - YC partner on the two-year window

09:53 - AI is an F1 engine in a world of bicycles

13:00 - Clients are pushing AI adoption, not the lawyers

15:19 - The goal is zero behavior change

17:00 - 1,500 documents, diligence memos, tens of minutes

18:22 - One good associate now does the work of six

30:56 - Engineers simplify, lawyers complicate

33:00 - 11PM, picture of his daughter, back to work

40:34 - Simple enough to use with zero instructions

The internet was the training set for intelligence

Nobody has built the equivalent for the physical world

Previously, Lucas Ngoo co-founded Carousell, scaled it past $1B 

Now at Cortex AI he's collecting the data robotics labs need to train foundation models

Cameras, VR headsets, glasses on factory workers, retail workers, everyday people. Recording real-world manipulation work (Maybe tens of millions, even hundreds of millions of hours)

Not building the robot. Not training the model. Collecting what goes in

2026: scale data in a big way 
2027+: start rolling robots out, 90% teleoperation, 10% autonomy

While humans step in, the system keeps learning

That's the flywheel toward full autonomy

01:38 Cortex as the data layer for general-purpose robotics
02:19 Why text data doesn’t transfer cleanly to the real world
02:51 Recording day-to-day human manipulation work
03:23 “Tens of millions” to “hundreds of millions of hours” of data
06:37 Cortex’s role: “We are really the data piece”
07:28 When humanoids become part of everyday life
08:00 2026 as the year data scales
08:13 90% teleoperation, 10% autonomy
08:37 Data flywheel toward full autonomy
08:55 Synthetic data + real-world data reinforcing each other

Some hotel software is still DOS-based. Sometimes pen and paper (that's why guests are waiting 45 mins to get towels)

Gavin's dad has worked at Marriott for the last nine years. When he showed Gavin the old software, that was the spark

Lance builds AI agents that answer calls, handle back office operations, run sales workflows & more

Started with voice, got inside the hotels, and realized how much more they could automate

With coding agents one engineer acts like five

Big contracts need custom solutions. Now they can deliver

🎙️ Gavin Brennen, Co-Founder, Lance (YC W26) on Fondo START pod

01:02 The reality of hotel software
01:53 The insight that sparked the company
02:01 A better way to handle guest requests
02:46 Why Lance had to build desktop agents
03:08 The centralized dashboard layer
04:00 Why hotel problems are highly custom
04:32 One engineer can now act like five
05:51 “Go all in”