The False-Positive Incumbent

Opt-Out Options for Targeted Ads in NBCUniversal's Privacy Policy Burnt Uses AI to Streamline Food Supply Chains, Raises $3.8 Million in Seed Funding The False-Positive Incumbent #SpotifyStartup, #TargetedAds, #PrivacyPolicy, #AI, #FoodSupplyChain, #SeedFunding, #PodcastEpisode

Episode Transcript

(00:00):
Welcome to Innovation Pulse, your quick no-nonsense update covering the latest in startups and

(00:09):
entrepreneurship news.
First, we will cover the latest news.
NVIDIA boosts its AI capabilities with a $900 million acquisition of Enfabrica and Burnt
revolutionizes the US food supply chain, securing $3.8 million in seed funding.
After this, we'll dive deep into the phenomenon of false positive incumbents in the AI startup

(00:33):
scene with insights from Yakov Lasker.
NVIDIA has recently made a significant move by acquiring talent and technology from Enfabrica,
an AI hardware startup.
This deal, valued at over $900 million and involving cash and stock, brings Enfabrica's
CEO, Roshan Sankar and other key employees to NVIDIA.

(00:56):
Enfabrica, founded in 2019, offers groundbreaking technology that can connect over 100,000 GPUs,
enabling NVIDIA to create integrated systems that effectively function as a single computer.
This capability is crucial for enhancing the performance of large GPU clusters, which are
essential for training large language models and providing AI services in the cloud.

(01:21):
The acquisition mirrors strategies by tech giants like Metta and Google, who have been
investing in AI talent through similar deals.
NVIDIA's commitment to expanding its capabilities in AI is further evidenced by its recent investments
in other companies, such as its $5 billion stake in Intel and $700 million in UK-based

(01:44):
end scale.
Enfabrica's technology, which allows for the seamless integration of massive GPU networks,
provides NVIDIA with a unique edge in the competitive AI hardware landscape.
Burnt, a Y-combinator-backed startup, aims to revolutionize the United States food supply

(02:06):
chain industry using AI agents to automate back-office tasks traditionally managed by
humans.
The startup has secured $3,800,000 in seed funding led by Pennyjar Capital, Steph Curry's
venture firm, with additional support from Scribble Ventures and other investors.

(02:26):
Co-founder and CEO Joseph Jacob, who has deep roots in the seafood industry, recognized
inefficiencies in traditional supply chain management while managing seafood imports
in the United States.
Typically, food distributors handle orders via multiple channels like emails and phone
calls requiring manual entry into outdated systems, which is time-consuming and error-prone.

(02:51):
Burnt's first AI agent, Ozai, automates the order entry process, handling up to 80% of
workflows still reliant on legacy systems.
Since its launch, Burnt has processed over $10 million in monthly orders for various
distributors and is seeing steady revenue growth.
By leveraging AI to enhance existing systems rather than replace them, Burnt offers a unique

(03:16):
solution that addresses long-standing challenges in the industry, especially for small and
mid-sized players.
And now, pivot our discussion towards the main entrepreneurship topic.

(04:06):
Oh yeah, it's everywhere.
The headlines, the funding announcements.
Right.
Well, here's the thing.
What if I told you that a bunch of these companies that everyone thinks are unstoppable
incumbents aren't actually incumbents at all?
They're just really good at cosplaying as one.
Wait, cosplaying?
You're telling me these billion-dollar companies are basically wearing a costume?

(04:30):
That's exactly what I'm saying.
And once you see it, you can't unsee it.
We're calling them false positive incumbents.
Okay, you've got my attention.
But help me understand this, because from the outside, these companies look like they're
crushing it.
They've got the revenue, the enterprise contracts, the massive funding rounds.
They do.

(04:50):
And that's what makes this so tricky.
See, traditionally, when you saw a company hitting those metrics, you could be pretty
confident they'd built something sustainable.
But AI has completely scrambled the signals.
These companies have all the appearance of market leaders, but underneath, they're
operating with fundamentally early-stage risk profiles.

(05:12):
So it's like they've got the suit and tie, but they're still figuring out how to do
the job?
Perfect analogy.
And here's why this matters right now.
Enterprise buyers are under this immense pressure to adopt AI.
Every board meeting, every strategy session, it's what's our AI strategy?
So they're making purchasing decisions based on FOMO, not proven ROI.

(05:36):
Oh, that's interesting.
So the demand itself might be artificial?
Exactly.
Think about it this way.
If your company is buying an AI tool because you're afraid of being left behind, not because
you've calculated the actual return, that's a very different customer than someone who's
bought into a solution because it genuinely transforms their workflow.

(05:58):
And those FOMO customers probably aren't going to stick around long term.
Bingo.
Which brings us to what I love calling stripper pole revenue ramps.
I'm sorry, what?
Stripper pole revenue, you know, straight up and down.
Growth that would have taken traditional incumbents decades now happens in under 12 months.

(06:19):
But here's the catch.
A lot of that speed comes from heavy discounting, subsidized customer acquisition, or land and
expand strategies where the land is basically free.
So they're buying growth.
They're renting customers.
And this is where it gets really interesting.
Because we've seen this movie before.
Let me take you back to 2010.

(06:41):
Do you remember Groupon?
Oh man, Groupon.
They were everywhere.
Fastest growing startup ever at that point.
Peak valuation of 1.2 billion.
Everyone thought they had these incredible network effects.
Customers finding businesses, businesses getting customers, looked unstoppable.
But they weren't.

(07:02):
Not even close.
When competition intensified and capital became scarce, the whole thing fell apart.
Turns out their network effects were just expensive customer bribes.
Merchants weren't loyal because users weren't loyal.
They were just chasing the next discount.
Okay, but hold on.

(07:22):
Building Devil's Advocate here, couldn't you have said the same thing about Amazon
back in the day?
They lost money for years.
Great question.
And this is the crucial distinction.
Amazon was subsidizing growth while building real infrastructure.
Warehouses, logistics networks, relationships.

(07:43):
Netflix burned cash to create a content moat.
The key is, what are you building during that subsidized phase?
So it's not about whether you're profitable now.
It's about whether you're building something defensible.
Exactly.
And this is where the false positive incumbents fail.
They're optimizing for the next fundraise, not for fundamentals.

(08:04):
Let me give you what I call the siege test.
The siege test?
What happens if this company couldn't raise money for 24 months?
Real incumbents adapt and survive.
They get stronger under pressure because their network effects become more valuable.
Their data moats widen, switching costs increase.
But false positive incumbents, they collapse.

(08:27):
Because without the subsidies.
Strip away the subsidies and you realize their network effects are just expensive user bribes.
Their data advantages are cash-burning data collection programs.
Their switching costs are actually switching incentives.
Customers only stayed for the discounts.
Ooh, that's cold.

(08:48):
But I like it.
So you're basically saying, if the life support gets unplugged, who actually survives?
Right.
And here's another test.
Customer behavior.
Would customers pay more for this product over time?
Real incumbents build increasing willingness to pay.
Think about how people pay more for Salesforce as they get more embedded.

(09:11):
False positives depend on maintaining artificially low pricing.
Wait, so some of these AI companies are essentially selling $1.50 worth of product
for one?
You got it.
They're paying open AI or Anthropic more for the underlying model than they can sustainably
charge for their wrapper product.
And the more customers you onboard, the worse your unit economics get.

(09:33):
This is fascinating because, okay, so we've talked about the false positives.
But historically, being first hasn't always meant winning, right?
Oh man, the second mover advantage is so underrated.
Facebook wasn't the first social network, but they recognized that real identity and
college networks created stronger engagement than pseudonymous platforms like Friendster.

(09:57):
Google wasn't the first search engine.
It was just the best, by a lot.
And that quality advantage turned into a distribution advantage over time.
Exactly.
And here's a more recent one that I love.
Remember Brex?
They raised $300 million, hit a $2.6 billion valuation, dominated startup spending.

(10:18):
Everyone thought they'd won the corporate card game.
But then Ramp came along.
Ramp focused on expense management and cost reduction instead of rewards.
When funding dried up in 2022-2023, all those high-burn startups that loved Brex's model,
they vanished.
Meanwhile, Ramp's value proposition, helping companies save money, got stronger as everyone

(10:44):
tightened their belts.
So Brex was optimized for a world of cheap capital, and Ramp was optimized for reality.
Perfect way to put it.
And this is what founders need to understand right now.
The AI companies that are winning the fundraising game today might not be the ones standing
in three years.
Okay.

(11:04):
So if you're a founder listening to this, what should you actually be doing?
Because it sounds like the whole market is playing this short-term game.
Great question.
Look, I get it.
Fundraising matters.
We're VCs, we wouldn't have jobs otherwise.
But here's the thing.
You might have to play the short-term virality game to some extent, but don't believe it's

(11:25):
your ticket to success.
So how do you balance that?
First, retention curves beat AI tourism every time.
Focus on engagement and retention metrics, not just ARR.
Ask yourself, are customers integrating your product into daily workflows?
Or are they just experimenting because their CEO told them to?

(11:47):
That's a great distinction.
Real usage versus checkbox AI adoption.
Exactly.
And second, create a differentiable value proposition.
Most startups don't begin defensible, but the best ones build it in over time.
Start solving real workflow problems, not just showcasing AI capabilities.
But that takes longer to demonstrate, right?

(12:09):
Like solving a real problem deeply might not look as sexy in a pitch deck.
That's the trap.
Everyone wants the fast growth story.
But here's what you need to remember.
It's better to have fewer customers who genuinely value what you're building than many who
see you as interchangeable.
Okay, but I have to ask about the elephant in the room here.

(12:30):
What about open AI or Anthropic?
These companies burn capital at rates that would kill traditional startups in months.
They don't have clear paths to margin expansion.
How do they fit into your framework?
Oh, man, I knew you'd go there.
And you're right.
They don't pass the siege test with flying colors.
In a 24-month funding drought, they'd face existential pressure despite their war chests.

(12:56):
So why aren't they false positives?
Because they've transcended the normal rules.
Open AI has achieved something unprecedented.
Belief network effects so powerful that funding them has become a geopolitical imperative.
Not just a business decision.
When your product becomes infrastructure, that entire industries depend on.

(13:17):
Different economic supply.
So they're too big to fail.
Pretty much.
But here's the thing.
If you're building a company with potential to become a geopolitical imperative, definitely
come talk to us.
Ha, fair enough.
But for everyone else who's not building the next open AI.
For everyone else, the lesson is clear.

(13:37):
Look around your market.
Half the incumbents you're worried about are just well-funded startups with unsustainable
unit economics.
The other half are building real moats while everyone else chases fundraising metrics.
And the opportunity is in recognizing the difference.
Exactly.
Because here's what people don't realize.
First mover advantages are coming in AI.

(14:00):
If you have the poise to see this market for what it is, play the game just enough to survive
but optimize for the long run.
You can win.
I love that.
So the companies optimizing for today's fundraise might be vulnerable to competition tomorrow.
Or two years down the line.
The pattern is consistent through history.
First movers validate markets, but winners build sustainable advantages.

(14:24):
AI doesn't invalidate this.
It just makes it harder to see in the moment.
This has been incredible, Yaakov.
So for our listeners, here's the bottom line.
Next time you see a company hit 100 million ARR in record time, don't just be impressed.
Ask yourself, are they building a moat or renting customers?

(14:44):
Are they creating value or just capturing attention?
And remember, not all revenue is created equal.
The real test isn't how fast you grow.
It's whether you're still standing when the music stops.
Yaakov Lasker, thanks so much for breaking this down.
This is the kind of insight that changes how you see the whole landscape.
Thanks for having me, Donna.

(15:05):
This was fun.
That's it for today's Innovation Pulse.
Until next time, keep questioning the narratives.
That wraps up today's podcast.
We explored NVIDIA's strategic acquisition to bolster AI capabilities and delved into
the Yaakov Lasker's insights on sustainable growth in the AI startup landscape.

(15:29):
Don't forget to like, subscribe and share this episode with your friends and colleagues
so they can also stay updated on the latest news and gain powerful insights.
Stay tuned for more updates.