I’ve loved investing since I was 13, when I bought my first stock, Apple, and realized you could actually own a piece of a company and participate in the upside.

In high school, I became one of the youngest interns at Morgan Stanley, where I studied under a financial advisor and learned how capital really moves.

Later, while working in strategy consulting and at a startup, I began angel investing on the side and quickly saw the power of getting in early and backing founders before the rest of the market catches on.

I have run a $25M fund and grown my personal portfolio to over 100+ companies including SpaceX, Anduril, Beehiiv, Kraken, and more. 

I now lead a frontier tech syndicate with a focus on space, robotics, and AI.

SpaceX wants to IPO at $1.5 trillion. OpenAI wants $1 trillion.

Meanwhile, 2025's biggest IPOs are already underwater.

Figma hit $115 per share on day one. Today it's $29. That's a 75% collapse in 6 months.

And Figma isn't alone.

Klarna: $40 IPO → $28 today
StubHub: $23.50 IPO → $15 today
Navan: $25 IPO → $15 today
eToro: $52 IPO → $31 today
Chime: $27 IPO → $26 today

Everyone blames the "IPO window."

VCs say timing was off.
Founders say the market turned.
Bankers say conditions shifted.

Nobody wants to say the obvious thing:

These companies went public at prices set by private investors who needed an exit, not prices public investors would actually pay.

In private markets, the next round sets the price.
In public markets, there is no next round.

No friendly markup. No bridge financing. Just millions of shareholders pricing your business every single day.

SpaceX has done 4 tender offers in 18 months. Each one doubled the valuation. Each one moved less than 1% of the company.

That's not price discovery. That's price manufacturing.

OpenAI is still losing money.
Anthropic just hit $350 billion.

The IPO window was never the problem.

The problem is the gap between what private investors agreed to pretend companies were worth and what public investors are willing to pay.

That gap is about to get tested at a scale we've never seen.

Elana Gold

👋 Hi, I'm Elana Gold! Thanks for checking out my profile.

Here's what you need to know about me:

👉 Partner at Red Beard Ventures (RBV), a pioneering Web3 fund with over $25M in capital, investing in blockchain consumer apps, infrastructure, gaming, metaverse, and DeFi, both in equity and tokens.
👉 I lead the RBV Syndicate, which invests in startups at any stage and sector, with over 3,900 LPs, 210+ company investments, and over $50M deployed.
👉 An advocate for women in investing, I founded The Girls Table, a platform (for men and women) boosting entrepreneurs, creators, and investors through my podcast, Newsletter, and IRL events. My efforts have brought over 20,000 individuals into the investing space.
👉 My work has earned speaking roles at events like VeeCon, NFT NYC, NFT LA, and Consensus
👉 I have received Forbes 30 Under 30 and Business Insider's Rising Stars of VCs.
👉 As a CPA in New York, I combine financial acumen with a passion for innovative tech and community building.

If you want to learn more about investment opportunities, let's connect!

Hit 'connect' and send me a DM to kick off our conversation!

Twitter: www.twitter.com/itselanagold
Instagram: www.instagram.com/itselanagold
TikTok: www.tiktok.com/@tradingfemale

Partner at Red Beard Ventures | VC & Angel Investor

Sam Altman says something that makes founders uncomfortable...

Most startups don't fail. 
They quit too early to ever work.

In YC office hours and interviews, Altman keeps pointing to the same mistake.

Founders are usually right about what they want to build.
They are almost always wrong about how fast it should start working.

What founders expect:
- Momentum early
- Clear validation
- Signals that feel obvious

What actually happens:
- Progress is slow
- Feedback is messy
- Nothing compounds on the timeline they imagined

That gap is where most startups disappear.

Altman has explained that early signals are some of the most misleading data points in a company's life. Early users are not representative. Early growth is noisy. Early feedback often points in the wrong direction.

Founders see this and assume something is broken.

Most of the time, it is not broken.
It is unfinished.

The common failure mode is not being wrong.
It is confusing slowness with failure.

Altman is not arguing for blind persistence or ignoring reality.

He is warning that many founders quit or pivot before the timeline they expected was ever realistic.

Early slowness is normal.
Confusion is normal.
Feeling behind is normal.

What is rare is staying long enough for the work to compound.

That is why most startups do not fail in dramatic explosions.

They fade out quietly when founders confuse
"this is taking longer than I expected"
with
"this will never work."

Those two things are not the same.

And confusing them is one of the most expensive mistakes a founder can make.

Why is nobody talking about this AI company right now??

Last week, Skild AI raised $1.4B at a ~$14B valuation.

That is one of the largest AI rounds ever raised for a company this young.
And it happened quietly.

Skild isn't building a robot. They're building the brain every robot could eventually run on.

They call it the Skild Brain.

One foundation model. Many bodies.
Humanoids. Quadrupeds. Robotic arms. Mobile robots.

The goal is a single AI system that can control entirely different machines without being retrained for each one.

That is the core unsolved problem in robotics.

Almost all robot AI today is vertical: One model. One robot. One task.

Skild is going horizontal.

Their model adapts in real time to robots it has never seen before. If hardware changes, sensors fail, or the environment shifts, the system adjusts instead of breaking.

That kind of generalization is common in language models.
It has been nearly impossible in the physical world.

The reason investors took this seriously is who's behind it.

Deepak Pathak (CEO) is one of the researchers behind curiosity-driven learning, a breakthrough that allows machines to learn through exploration instead of being explicitly programmed. PhD from Berkeley. Former Meta FAIR. Former CMU faculty.

Abhinav Gupta (President) earned his PhD from the University of Maryland, is a tenured CMU professor, a founding member of FAIR Robotics, and one of the most cited researchers in the field.

This is not a pair of founders pivoting into robotics.
These are two people who have been trying to solve the same foundational problem for most of their careers.

The round was led by SoftBank, with participation from NVIDIA (via NVentures), Bezos Expeditions, Samsung, LG, Sequoia Capital, Lightspeed, Coatue, and Felicis.

Seven months ago, Skild was valued around $4.5B.
Now it's $14B.

They're not selling a robot today.
They're trying to solve the harder problem first: intelligence that actually understands the physical world.

As Stephanie Zhan from Sequoia put it,
"A GPT-3 moment is coming to the world of robotics."

This is a bet on whether general-purpose robotics is finally possible.

And bets like that rarely stay quiet for long.

Everyone said she was too late to self-driving.

She just raised $1B to prove them wrong.

Raquel Urtasun grew up in Pamplona, Spain. 

She left Spain for a PhD, then built a career across MIT, Berkeley, and Toronto - becoming a full professor and founding member of the Vector Institute alongside Geoffrey Hinton.

Then Uber recruited her to lead their self-driving research as Chief Scientist. 

But she spent years watching the industry burn billions on the same broken approach: drive millions of real-world miles, collect data, repeat.

Slow. Expensive. Dangerous.

She knew there was a better way.

In 2021, she left Uber to prove it.

When she announced Waabi, people said she was "too late" to self-driving.

Her response: "If anyone said I wasn't capable, I set out to prove them wrong."

Waabi's bet: train AI in simulation first, not on roads.

They built Waabi World - a neural simulator so realistic their AI learns to drive before it ever touches a highway.

The result?

While competitors burned through cash:
- Embark went public at $5.2B, sold for $71M
- Aurora ($9B valuation) put human observers back in their trucks

Waabi hit Level 4 autonomy in four years. With a fraction of the capital.

Investors noticed.

This round: $750M Series C led by Khosla and G2, plus an additional Uber investment to deploy Waabi-powered robotaxis on the Uber network. 

Originally they set out for $500M, but demand pushed it higher. NVIDIA, Volvo, Bezos Expeditions, Geoffrey Hinton, and Fei-Fei Li all backed her.

- Waabi is already running commercial freight between Dallas and Houston. 
- Volvo signed a deal to put Waabi's tech in production trucks. 
- Uber Freight's former CEO joined as COO.

Raquel's goal: fully driverless trucks this year. Then robotaxis. Same brain. Same platform.

"Physical AI is the next big frontier," she said. "Five to 10 years from now, you're going to see robots everywhere."

This is the largest fundraise in Canadian history… and she's just getting started.

Three years ago this week, ElevenLabs raised a ~$2M pre seed.

Today, the company is reportedly in talks to raise at around an $11B valuation.

If that initial round was done anywhere near a $10 to $12M valuation, which was typical for top tier European pre seeds in 2022, that implies close to a 1,000x outcome in roughly three years.

Even if you cut that in half to account for dilution and conservatism, you are still looking at a ~500x return in THREE YEARS.

That is not normal venture math.

ElevenLabs was founded by Piotr Dabkowski, a former Google machine learning engineer, and Mati Staniszewski, who previously worked at Palantir.

The company did not start with enterprise contracts or a massive vision deck.

It started with a very specific frustration.

Dabkowski grew up in Poland watching poorly dubbed movies - a single voice actor reading all the dialogue in a flat monotone over the original audio. Even as generative AI improved, synthetic voice still felt robotic and emotionally hollow.

So they focused obsessively on one thing: making AI voices sound human.

Not just readable. Expressive. Emotional. Believable.

That focus turned ElevenLabs into core voice infrastructure for creators and developers, powering narration, audiobooks, character voices, localization, dubbing, and real-time speech generation across 32 languages.

What began with creators quickly spread to enterprise. Major publishers like HarperCollins, media companies like The Washington Post and TIME, and gaming studios like Paradox Interactive all use ElevenLabs.

The company also scaled with real capital efficiency.

ElevenLabs has raised under $300M total to date, which means early investors were not heavily diluted while the business compounded at an extraordinary rate.

For early backers like Credo Ventures and Connect Ventures, this is a fund defining outcome.

This is the power law in action.

You do not need many of these.

You just need one.

The 6-month MVP is dead. I just built one in a few hours.

Here's what I did…

I had an idea stuck in my head for months. There's no easy way to build a company data room. Most founders just use a messy Google Drive link - no clean interface, no tracking, no way to know if investors even opened it.

So I opened Lovable and started building.

I called it DataRoomIQ.

It does what founders actually need:
- Starts with ready-made templates from Pre-Seed to Series A
- Creates a professional front-end for your raise
- Enables you to see exactly who viewed your deck, when, and for how long
- Lets investors ask questions - AI answers instantly from your docs

A few hours later, I had an MVP.

A week later, I have a working product with pricing, templates, and a live example data room.

No developer. No coding experience. No six-month search for a technical co-founder.

I just built it.

This isn't just for solo founders anymore.

Product teams at Klarna, Uber, and Deutsche Telekom are already using Lovable.

The old way at big companies:
- Whiteboard and design for hours
- Bring in engineering weeks later
- Build a prototype over months
- Finally test with customers

The new way: 
- design and build on Lovable. 
- test it with customers instantly. 
- iterate live.

Pick your features. Build the prototype. Get real feedback. All during the same session.

The gap between idea and product used to be months.

Now it's hours.

Founders can validate before they fundraise. Product teams can test before they commit engineering resources.

I’m now sharing DataRoomIQ with founders and seeing them actually use it to replace messy folders with structured, investor-ready rooms.

The barrier to building used to be code.

Now it's just whether your idea is worth building.

What idea have you been sitting on that you could test this week?
#lovablepartner

Brand partnership • Partner at Red Beard Ventures | VC & Angel Investor

I'm terrible at delegating. 

But I'm drowning…

New baby. Meetings. Portfolio companies. Deal flow. Research. Fundraising. Content. Networking. Emails.

And somehow still need to fit in eating, sleeping, seeing friends and family, and working out.

The list never stops growing.

So, I finally decided that I needed to make a change.

I hired a VA through Wishup a few months ago. Honestly? I should have done it years ago.

Here are some of the things he handles now:

- Scheduling and calendar management
- Inbox triage and first-pass responses
- CRM enrichment
- Content image creation
- Weekly startup and founder research
- Newsletter formatting
- Travel booking and logistics

None of this is glamorous work. But it was eating 10+ hours of my week.

If you're a founder or investor still trying to do everything yourself - I get it. I was you.

But the hours don't come back. And the list never gets shorter on its own.

Sometimes you need to get help along the way. Still learning that lesson myself.
#WishupPartner

80% of AI startups will pivot, consolidate, or die in 2026.

Not because AI is slowing down.

Because the market is overcrowded, the platforms are moving too fast, and the easy categories are already getting absorbed.

We've seen this before.

SPACs in 2020. Crypto and NFTs in 2021. Now AI.

The pattern never changes:
- Everyone rushes in
- Capital floods the market
- 1,000 companies chase the same TAM
- Reality hits like a brick wall

AI isn't different. It's just faster.

In 2024, generative AI startups raised $56B across nearly 900 deals. In 2025, AI companies raised roughly $226B, nearly half of all venture funding.

There aren't a handful of AI startups. There are thousands.

And they're not competing for attention. They're competing to exist.

Here's what makes AI especially brutal:

Your biggest risk is not another startup, it is the AI labs that can ship one update and turn your entire product into a feature overnight.

The survivors will share 3 traits:

1. They own the workflow, not just the AI
2. They have distribution before AI becomes the wedge
3. They're default alive on today's revenue

One founder put it simply: "If the AI stopped improving tomorrow, our business would still work."

That's the bar.

The gold rush phase is ending. Now it's about real companies.

I break down the full analysis in this week's Cap Table 👇

I'm embarrassed to say this, but the best deal I ever lost wasn't because of price. Or competition. Or timing.

I forgot to follow up.

Great call. Loved the founder. Told my partner we should move fast.

Then it got buried. Other deals came in. The note I made sat in a spreadsheet I didn't open for two weeks.

By the time I reached back out, they'd already signed a term sheet and the round was closed.

The founder wasn't upset. They just moved on.

But I think about that one a lot.

Not because it was life-changing money - I don't know what that company will become. But because the miss had nothing to do with judgment.

It was a systems failure.

I don’t think I’m the first investor to do this. Other funds I’ve spoken with also struggle with organization. There are so many deals in the pipeline and conversations scattered across emails, texts, Telegram, WhatsApp groups.

After that miss, I realized we needed to take our CRM seriously.

Last year our fund switched to Attio. 

Now, the first thing I do after a founder call, before I even close Zoom, is open the deal in Attio and decide what happens next.

If we’re moving forward, I log the notes, tag it hot, and schedule the follow-up. If we’re passing, I mark it, send the founder a note, and move on. And if I do nothing, Attio doesn’t let me forget.

Attio automatically tracks the last real interaction on every deal. Calls, emails, notes, and meetings all count as activity.

If nothing new happens, the clock keeps running in the background. A few days later, the deal resurfaces until I either move it forward or kill it.

I learned the hard way: you either have a system, or you have regrets.
#Attiopartner

Early employees were sold a simple deal.

Join the startup.
Take less cash.
Give it 5 to 7 years.
Liquidity comes at the exit.

That deal no longer holds.

Take SpaceX.

World changing company. Tens of billions in value.

But they are still private after two decades.

Early employees didn’t lose conviction. They just ran out of patience.

So SpaceX has repeatedly allowed employee liquidity through secondary sales and tender offers, not as a bearish signal, but as pressure relief.

That’s the new normal.

The best companies are staying private longer.
Exits are slower.
Paper gains stopped feeling real.

Liquidity didn’t disappear. It moved to secondaries.

I break down why this matters for employees, angels, and investors in this week’s Cap Table 👇

Welcome back to The Cap Table Newsletter! This bi-weekly newsletter shares key insights on angel investing, start-ups, and investment opportunities. T

Open article: Why Secondaries Are So Attractive Right Now by Elana Gold • 5 min read

Why Secondaries Are So Attractive Right Now

Venture is back.

But not in the way most people think.

2025 looked like a comeback year on the surface: $469B deployed globally, the highest level since 2022. Headlines screamed recovery.

Underneath, the market quietly rewired itself.

Capital concentrated harder than ever.

Total deal count fell 17%, but mega rounds exploded. Fewer companies raised. The same companies raised again and again. 65% of all global venture dollars went into just 738 rounds.

Bigger checks. Fewer winners.

And almost all of it flowed to one place.

US startups captured ~70% of global funding. Europe and Asia grew modestly, but neither benefited meaningfully from the AI surge.

AI didn't just win. It absorbed the market.

AI companies raised $226B in 2025. Six rounds alone accounted for $111B.

This is not diversification. This is one macro bet.

If the winner-takes-most assumption holds, returns will be historic.

If it breaks, there are very few sectors left to cushion the fall.

Meanwhile, robotics quietly crossed a threshold.

$40.7B went into physical AI, the highest on record. Humanoids, autonomous systems, drones, foundation models.

Valuations moved faster than fundamentals. Some companies hit massive price tags without revenue or commercial proof.

And the smartest capital leaned into the concentration.

Top performing VC firms clustered almost all activity around AI agents, copilots, and infrastructure. A sharp departure from classic venture theory.

So what does this mean heading into 2026?

Venture did not broadly recover. It refocused.

Capital is available, but only if you fit a very specific narrative.
Scale matters more than ever.
Proof matters later than it probably should.

Are you betting on the concentration thesis? Or looking for what got left behind?

He arrived in America at 16 with $500. His first night was in a $3 YMCA room. His first job was washing dishes for $1.20/hour.

Now he owns an NFL team and is worth $15 billion.

Shahid Khan grew up in Lahore, Pakistan. Middle-class family. His dad ran a small construction company.

In 1967, he flew to Illinois to study engineering. No connections. No safety net.

He graduated, got a job at Flex-N-Gate, a small auto parts company, and became engineering director.

But he saw a problem. Their bumpers were made by welding 15 separate pieces together. Inefficient. Heavy. Expensive.

So he invented a one-piece stamped bumper. Lighter. Stronger. Cheaper.

In 1978, he left to start his own company: Bumper Works.

One week later, Flex-N-Gate sued him.

He couldn't afford a real lawyer. So he ran his own defense - studying in the law library at night, running production during the day.

He fought for two years. Won.

Then bought the company that sued him.

GM noticed his bumper design. Loved it. Wanted it for their entire vehicle line.

But they told him Flex-N-Gate was too small. They'd hand his design to bigger suppliers instead.

His innovation was about to put him out of business.

GM offered one lifeline: "Here's a name and number of a guy in Japan. Knock yourself out."

That call led to Isuzu. Then Mazda. Then Toyota.

By 1989, Flex-N-Gate was the sole bumper supplier for every Toyota vehicle in the US.

Today:
- 76 plants worldwide
- 27,000 employees
- $9B+ in annual revenue

In 2012, Khan bought the Jacksonville Jaguars for $770M. First ethnic minority owner in NFL history.

Net worth: $15 billion.

Khan put it simply: "You can do anything you want to do. You have to work hard, you have to create your own luck, and you have to have some luck also. But here, it's possible."

If your calendar is full and revenue is flat, this is probably why…

You're spending time on people who were never going to buy.

The bottleneck isn't effort. It's timing and signal.

Most founders treat every lead the same. But real buyers don't announce themselves. They research quietly, compare options, loop in teammates, and by the time they talk to you, they've already decided.

Here's what I'd do differently today.

Step 1: Stop guessing who's in market

Not "enterprise companies." Be specific.

"Series C+ fintechs with 200+ employees hiring a VP of Engineering."

Apollo AI Assistant prompt:
"Build my TAM using ICP filters and score accounts Bronze to Platinum."

You get a ranked list and clear reasoning for why each account fits.

Step 2: Identify the right people, not just accounts

Enterprise deals don't close with one person.

You need the people with budget, influence, and urgency.

Prompt:
"Find decision makers at these accounts and enrich their contact information."

No manual research. 

Step 3: Prioritize accounts showing real intent

This is where most founders waste time.

Pricing page visits. Product deep dives. Relevant role changes.

Prompt:
"Show me accounts visiting my pricing page or engaging with high intent pages."

Step 4: Let workflows handle the follow-up

When intent shows up, speed matters.

Prompt:
"Create a workflow that moves high intent accounts into an active sequence."

No manual follow-up is needed. 

This is why I partnered with Apollo.

The AI Assistant doesn't help you send more messages. It helps you stop wasting time on the wrong ones.

For founders, that's the difference between being busy and building revenue.

#ApolloAIAssistant #ApolloPartner

He dropped out of school at 16. Couldn't get a job in Canada. Built a snowboard shop that flopped.

Then accidentally created a $200B company.

Tobias Lütke had ADHD and dyslexia. School was a nightmare.

But at 6, he got a Schneider computer. By 11, he was rewriting the code of video games he played.

At 16, he dropped out after 10th grade and entered an apprenticeship at Carl-Benz-School in Koblenz, Germany. By 17, he was programming for a Siemens subsidiary.

Then he met a Canadian woman named Fiona on a snowboarding trip.

He moved to Ottawa in 2003 to be with her.

One problem: he couldn't get a work permit. No Canadian employer would hire a German dropout with no degree.

His only legal option? Start a business.

So he partnered with Scott Lake to launch Snowdevil, an online snowboard shop.

The timing was terrible. The dot-com crash had just happened. Everyone thought e-commerce was dead.

And the software to run an online store? Awful.

So Tobi built his own. Using Ruby on Rails. In two months.

The snowboard shop flopped. But the software worked.

Other merchants started asking to use it. In 2006, Snowdevil became Shopify.

For years, almost no one noticed.

By 2007, they nearly ran out of money. Angel investor John Phillips put in $250K to keep them alive.

By 2013: 80,000 customers, 300 employees. Still relatively unknown.

Then 2014 hit. Globe and Mail named Tobi "CEO of the Year."

In 2015, Shopify went public at $17/share, raising $131M.

Today:
- 5+ million merchants worldwide
- $9B in 2024 revenue
- Tobi's net worth: $13.4B

Tobi just built a tool to solve his own problem. Then waited.

What looks like vision in hindsight was really patience, timing, and a background optimized for building.

Everyone wants to invest in SpaceX.
Almost no one realizes they actually can.

Ask anyone what startup they'd invest in today.

SpaceX.
Stripe.
Anthropic.
Anduril.
OpenAI.

Category-defining companies. They seem like obvious winners.

Most people assume access is impossible.

It's not.

Secondary markets now let individuals buy shares in many of the most well-known private companies.

Not by getting into the next round.
By buying existing shares from early employees, angels, or funds looking for liquidity.

This changes the entire question.

It's no longer just:
"What's the best startup?"

It becomes:
"What's the right company, at the right price, through the right access point?"

But secondaries come with real risks.

Fraud is rampant. Fake shares. Forged cap table documents. Sellers who don't actually own what they're selling.

Pricing is opaque. You're often buying at a markup with no visibility into the last actual round.

Liquidity is uncertain. You might wait years for an exit or never get one.

And some companies actively block transfers, making shares nearly impossible to move.

The best investments are rarely the most famous names at the highest prices.

They're the ones you can actually access, properly verify, and buy at a price that still leaves upside.

So I'm curious - if you could buy into any private company today through secondaries:

Would you take a small slice of a SpaceX or Anduril?

Or would you go bigger on something earlier, before everyone else catches on?

Drop the company and your reasoning below 👇

Last year I was taking 12+ calls a day.

But... I was drowning.

Hearing pitches, spending time with portfolio companies, growing my network, helping to fundraise, and running the syndicate.

I would eat during calls. Run to the bathroom if I was lucky enough that one ended early.

All while trying to listen, think, and take notes at the same time.

I told myself that taking notes helped me pay attention.
In reality, I was half present, trying to type and ask questions.

A week later, during our investment call meeting, my partner would ask follow-up questions:

- What valuation was their last round at?
- What did they say about last year's product launch?
- How much revenue were they projecting for this quarter?

I'd scroll through my notes, hoping they were right.
Trying to decode half-sentences I barely remembered writing.
Realizing my notes were not clear but they were my only source of truth.

And that was the problem.

One day, we decided to try an AI notetaker.

I was skeptical. I assumed outsourcing notes would make me less engaged.

What actually happened was the opposite.

I stopped typing.
I listened more closely.
I asked better questions.

And instead of relying on my memory or messy bullet points, I could go back and ask real questions later and get real answers.

That's when Otter.ai started to feel less like a productivity tool and more like infrastructure.

Now every call is searchable. Every detail is saved. And when my partner asks about a conversation from three weeks ago, I pull up the exact quote in seconds.

I still take meetings all day.

I just stopped pretending my brain and sloppy notes were enough.
#OtterPartner

In 2022, Sequoia called an emergency meeting with every founder in their portfolio.

The market was crashing. Late-stage investors were pulling back. The era of free money was over.

They opened with a quote from Ayrton Senna:

"You cannot overtake 15 cars in sunny weather... but you can when it's raining."

Sequoia called it a "crucible moment."

No quick bounce back. No policy rescue. The tools that saved everyone in 2020 had already been spent.

Most founders did what felt safe. Cut costs. Lay low. Wait it out.

Sequoia's message was the opposite: this is when you pull ahead.

They told founders to plan their cuts now. Projects, R&D, marketing, headcount. Not to act immediately, but to be ready to move fast if things got worse.

"When you have just six months of cash left, focus becomes incredible. Get that focus now, regardless of how much you have in the bank."

But here's what most people missed.

The founders who win in downturns aren't the ones who hustle harder. They're the ones who designed their finances to outlast everyone else.

Endurance isn't a personality trait. It's a financial strategy.

Staying alive longer does three things:

1. Preserves optionality. Desperate companies can't wait for the right deal, the right hire, the right window.

2. Keeps leverage on your side. When you need money, investors smell it. When you don't, you negotiate from strength.

3. Lets learning compound. Most startups don't fail because the idea was bad. They run out of time before the work starts working.

The founders who survive aren't always the smartest or the fastest.

They're the ones who stay solvent long enough for everyone else to quit.

That's not motivational advice. That's one of the most underrated advantages in startups.

What's the most counter-intuitive move you've made to extend your runway?

Adapting to Endure

Can we just take a moment to look at this??

These are the top 10 Most Valuable Private Companies in the U.S.

SpaceX: $854B
OpenAI: $500B
Anthropic: $354B
xAI: $250B
Databricks: $139B
Stripe: $110B
Anduril: $79B
Ramp: $32B
Ripple: $20B
Rippling: $20B

Combined, that's $2.36 trillion in private company value.

More than the GDP of Italy.

SpaceX is worth $854 billion.

That would make it the 8th most valuable public company in America - ahead of Walmart, JPMorgan, and Visa.

But it's not public.

OpenAI is worth $500 billion. Anthropic, $354 billion. xAI, $250 billion.

These are valuations that used to take decades to reach, and only after going public.

- Amazon hit $500B in 2017. It was founded in 1994 and went public in 1997.
- OpenAI hit $500B in 2025. It was founded in 2015 and has never gone public.

The IPO used to be the finish line.

Now it's just an option.

Do any of these companies ever need to go public at all?

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.css-6ytdur{background-image:linear-gradient(284deg, rgba(39, 43, 72, 1) 0%, rgba(33, 47, 183, 1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Elana Gold's Linkedin Analytics

Elana Gold

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Elana Gold's LinkedIn Performance

Elana Gold's Best Posts .css-1emoo84{font-size:var(--chakra-fontSizes-md);font-weight:400;color:var(--chakra-colors-gray-600);}(last 30 days)

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Elana Gold's Linkedin Analytics

Performance ScoreElana Gold's LinkedIn Performance

Elana Gold's Best Posts (last 30 days)