Best of LinkedIn: Venture Capital CW 17/ 18

00:00:00: provided by Thomas Allgaier and Frennus, based on the most relevant LinkedIn posts about venture capital from CW-Seventeen in eighteen.

00:00:07: Frenness supports general partners in identifying relevant limited partners across multiple sources researching connection strategies coordinating event attendance and running structured outreach campaigns that turn cold lists into scheduled conversations and committed capital.

00:00:21: you can find more info

00:00:23: And welcome to the deep dive, everyone.

00:00:25: If you are a professional operating anywhere in strategy M&A private equity or the VC space You're definitely in the right

00:00:33: place.

00:00:33: absolutely.

00:00:34: we keeping things smart and focused today no fluff.

00:00:36: Our mission is to distill the absolute top venture capital insights that we're circulating across LinkedIn over calendar weeks, seventeen and eighteen of twenty-twenty six.

00:00:45: Right!

00:00:46: And we've got a lot of ground to cover.

00:00:47: We are looking at market momentum This crazy AI power law The totally evolving founder fundraising playbook Some major geographic shifts in vertical AI and Dubtech

00:00:58: Plus the current reality of liquidity in secondaries Which is honestly keeping people awake right now

00:01:03: For sure.

00:01:04: So let's jump right in

00:01:05: Let's do it.

00:01:06: So normally, when we look at capital markets there is this underlying assumption of fluid dynamics like water flowing from high to low.

00:01:15: Capital supposed naturally disperse and fill all the available gaps in market.

00:01:19: Right always seeking out that efficient frontier innovation as a balanced ecosystem

00:01:24: Exactly.

00:01:26: But looking at the VC landscape over the last few weeks, those laws of physics seem to have just completely broken down.

00:01:32: I mean it does not look like a flowing river anymore.

00:01:35: No!

00:01:35: Not at all.

00:01:36: It's more like supermassive black hole Just bending all available liquidity in room into single point.

00:01:42: Yeah... The power law has frankly gone into overdrive.

00:01:45: Yeah, Devin Chata shared some numbers from Q one twenty-twenty six and they demonstrate exactly how concentrated things have become.

00:01:52: all those

00:01:52: number grew wild

00:01:53: right.

00:01:54: we saw a staggering two hundred sixty seven point.

00:01:57: two billion dollars invested in the single quarter.

00:02:00: to put that in perspective That matches us eighty three percent of all the venture volume.

00:02:04: for me entirety of twenty twenty five just three months

00:02:07: see it on paper looks like massive broad based market recovery

00:02:11: But there is a huge catch.

00:02:13: Yeah, the top line numbers are incredibly deceptive because when you drill down into where that capital actually went The whole broad-based recovery narrative just completely falls apart.

00:02:23: Just three deals

00:02:24: Open AI Anthropic and XAI.

00:02:27: Exactly Those Three Deals captured sixty five percent of all That Capital.

00:02:32: Three companies absorbed nearly two-thirds of the oxygen in the global venture ecosystem.

00:02:37: It's just unprecedented, and we're seeing an exact same consolidation happening at the fund level too.

00:02:42: Evelina Dineva analyzed data from April twenty-twenty six regarding fund closures.

00:02:46: What did she find?

00:02:47: So out of thirty closed funds that raised a combined seventeen point five billion Just four funds took eighty three percent.

00:02:56: Just

00:02:57: for yeah, we're talking about legacy giant scooping up almost everything which leaves smaller and emerging funds Structurally starved.

00:03:04: And to understand why this is happening We really have to look at the underlying mechanics of what these top tier AI companies are actually building.

00:03:10: Harry Stebbings highlighted an incredibly stark metric about the cost a frontier AI.

00:03:15: Oh The CapEx ratio

00:03:16: Right.

00:03:17: For these foundation model companies, every single dollar of run rate revenue requires approximately four to five dollars of capex to support it

00:03:25: which fundamentally breaks Every traditional sauce model we've relied on for the past fifteen years completely.

00:03:32: because with traditional software your cloud infrastructure costs scale somewhat linearly, or they even decrease as a percentage of revenue.

00:03:41: is you get more users.

00:03:42: But

00:03:42: with Frontier AI that infrastructure investment has to happen years before the revenue even materializes.

00:03:48: I mean CEOs are forced to forecast compute demand two years in advance.

00:03:52: That

00:03:52: sounds like a nightmare.

00:03:53: It is.

00:03:53: if you underestimate the GPUs You need latency spikes and you literally cannot serve your users.

00:03:59: but If you overestimate you're left holding billions of dollars into appreciating compute capacity.

00:04:04: The sheer physical cost of the hardware is why these companies need ten-digit checks just to stay in the game.

00:04:10: Okay, so that perfectly explains the demand side right?

00:04:12: Why do AI giants needs so much money?

00:04:15: but let's look at this supply site for a second.

00:04:17: We know from historical data That emerging managers like fund wine or fun two vehicles.

00:04:23: they consistently outperform mega funds In terms of alpha.

00:04:26: historically

00:04:27: yes.

00:04:27: So if the math shows emerging managers yield better returns I'd really struggle to understand why LPs are playing it so safe and exclusively feeding these massive incumbent funds.

00:04:38: Well, Myrtle-Lalacos broke down the LP mechanics on this recently... ...and reveals a massive structural bottleneck!

00:04:46: LPs essentially handcuffed by their own check sizes.

00:04:49: Handcuffed how?

00:04:51: Think about a large institutional allocator, right?

00:04:53: Like estate pension fund.

00:04:54: They need to deploy massive tickets just to move the needle on their own returns.

00:04:58: but if they write say a fifty million dollar check to an emerging manager's one hundred million-dollar fund... Oh!

00:05:04: ...they

00:05:04: suddenly cross that twenty percent ownership threshold.

00:05:06: Exactly and crossing that twenty per cent line triggers beneficial ownership rules.

00:05:11: That brings on immediate tax compliance in governance headaches The institutional capital simply will not tolerate.

00:05:17: Ah, so they physically cannot deploy small checks without triggering a regulatory nightmare.

00:05:23: Right and then you add internal diligence limits.

00:05:26: on top of that large LPs operate under strict policy guidelines.

00:05:30: They need audited financials across a multi-fund track record.

00:05:34: A brilliant new manager just doesn't have a decade of audited paper trail.

00:05:38: And I mentioned there's a lot career risk involved too.

00:05:40: Oh huge career risks.

00:05:42: Nobody gets fired for backing legacy blue ship firm even if the returns end up being mediocre.

00:05:47: But if you champion a first-time manager and the fun tanks, You wear that loss personally.

00:05:53: Which means founders are suddenly collateral damage in this market.

00:05:56: Capital is concentrated at the very top And the diligence hurdles for everyone else Are just higher than ever.

00:06:02: Yeah!

00:06:02: If your founder navigating early stages right now The margin of error has effectively dropped to zero.

00:06:08: You need a flawless playbook.

00:06:10: Quang Fan pointed out a fascinating shift In how deals actually being screened.

00:06:15: If you're a founder today, You aren't just competing against other founders anymore.

00:06:19: You are actively competing against investor AI.

00:06:22: Wait really?

00:06:23: VCs or automating the initial screen

00:06:25: completely vcs Are deploying ai models to screen pitch decks and data rooms before a human partner even opens The PDF.

00:06:34: These tools instantly cross-reference your metrics Against proprietary industry databases.

00:06:39: That is brutal.

00:06:40: Yeah.

00:06:41: So if your total addressable market claims are vague or your customer acquisition cost deviates from historical benchmarks for your specific vertical, the AI just flags it immediately.

00:06:51: Quying notes that to survive this, founders have to build a forty-seven item data room nine months before they even plan to raise.

00:06:58: Nine months?

00:06:59: Wow!

00:06:59: I mean building historical cohort data and granular churn metrics takes a lot of time.

00:07:04: you cannot synthesize that overnight.

00:07:06: No You can't.

00:07:06: And Chris Topman actually echoed the exact point He noted.

00:07:10: it's rarely a fatal flaw in business model That kills a deal at later stages.

00:07:13: It is almost always reactive data room

00:07:16: Like scrambling for numbers.

00:07:17: Exactly When a VC asks a complex financial question in week three of diligence and the founder has to pause the whole process to build a spreadsheet, answer it.

00:07:26: The momentum just stalls out trust evaporates And investor moves on into next company.

00:07:31: It's like showing up for final exam asking professor to borrow pencil.

00:07:35: That is great way.

00:07:36: put it.

00:07:37: Speaking materials Jenny Stoczkiewicz used ChatGPT to analyze four hundred of her own historical deal decks.

00:07:43: Just see what actually secured funding.

00:07:45: And the winning traits were so clear, it wasn't about dense text explaining complex technical architecture.

00:07:51: What was the differentiator then?

00:07:53: Visual trust using customer logos over text on the traction slide and even more importantly The winners used strict bottom-up market sizing grounding

00:08:02: vision in undeniable reality.

00:08:04: exactly Instead of claiming a top-down, you know, fifty billion dollar industry.

00:08:09: TAM.

00:08:09: successful founders calculated exactly how many target companies exist multiplied by specific validated annual contract value.

00:08:16: That makes so much sense and adding to that tactical advice Yorian Hoover brought up really important point Founders need stop obsessing over round labels.

00:08:25: Oh like seed versus preseed.

00:08:27: Yeah They agonize over whether to call it an extension or a pre-seed.

00:08:31: His advice is just drop the nomenclature entirely, state clearly that you were raising a three million dollar round and focus one hundred percent of your narrative on.

00:08:41: Are

00:08:50: we forcing founders to play a game they don't even need to be in?

00:08:53: With capital so concentrated, is traditional VC the right tool for most software companies

00:08:58: anymore?".

00:08:59: That's the million dollar question isn't it.

00:09:02: Iowanus Cacousis observed that for the vast majority of SaaS Founders, VC is actually the wrong tool.

00:09:07: now!

00:09:08: Really?!

00:09:08: How so?

00:09:09: Well…

00:09:09: The venture model relies on injecting massive capital intensive bets into a portfolio...

00:09:14: Right??

00:09:14: Historically, building software required that.

00:09:16: You needed a massive engineering team.

00:09:18: two years of cash burn just to ship an MVP

00:09:21: Right but today one developer with AI coding assistants can ship a viable product in three months

00:09:27: Precisely The capital intensity has plummeted.

00:09:30: So taking VC money forces a highly efficient bootstrapable business onto this totally unsustainable treadmill, you're suddenly optimizing for a billion dollar exit.

00:09:40: instead of just building a profitable company...

00:09:43: You are essentially strapping the rocket engine to bicycle!

00:09:46: The frame is not meant to handle that velocity

00:09:48: Right and it breaks apart.

00:09:50: Well Hunter Tobin actually brought some hard data back up how founders were adapting.

00:09:55: He's seeing a massive spike in founders utilizing zero percent APR business credit cards to secure up to five hundred K and early capital.

00:10:03: Oh, wow That is a brilliant cost of capital optimization isn't

00:10:07: it?

00:10:07: you bridge your early growth with free short-term debt.

00:10:10: instead Of giving out like forty percent of your equity You retain full ownership.

00:10:14: no board demanding a ten x return.

00:10:16: No liquidation preferences.

00:10:18: Peter Daimov summarized this modern founder mindset perfectly Fundraising should be treated as a trade for speed and time.

00:10:26: It is not a victory lap, And it's definitely no proof of market validation.

00:10:30: No its just really expensive fuel

00:10:32: Exactly.

00:10:33: But If pure software is becoming highly capital efficient, we do have to look at where that expensive venture fuel is actually flowing efficiently because outside of the massive foundation models.

00:10:45: We are seeing a massive shift toward vertical AI and deep tech.

00:10:50: Yes Ryan Alice broke down the market momentum here.

00:10:52: Vertical AI is definitely winning the enterprise software funding.

00:10:56: right now We're seeing things like a hundred and sixty million dollar raise for an AI platform focused exclusively on investment banking workflows.

00:11:05: But there is massive caveat, the underlying models themselves are not defensible.

00:11:10: moat

00:11:10: Right because buyers avoiding multi-year contracts knowing that AI might be commoditized in six months.

00:11:16: Exactly!

00:11:16: The switching costs are incredibly low.

00:11:18: If your only competitive advantage is sleek UI wrapped around third party language model You aren't building durable soft business.

00:11:25: you build churn machine.

00:11:26: So to survive, you need proprietary data and deep workflow embedding.

00:11:30: Which brings us the physical world where embedding technology is inherently difficult but deeply defensible.

00:11:37: Stanley August shared a fascinating insight on why Germany suddenly leading in AI robotics.

00:11:44: Oh, this is super interesting.

00:11:45: It's not about the software models right

00:11:46: now?

00:11:46: No it has nothing to do with having better foundational models than Silicon Valley!

00:11:50: It is entirely about physical infrastructure.

00:11:53: Germany has legacy factories companies like BMW and Siemens that are willing serve as live at scale production testbeds for humanoid robots.

00:12:01: That is wild because we've historically viewed Europe heavy regulation an industrial base As a massive handicap compared to US software market.

00:12:11: Right

00:12:12: But the moment AI needed to interact with a physical world, that dynamic completely inverted.

00:12:17: You cannot test industrial robotics in clean software lab and San Francisco.

00:12:21: you need a messy complex factory floor.

00:12:24: That industrial legacy has become massive structural moat

00:12:28: And the capital is shifting to reflect that.

00:12:30: Mate Varkoni pointed out that Germany's government backed HTGF Has actually overtaken Y Combinators Europe most active seed investor.

00:12:37: Wait!

00:12:38: Overtook YC.

00:12:39: Yeah

00:12:40: They are deliberately funding these hardware heavy deep tech bets that US generalist VCs usually skip over.

00:12:48: That's

00:12:48: huge, but there is a severe structural trap waiting for those European deep-tech companies as they mature.

00:12:54: Natalie Perron Wallace and Kambis Kohansal Vijarga both highlighted that while Europe has the talent in early stage momentum it severely lacks scale up capital

00:13:05: Because of pension funds.

00:13:06: Exactly European pension funds allocate roughly a hundred times less capital to venture than their US counterparts.

00:13:12: So while public initiatives are great at the seed stage, founders are starving during the transition phase when they need those fifty or one-hundred million dollar checks.

00:13:20: become global champions right?

00:13:21: And if the late-stage growth capital isn't there and exit timelines were stretching out we hit the industry's most pressing bottleneck liquidity.

00:13:29: I mean how do investors actually get their money back in this environment?

00:13:32: well The mechanics of venture exits have undergone fundamental regime change.

00:13:36: Masha Sheil shared some staggering data on this.

00:13:39: Over the last twelve months, venture secondary transactions hit sixty-one billion dollars.

00:13:45: Sixty one billion?

00:13:46: Yeah!

00:13:47: That officially exceeds liquidity generated by VC backed IPOs

00:13:51: because LP distributions have plummeted forty four percent from historical norms.

00:13:56: right

00:13:56: exactly?

00:13:56: The exit strategy is no longer about timing the IPO.

00:13:59: it's all about managing portfolio liquidity through a secondary market.

00:14:03: Institutional LPs simply cannot lock up their capital for fifteen to eighteen years waiting for an IPO.

00:14:09: that might never happen.

00:14:11: So if the exit timelines are stretching and the types of companies being funded or changing, how is that altering the actual DNA of investment firms themselves?

00:14:21: Well, Ivan Dundarov made a point in private markets structure as strategy And we're seeing this vividly in specialized sectors.

00:14:28: Take healthcare VC for instance, it's quietly evolving into a specialized private equity hybrid

00:14:33: because the math of a massive fund doesn't work for smaller exits.

00:14:36: precisely In health care software A fantastic exit might be a three hundred million dollar acquisition.

00:14:43: You have a billion-dollar generalist fund That doesn't even move the needle.

00:14:47: You're mathematically forced to hunt for ten billion dollar outliers, so these specialized healthcare funds are keeping their fund sizes small and acting more like micro PE

00:14:56: firms.".

00:14:58: Regarding private equity, Alex J. Prince pointed out a massive misconception among allocators.

00:15:04: Many LPs think they're getting diversified exposure by allocating to the mass of PE giants But those firms actually hold almost zero real early-stage venture Exposure right

00:15:13: there highly concentrated and late stage buyout while thinking They are diversified exactly.

00:15:18: And there's one final strategic shift we need to touch on regarding how firms were evolving.

00:15:22: from Pavel Prada He calls it wave three point O of VC media Because winning relies on seeing every deal first, top firms aren't just producing content anymore.

00:15:32: What

00:15:32: are they doing?

00:15:33: They're outright acquiring media publications to build proprietary top-of-funnel moats.

00:15:38: Wow!

00:15:39: Instead of building an audience from scratch by the established trust...

00:15:42: Exactly control a funnel and control deals.

00:15:45: To synthesize all this for you listening whether your an LP or GP or M&A professional You are navigating landscape where middle is completely hollowing out.

00:15:55: The Middle Is Dead

00:15:56: It is.

00:15:57: You either have the extreme capital to compete in Frontier AI, or you must build highly specialized defensible moats in vertical software hard tech or secondary liquidity.

00:16:07: The era of generalist funds coasting on the beta of standard SAWS Is effectively over.

00:16:12: it's adaptor become a rounding error.

00:16:15: and I want To leave with one final provocative thought inspired by luke's car wacky observations from the o-one hundred Europe conference.

00:16:22: Oh, the AI diligence point.

00:16:24: Yes!

00:16:25: As VC firms increasingly adopt AI tools for sourcing and diligence allowing a lean team of six to do operational work that used to take an analyst army of ten or fifteen people how long will it be before limited partners start aggressively demanding compression in traditional two percent management fee?

00:16:40: That is fascinating structural question.

00:16:43: The efficiency games are undeniable And LPs eventually want their share of those savings.

00:16:49: It could redefine firm economics entirely.

00:16:52: It really could.

00:16:53: Well, if you enjoyed this episode new episodes drop every two weeks.

00:16:56: also check out our other editions on private equity M&A and strategy in consulting.

00:17:01: Thank You so much for joining us On This Deep Dive.

00:17:03: Don't forget to subscribe And we'll catch ya next time.