Best of LinkedIn: M&A Insights CW 05/ 06

00:00:00: provided by Thomas Allgaier and Frennis, based on the most relevant posts on LinkedIn about M&A insights from CW five and six.

00:00:07: Frennis is a BDB market research company supporting M&A consultancies with a market and competition perspective, for example, in commercial due diligence.

00:00:16: Welcome back to the deep dive.

00:00:17: We have a massive stack to get through today.

00:00:21: We're looking at weeks five and six of twenty twenty six.

00:00:24: And honestly, if I had to put a label on it, It feels like the hangover is finally over.

00:00:29: The hangover.

00:00:30: You mean the post-hype slump?

00:00:32: Exactly.

00:00:32: You know that whole phase where everyone was just throwing money at anything with the .ai in its name?

00:00:37: It feels done.

00:00:39: Looking at these sources, the whole vibe has shifted from pure hype to, I'd say, almost ruthless operational discipline.

00:00:45: I see that.

00:00:46: It's less, this will change the world and more, how do we actually integrate this before we run out of cash?

00:00:51: We're not just looking at deal volume anymore.

00:00:53: We're seeing this fundamental change in who gets bought, how they get found, which is a huge topic this week.

00:00:59: And even the scale.

00:01:00: I mean, we have to talk about the misonomy at some point.

00:01:02: Oh, that merger is arguably the biggest news in business history.

00:01:06: But we've also got this silver tsunami of retiring owners and some pretty scary data on why deals are failing internally.

00:01:16: It is a dense couple of weeks.

00:01:17: We're going from the stratosphere of space tech right down to messy spreadsheets in a community bank.

00:01:23: So let's just jump in.

00:01:24: I think we should start with this sourcing revolution.

00:01:27: Because if you're listening and you work at M&A, finding the deal That's the part that makes you want to pull your hair out.

00:01:33: And according to Alexander Ivanov, the old way is officially dead.

00:01:37: RIP, traditional outbound rate.

00:01:40: And to be honest, looking at the numbers he gives, it's about time.

00:01:43: Okay, walk me through why he's calling it dead, because let's be real, plenty of firms are still doing it.

00:01:47: They are, but the efficiency or, you know, the lack of it is just brutal.

00:01:51: Ivanov describes the classic spray and pray.

00:01:54: You buy a list, you blast out a hundred thousand generic emails.

00:01:57: And just hope for the best.

00:01:58: Exactly.

00:01:58: You optimize for reply rates, but the math is just terrible.

00:02:02: You get maybe a one to two percent retainer rate.

00:02:05: And even when you get a bite, you're talking to an owner who might want to sell in what three to five years.

00:02:11: So you're just filling your pipeline with maybe later.

00:02:13: You've got it.

00:02:14: It's an incredibly inefficient use of time and capital.

00:02:17: Ivanov's proposing this shift to a signal-based system.

00:02:21: He's basically arguing that instead of volume, you need to filter for readiness.

00:02:26: He talks about identifying fifteen to twenty specific signals.

00:02:29: Signals that tell you an owner is ready to sell in sixty to ninety days, not three years.

00:02:34: That's the difference.

00:02:36: And Patrick Dower backs this up with some numbers that I actually had to read twice.

00:02:40: He compared a traditional analyst process with an AI drift.

00:02:43: The

00:02:44: speed difference is just.

00:02:45: It's exponential.

00:02:47: It is.

00:02:47: Bauer said a human analyst digging through databases might get through, say, forty targets in a few weeks.

00:02:52: That gets you maybe two qualified calls.

00:02:54: But if you're using AI agents like Apollo or ChatGPT configured the right way, you can screen five hundred targets.

00:03:00: In ten minutes.

00:03:01: Ten minutes.

00:03:02: It fundamentally changes the job description of the analyst.

00:03:06: They go from data gatherer to decision maker.

00:03:08: Yes.

00:03:09: But let's drill down on what signals actually means.

00:03:12: Because it's easy to say use AI, but if you don't know what you're looking for, you just get noise faster.

00:03:19: Lizwell Volkendorff had a great piece on this.

00:03:21: She calls it the AI deal scanner.

00:03:24: I love this concept.

00:03:25: It's like a metal detector for distressed companies.

00:03:28: But what are the actual signals?

00:03:29: Is it just looking for, I don't know, for sale keywords?

00:03:32: No, and that's the key.

00:03:34: If they're saying for sale, You're already competing with everyone.

00:03:37: She's talking about the breadcrumbs in public data that show distress or a ready to talk mindset.

00:03:44: For example, she highlights runway compression.

00:03:46: Which means?

00:03:47: Basically checking if a company's burn rate means they have less than twelve months of cash left.

00:03:51: That's all public data if you know how to pull it together.

00:03:54: Or another one she mentioned was ATM dependence.

00:03:57: I'm assuming we're not talking about cash machines.

00:03:59: No, no.

00:04:00: At the market offerings.

00:04:02: So continuous stock issuance.

00:04:04: If a company is constantly issuing stop just to keep the lights on, that's a huge red flag for distress.

00:04:10: It's like using one credit card to pay off another.

00:04:12: Precisely.

00:04:14: And she also mentions subtler things, like the language and earnings calls.

00:04:18: If a CEO shifts from talking about growth and expansion to preservation and efficiency, the AI can pick up on that sentiment shift, or maintenance capex deferral.

00:04:28: If a factory stops repairing its machines, they're either dressing the business up for a sale, or they're broke.

00:04:34: So the AI can spot that the roof is leaking before the landlord even thinks about putting up a sign.

00:04:38: That's it.

00:04:39: These signals appear months before the market knows.

00:04:41: And in twenty twenty six, if you're waiting for the teaser deck, you are already too late.

00:04:45: That brings us to the other side of this, though.

00:04:47: If AI helps you find the deal, it also completely changes how you value it.

00:04:52: Hima Day had a pretty serious warning for CFOs on this.

00:04:55: This is critical.

00:04:56: She basically argues that AI readiness is the new EBITDA quality test.

00:05:00: Explain that because EBITDA is usually just math, right?

00:05:04: Earnings before interest, tax.

00:05:07: How does AI change the math?

00:05:09: So think about how you value a company.

00:05:11: You look at their profit margins.

00:05:13: But HEMA's point is, how are they generating those margins?

00:05:17: If their profits look good, but it turns out they're driven by an army of five hundred people doing manual data entry that an AI could do for almost nothing.

00:05:25: Well, the quality of that EBITDA is pretty low.

00:05:27: Because the buyer knows that whole model is obsolete.

00:05:30: Right.

00:05:30: Buyers are starting to discount businesses that rely on these labor-heavy workflows.

00:05:35: If I buy you, I'm the one who has to fire those five hundred people and implement the AI, that's risk, that's cost.

00:05:41: So you face what she calls multiple compression, your business is simply worth less.

00:05:46: So it's not just do you have AI, it's is your business model defensible in an AI world?

00:05:50: And Johannes Reidel added another layer to this looking at HubSpot.

00:05:54: HubSpot's great case study for this twenty-twenty-six strategy, according to Radle.

00:05:57: They're not just adding AI chatbots.

00:05:59: They're acquiring horizontal tools like cash flow for payments, clear bit for data.

00:06:03: They are building a vertical AI monopoly.

00:06:06: They want to own the whole stack.

00:06:07: They want to make it so that for a small or medium business, HubSpot is your AI strategy.

00:06:12: You don't need five other vendors.

00:06:14: And speaking of owning the stack, we have to talk speed.

00:06:19: Because if you find these targets, you can't hang around.

00:06:21: Hendrick Jordan highlighted Meta's acquisition of Manus AI.

00:06:25: The two billion dollar deal.

00:06:26: Two billion.

00:06:27: And you know the timeline on that?

00:06:28: I saw this negotiated and closed in roughly ten days.

00:06:32: That is, that's just

00:06:34: wild.

00:06:34: It's unheard of for that size.

00:06:36: But Jordan's point is we are in a race for a gentic AI that actually does things.

00:06:42: And in that race, speed and certainty are becoming more important than haggling over the last five percent of the price.

00:06:47: You see the right asset, you just buy

00:06:48: it.

00:06:49: which is a perfect transition to the biggest buy we saw in this whole data set.

00:06:52: We're going from fast deals to absolutely massive deals.

00:06:55: We need to unpack the musconomy.

00:06:57: The

00:06:57: musconomy.

00:06:58: Emanuel Balsa broke this down and the scale is, it's just staggering.

00:07:02: We're talking about the merger of SpaceX and XAI.

00:07:05: The valuation numbers are hardy and get your head around a hundred.

00:07:08: No, one point two five trillion.

00:07:10: Balsam notes this makes it the biggest MNA deal in history.

00:07:14: Beats the old Vodafone management record.

00:07:16: But

00:07:16: look past the price tag for a second.

00:07:18: Most people see this and think, okay, Elon is putting his chatbot.

00:07:22: on a rocket.

00:07:24: But Balsa points out that this is an infrastructure play.

00:07:26: It's actually about energy and thermodynamics.

00:07:29: Thermodynamics.

00:07:30: Think about the bottlenecks for AI right now.

00:07:32: Data centers, they need insane amounts of energy and they generate insane amounts of heat.

00:07:37: Cooling them on Earth is as expensive.

00:07:39: You need water, fans, power grids.

00:07:42: and in space.

00:07:43: Space is a giant cold vacuum, and the sun is always shining if you're in the right orbit.

00:07:47: So the logic Balsa lays out is you put your data centers in orbit, you get unlimited solar power and free, perfect cooling.

00:07:54: You connect them with the Starlink network, which SpaceX already owns, and you run the XAI models on that infrastructure.

00:08:01: It's a vertical integration play that covers chips, energy, network and orbit.

00:08:06: It completely shifts the race.

00:08:07: It goes from who has the best language model to who owns the actual physics of computation.

00:08:14: It is terrifyingly smart.

00:08:16: It really redefines the playing field.

00:08:18: Okay, but let's come back down to earth.

00:08:20: While Musk is looking at Mars, the banking sector is seeing its own massive consolidation.

00:08:25: Panagiotis Criaris and Ben Brown were all over the Capital One acquisition of Brex.

00:08:31: Yeah, a five point one five billion dollar deal.

00:08:34: The largest bank fintech acquisition ever.

00:08:36: Why Brex though?

00:08:37: They do corporate cards, spend management.

00:08:39: Right.

00:08:40: Why does a huge bank need that?

00:08:41: Stickiness.

00:08:42: That is the key word in banking right now.

00:08:44: Capital One knows that just lending money is a commodity.

00:08:47: Anyone can do it.

00:08:47: But software, workflows, that is sticky.

00:08:50: If you are the expense management software that a company's employees use every single day, You are essential.

00:08:56: You can't be ripped out easily.

00:08:57: So Capital One isn't buying a bank.

00:08:59: They're buying a modern business payments platform that would have taken them years and years to build themselves.

00:09:05: Exactly.

00:09:05: And James White added some urgency to this for the smaller players.

00:09:08: He mentioned there have been a hundred and fifty bank deals announced recently.

00:09:12: The middle is just disappearing.

00:09:14: It's

00:09:15: scale or sell.

00:09:16: That's the mantra.

00:09:17: Tech costs are up.

00:09:18: Margins are thin.

00:09:19: If you're a community bank, you just can't afford the tech stack to compete with a Capital One Brex hybrid.

00:09:25: So you merge or you sell.

00:09:27: So whether you're SpaceX or a community bank, the pressure is on to consolidate.

00:09:33: But, and this is where it gets really interesting, integration is where these things usually fall apart.

00:09:37: We always assume deals fail because the market turns, don't we?

00:09:41: We do.

00:09:41: We blame the economy.

00:09:43: But Jesse Tremblay and Dave Fields looked at the actual data and market conditions.

00:09:47: It ranked last, literally one percent of the pain points.

00:09:50: One percent.

00:09:51: So you're saying ninety nine percent of the times are own fault.

00:09:53: Pretty

00:09:54: much.

00:09:54: The real killers are internal.

00:09:56: Fragmented tools, stitching together spreadsheets that don't talk to each other, just process chaos.

00:10:03: Frank Akela calls this the gray zone.

00:10:05: Define the gray zone.

00:10:06: It's that dangerous gap between the beautiful board deck where all the graphs go up and to the right and the actual messy accountability for execution in the first hundred and eighty days.

00:10:17: It's the who is actually doing the work phase.

00:10:20: And Kevin Oakes points out.

00:10:22: the soft stuff, the culture is actually the hardest part of that zone.

00:10:26: Culture mismatch is the number one failure mode and you know bankers often ignore it because you can't put good vibes in an Excel model.

00:10:34: But Jamie Hodari gave a fantastic example of when it works with the industrious and CBR-EDO.

00:10:40: I have to mention the image he used.

00:10:41: he compared it to a cat writing a Roomba.

00:10:44: It's a brilliant analogy.

00:10:45: Just stick with me here.

00:10:46: So industrious is the cat.

00:10:47: They bring the personality, the agility, the brand, the cool factor.

00:10:51: CBRE is the Roomba.

00:10:53: They bring the massive industrial engine that methodically covers every inch of the floor.

00:10:57: And the point is the cat doesn't try to vacuum the floor and the Roomba doesn't try to be cool.

00:11:01: Exactly.

00:11:02: It's symbiotic.

00:11:03: Too often in M&A, the big company tries to crush the small one.

00:11:07: The Roomba tries to eat the cat.

00:11:09: Here, they recognize that distinct value.

00:11:13: And Anna Vinson clarified this with a great technical distinction.

00:11:17: He says integration isn't just an IMO and integration management office-coordinated calendars.

00:11:22: It is operating model convergence.

00:11:25: That sounds a bit like consulting jargon.

00:11:27: What's it actually mean?

00:11:28: It

00:11:28: means if the business doesn't get simpler and faster after the merger, you haven't integrated.

00:11:33: You've just coordinated.

00:11:34: If you just slap two org charts together and create more meetings, you've failed.

00:11:38: You have to converge the models into something better.

00:11:41: I love that distinction.

00:11:42: And speaking of avoiding failure, let's talk diligence.

00:11:45: Because Adrian S shared a story that should terrify anyone looking to buy a software company.

00:11:50: This was such a great specific example.

00:11:53: A firm was looking at a fifteen million dollar acquisition.

00:11:56: They decided, you know, let's do one week of deep security due diligence.

00:12:00: What did they find?

00:12:01: They found the target's code was so full of security flaws that fixing them would cost twenty three million dollars.

00:12:07: Wait,

00:12:07: the company cost fifteen million?

00:12:09: The fix was twenty three million.

00:12:11: Exactly.

00:12:11: If they'd bought it, they would have been under water by eight million on day one.

00:12:15: That is the

00:12:15: definition of a bullet dodged.

00:12:17: It is.

00:12:18: And that's why Ingmar Kat and Mathias Engelmeyer argue for bringing enterprise architects in early.

00:12:23: You can't just leave the IT assessment for after the deal closes.

00:12:27: You need transparency on that IT mess before you sign the check.

00:12:31: Right.

00:12:31: And David Dinolfo and Abdullah Atizah is chimed in on this too.

00:12:35: It's not just IT.

00:12:37: tax, cyber, cultural risks, these are lie variables.

00:12:41: They affect the valuation.

00:12:43: It's not just legal fine print you check off at the end.

00:12:45: It all connects back to the AI point from the start.

00:12:48: If you use AI to scan for these risks early, like that deal scanner, you avoid buying a

00:12:53: lemon.

00:12:53: So we've talked about finding the deal, valuing it, integrating it.

00:12:56: Now let's talk about exiting, because eventually you want to sell and we have this demographic cliff coming up.

00:13:01: The silver tsunami.

00:13:02: B

00:13:03: Lane Carrick was discussing this.

00:13:04: It refers to the aging business owners.

00:13:06: Yes.

00:13:07: The majority of US business owners are over fifty-five now, so we're looking at a massive transfer of wealth as these founders retire.

00:13:14: But Carrick points out this dangerous gap.

00:13:17: These sellers, they do this once in their lifetime.

00:13:20: They built a great company over thirty years, they sell it once.

00:13:23: The buyers, usually PE firms, they do this every Tuesday.

00:13:27: It's asymmetric warfare.

00:13:28: Like playing poker against a pro when you just learned the rules five minutes ago.

00:13:32: Precisely.

00:13:33: And Saqya Haftutu, who's ex-Big Four, drops some serious reality here.

00:13:38: He says, you don't win by just finding a buyer.

00:13:41: You win by engineering competition.

00:13:42: Engineering competition.

00:13:44: Sounds a little manipulative.

00:13:45: It's

00:13:45: strategic.

00:13:46: You have to make the buyers feel like they're replaceable.

00:13:49: You have to create a situation where they bid like they are about to lose the deal.

00:13:53: That tension, that FOMO, is the only thing that gets you a top dollar outcome.

00:13:57: If a PE firm thinks they're the only one at the table, they will low ball you.

00:14:01: But you can't engineer that competition if your own house isn't in order.

00:14:05: And Keladizar had this brutal but necessary metaphor about the cap table.

00:14:10: He compares a business exit to a high society wedding.

00:14:13: Uh-oh.

00:14:15: I think I know where this is going.

00:14:16: So picture it.

00:14:18: The venue's booked, the cake's there, everyone's dressed up, the couple is at the altar, and then the ex bursts through the doors.

00:14:26: And

00:14:26: the ex in this case is?

00:14:27: That developer you promised equity to on a napkin at a bar back in twenty nineteen, or the cousin who gave you five grand to start the business and now thinks they own ten percent of a ten million dollar company.

00:14:39: That is a nightmare scenario and it absolutely kills deals.

00:14:42: It does.

00:14:43: As ours point is, buyers hate surprises.

00:14:48: A messy cap table signals a messy operation.

00:14:51: You have to get formal releases, you have to clean it up, don't let an uninvited guest object to your exit.

00:14:55: It sounds so basic, but it happens all the time.

00:14:57: Founders are often handshake people, but M&A is a contract game.

00:15:00: And this all ties into what Darman just sang and Matt Graham were saying.

00:15:04: An exit isn't just a transaction you decide on a Tuesday, it's a strategy you design years in advance.

00:15:09: Graham had a specific take on this with leverage, didn't he?

00:15:11: Right.

00:15:12: He says, don't just build a company to be sold for a revenue multiple.

00:15:16: Acquire assets that give you leveraged client books, unique delivery capabilities, or specific talent, you know, AQUI hires.

00:15:24: If you build your company with the exit in mind, you make yourself an irresistible target for a bigger player who needs exactly that leverage.

00:15:31: So if I'm trying to summarize this whole stack from Musk all the way down to the Silver Tsunami, the common thread is preparation.

00:15:39: preparation and discipline, the whole growth at all costs and move fast and break things era.

00:15:44: It seems to be maturing.

00:15:45: The twenty twenty six vibe is move fast, but know exactly what you're buying, how you're going to integrate it and how you're eventually going to exit

00:15:51: and use the right tools, those AI signals to do it.

00:15:54: Exactly.

00:15:55: Don't rely on luck.

00:15:56: Rely on data.

00:15:57: So what does this all mean for you listening right now?

00:16:00: Well, Whether you're aiming for Mars like Musk or just trying to clean up your cap table like a local founder, the winners in twenty twenty six are the ones prioritizing preparation, speed and operational discipline over hype.

00:16:13: The data is out there, the signals, the risks, the opportunities.

00:16:16: You just have to be disciplined enough to look for it

00:16:18: and brave enough to act on it quickly when you find it, ideally in under ten days.

00:16:22: Ideally, if you enjoyed this episode, new episodes drop every two weeks.

00:16:26: Also check out our other editions on private equity, venture capital and strategy and consulting.

00:16:31: Thanks for listening and don't forget to subscribe.