Brex was one of its kind back in the day. They started with offering corporate cards to founders who couldn’t get them from traditional banks. In recent years, Brex moved beyond startups to serve massive enterprises like Anthropic, DoorDash, and Roblox, focusing on “AI-native” expense management software rather than just credit limits. They also built their own core ledger and global issuing infrastructure, allowing it to issue cards in 50+ countries.

For Capital One, it can now be their software layer over the tech. enabled banking stack they have been building over the years.

Capital One is already a leader in consumer and small business (SMB) banking. However, it lacked the high-end, software-integrated “spend management” platform that modern CFOs demand.

Having recently integrated Discovery Financial (acquired in 2025), Capital One now owns its own payment network. By adding Brex’s software layer, Capital One can move money across its own rails while providing the best user interface in the business.

They can now target more high growth companies by providing them the all-in-one stack: Cards, expense management and credit with an AI software layer on top. Big W for Capital One.

What about the company and its investors?

The estimated cap table and returns below might give us an idea. (Shared by on X)

The founders? Big W as they take home more than $1B and get to lead Brex as a semi-independent unit inside Capital One.

What about the employees? Apparently the deal has minted more than a 100 millionaires, with the employee stock pool taking home $1.3B.

Some of them did not get to see the W as they were issued the stock at the peak of Brex valuation of more than $12B and it ended up being acquired at 40% of that price, meaning their RSUs were underwater at exit. More on the employee share of the proceeds in below.

The investors? Depends when you got on board!

BIG BIG W for YC. According to estimates their first check netted out close to $100M on an $120K check at the time. That is 800x in 9 years = 110% IRR. If you include their second YC continuity check, that is a total of $600M on probably ~$40M invested.

Series A investors did 80x and 64% IRR. Win.

Series B backers did 12x and 39%. Another Win.

Thereafter the returns start looking like more mature assets on the risk return spectrum.

Series C investors in 2018 do okay (2.75x and 15% IRR). This is more PE like.

Series C+ investors in 2020 with 1.3x but suboptimal IRR (4%). 2020-21 was the everything bubble.

Series D and D+ investors get nothing, apart from their money back thanks to the liquidation preference. 0% IRR but “at least they didn’t lose anything!!”.

This shows how different the dynamics are for different stages of venture, and it increasingly gets harder and more competitive (impacting the price you end up paying for your investment).

Venture lately has been looking like “go-early or go home” kind of a game. Mega cap IPOs might still make some large late stage investors some $$ but the rest in the middle will probably not be compensated for the risk they took.

2020-21 is far behind the rear view mirror and is probably never coming back. Late stage cross over pre-IPO arbitrage is gone with it.

Entry price matters. Backing great founders early is starting to be fashionable again.

You win some, you lose some. But when you win its a generational home run.

Until next time,

The Atomic Investor

We are now 3 years into current technology cycle and every quarter post earnings we get a sentiment check on where we are on the exuberance - pessimism spectrum. The durability of the momentum that has been powering the upwards move in the markets gets questioned time and time again.

We have had several ongoing debates since the start of the cycle - AI adoption, margins, scaling laws, valuations, and now the eternal AI capex bubble or not a bubble debate.

Even one of my favourite investors and thinkers, Josh Wolfe of Lux Capital, put out a tweet recently on the lack of value or returns on Big Tech capex.

I felt a simple statement or a back of the envelope calculation like this needed a deeper analysis. I understand the counter-argument: all this investment is billions of dollars of capex on fast depreciating infrastructure and high obsoletion risk, based on demand that is still yet to reach its peak and the technology yet to reach full maturity.

But here is how I see: it is missing the forest for the trees. The investment is predominantly long term in nature, not for returns from one quarter or one year to the next. We might not remember it now, but it took Microsoft several years to get to positive ROIC territory for Azure.

Second, all the investments in AI is not just allowing the Mag 7 to enter new product markets (see Google if you do not agree), but it is significantly improving their existing core business and deepening their moats.

I computed the Return on Incremental Capital Invested (ROIIC) for these companies and compared them over different periods as a test for increasing marginal returns on capex (whether incremental ROIC improved as AI investment ramped). I was not surprised (more on that below).

I agree it might be a bit of AI capex FOMO for Big tech, but it is now existential for them, not optional.

And like many other moves in the market, this cycle could turn out to be reflexive as well.

So what is really making people so jittery?

The shear scale of the investment in this short a period rhymes with a painful boom-bust event

We, for better or worse, are pretty good at pattern recognition. The absolute dollar amount of billions of AI related capex just over the past 3 years is taking us back to the dotcom bubble where billions were ploughed in to build the infrastructure layer of internet cycle (the fibre networks).

A fear of the ghost of depreciation eating into the earnings of these stocks continues to creep in. Some well known short sellers, who have now shut shop and are now hiding behind a paywall, are accusing the largest companies in the world (by market cap) of inflating the earnings by extending the useful life of the AI equipment (which lowers the annual depreciation). So much for pattern recognition and literally painting them as the “Enrons of the tech bubble”.

What is typically left out of the conversation is the difference between how the capex is being funded now vs the cycles they are compared to - a mix of growing base of cash flows and debt. Notice where the capex to FCF was during the actual bubble this cycle is compared to.

A levered bet is what exposes you against a slowdown or a downturn as opposed to a free cash flow funded one. Where deployment timing and amounts can be adjusted based upon where you are in the cycle, as compared to creditors knocking on your door if it does not work out as expected in a debt only funded investment cycle.

But where are the returns on all this investment????

Even if you are funding the capex mostly via cash flows, there is an opportunity cost of all these dollars being deployed. Which is where the returns argument comes in (as Josh Wolfe argued in his tweet).

I tried using this simple framework published by Coatue to measure the impact of AI on the top and bottom line, and the ROIC (using cumulative ROIIC).

Since investment cycles in tech are generally long term, it made sense to compute the cumulative ROIIC over periods - 2015-19 (pre - AI buildout) vs 2020-24 (AI buildout). 2022-24 was also computed to normalize for the Covid-19 period.

**Not normalized for GAAP non-cash charges and R&D capitalization effects (AI R&D is expensed immediately but is long term in nature) which understates the operating income (NOPAT)

There seems to be clear increase in the marginal returns on investment over periods for all but Microsoft. If you use ROIC as an indicator for long term economic value added for a business (above the cost of capital of course), this chart should put a smile on your face as a shareholder. Increasing returns to scale is what dreams are made of.

The operating income is essentially driven by both top and bottom line impacts.

For the top line, one can look at revenue growth rates, product usage, number of active users etc. as a test of the invested capital directly improving their core business, which at that scale is even more valuable.

Cloud:

All three major hyperscalers (AWS, Azure, Google Cloud) revenue growth rates have reaccelerated substantially since 2023, a direct result of new models and AI related services that they have been introducing.

Ads:

Search (GOOG) was +14.5% in Q3 25, Youtube ads +15%, Meta overall ad growth was +26% with both impressions and ad price growth reaccelerating. AMZN ads was +22% in Q3 25.

Engagement:

Across Facebook, Instagram and Threads, our AI recommendation systems are delivering higher quality and more relevant content, which led to 5% more time spent on Facebook in Q3 and 10% on Threads. Video is a particular bright spot with video time spent on Instagram up more than 30% since last year. And as video continues to grow across our apps, Reels now has an annual run rate of over $50 billion. - META Q3 2025 earnings

Rufus, our AI-powered shopping assistant has had 250 million active customers this year with monthly users up 140% year-over-year, interactions up 210% year-over-year and customers using Rufus during a shopping trip being 60% more likely to complete a purchase. Rufus is on track to deliver over $10 billion in incremental annualized sales. - AMZN Q3 2025 earnings

Enterprise:

MSFT’s Productivity and Business Processes segment up 17% in Q3 25, driven by Copilot, automations and AI assistants led increase in usage.

Bottom-line impacts are related to cost efficiencies, which we have started to notice with headcount growth. Sadly, they also have long term implications for our society and the labour market.

Reflexivity rules

Using George Soros’s theory of reflexivity to analyse the cycle makes it even more interesting.

The core concept of the theory posits that the markets simply do not move towards equilibrium just based on fundamentals (prices where buyers and sellers agree at a point in time). Instead it operates through a two way feedback loop with two key components:

• Fallibility: Investors have imperfect understanding of reality, heavily influenced by cognitive biases and emotions, leading to systematic mistakes in assessing fundamentals.

• The Reflexive Loop: Rather than fundamentals driving stock prices, stock prices actually drive fundamentals. What investors think about reality influences reality itself, which then shapes their thinking again.

Applying this framework to the current cycle, one can look at periods of rising Mag 7 or Big tech stock prices (since 2023) where investor enthusiasm around AI drove prices up along with some early results in the fundamentals which justified increasing capital spending.

But now as some of these stocks enter considerable drawdowns from their recent highs as the markets start to question the spending (Meta for example, which has corrected 25% from its highs), the reflexive loop is likely to act in reverse where falling stock prices make Big Tech readjust the amounts and timing of the capital spending, self correcting the speculative elements of the AI capex and forcing upon some capital spending discipline on them.

Meta is a case in point. It was reported last week that there could be potential budget cuts of as high as 30% in the Metaverse segment in 2026, a direct response to stock price correction. AI spending will still increase, but instead of putting in more of its cash flows on AI a part of Metaverse related spending will be reallocated towards higher priority AI related projects.

Why? you may ask.

Because stock prices matter to the managements. In the current AI buildout phase which is getting ultra competitive by the minute, stocks are the currency used to attract talent and make acquisitions, the value of which depreciates as the prices go lower.

A lower stock price makes the value of equity packages less attractive and puts you at a competitive disadvantage in recruiting top AI talent. Then, stock based acquisitions become more dilutive and less attractive to the targets at a lower price.

This self correcting mechanism might as well take care of some of the frothiness we might be witnessing in this AI cycle.

So which way are we leaning in this debate? You can probably infer where I stand from my takes above.

Until next time,

The Atomic Investor

The Athletic ran an interesting piece back in January on the rise of Visionaries in football using both Amorim and Ange as an example, and how results, although still of prime importance have given way to an ‘identity’ and principles focused approach at most clubs. It is important to win still, but its even more important how you win.

The game has evolved and fans (and the players) need to believe in the ‘project’. Literally this is the word clubs use to sell the vision to get both players and fans on board. Managers or Head Coaches who are in the driving seat are therefore more suited for the role if they themselves are more visionary than pragmatic, not focused on just getting the next 3 points or the title but creating a style and culture in and around the club.

“I do not know what a Plan B or C is, … If people want me to change my approach, it’s not going to change. We are doing it for a reason.” - Ange Postecoglou after losing 6-3 to Liverpool last season

“I cannot change my idea in one day because we will lose much more now,…I was here because of my idea and I will continue to do my idea until the end,… “I have to sell my idea,… I don’t have another one.” - Ruben Amorim after one of the many losses last season

I wondered, so this is what defines a visionary then. The psychological resistance of falling short time after time but sticking to your identity and what you believe in. And then came the night of the final, two visionaries facing off for a European title, and most importantly a spot in the Champions League next season.

One of them stayed their visionary self sticking to their principles, and the other got their most Jose Mourinho style pragmatism out. Defending deep and counter attacking. Something they had not really turned to throughout the season. And no it was not Jose’s compatriot Amorim, but Ange.

This was where one could separate the real visionary who put everything on the line for what he believed in (and fell short most of the times), from someone who shifted to a relatively safer approach when the stakes were the highest - A European (or any) title the club never won, and his own job being at risk. Interestingly, the pragmatist won but still got the axe, while the visionary who lost, and lost badly is still there trying to build something he believes in from the start.

It was fascinating to notice and think about the tiny details that could have made a difference. The environment they were in - one being told he can keep building even if he fails at the first big test, while the other probably told that it was now or never. The difference between having the option of taking a risk, failing, getting embarrassed while still sticking to the path you want to be on. Long term vision and identity vs delivering short term results under pressure.

Similar instances can be found throughout history - in business, technology and investing about certain visionaries who did not give in when the going got tough and found certain early backers at their side who bought into that vision, being equally, if not more critical to turn that into reality.

A few examples below, albeit from the ‘extremistan’ - rare instances of unforeseeable events where many unknown unknowns play a role in driving the outcomes. Also guilty of ignoring the embedded survivorship bias in them in picking the best of the best. But we can all of easily relate to these stories and famed companies and founders, which makes it easier to drive home the point.

Steve Jobs and the Apple Resurrection (1997-1998)

When Jobs returned to a near-bankrupt Apple, board member Ed Woolard Jr. took enormous personal risk bringing back someone known for uncompromising decisions. Larry Ellison offered to help Jobs buy Apple outright, understanding that integrated hardware-software vision wasn't just different but necessary. With this backing, Jobs killed profitable product lines, bet everything on the iMac, and launched "Think Different" while hemorrhaging money. The early believers provided cover for decisions that looked insane but proved transformational.

Jeff Bezos and Amazon's Dot-Com Survival (2000-2001)

As Amazon's stock fell 94% and critics called it "Amazon.bomb," board member Tom Alberg and investor John Doerr provided crucial support for Bezos' "Day 1" philosophy. Alberg defended long-term infrastructure investments when other directors demanded immediate profitability. Doerr helped articulate the customer obsession strategy to the skeptical Wall Street. This patient capital allowed Bezos to accelerate expansion during the crash rather than retreat to short-term thinking.

Elon Musk and Tesla's Production Hell (2018)

During Tesla's near-bankruptcy, early investor Antonio Gracias of Valor Equity and Larry Ellison doubled down when others fled. Gracias understood from the PayPal days that Musk's refusal to outsource manufacturing wasn't stubbornness but strategic necessity. These believers absorbed massive paper losses while providing the capital that let Musk sleep on factory floors and build production tents rather than compromise on vertical integration.

Zuck’s Facebook and early challenges

Peter Thiel's investment in Facebook is another example, from a time when it was still limited to college campuses. He recognized that Zuckerberg's obsession with authentic identity and real-name verification represented a fundamental shift from anonymous internet culture. When other social networks were adding games and entertainment features, Thiel supported Zuckerberg's insistence on maintaining Facebook's core identity focus. This patient capital allowed Facebook to resist the temptation to become a general entertainment platform and instead build the infrastructure for what became the social media revolution.

What connects all these examples to what I noticed about Amorim and Ange is that moment of ‘push come to shove’ - when external pressure demands compromise of core vision. Each visionary and their backer faced their ‘Europa League final moment’ where they could have pivoted to a safer, more conventional approach.

Which is also why the world of tech and finance and the amalgamation of it - venture investing fascinates me the more I learn about it and read stories like above. The early believers fail as often as the visionaries, but that one visionary may eventually compensate for all others that did not fulfill their vision (no offense to Adam Neumann and Sam Bankman-Fried ofc). They look for telltale signs about their builder’s character like:

• What drives them to get up every day and work towards realising the vision?

• Do the even believe in it themselves? Or is it borrowed conviction?

• How do they think about failure? And uncertainty? And the high risk of never reaching their goal?

• Most importantly, when the going gets tough, would they ‘pivot’ towards the safer bet? Or double down?

Something very intangible and hard to back by KPIs and data points but still of utmost importance. Not just for the investors but for the visionaries themselves. Managers with their players and founders with their founding team and early investors. A symbiotic relationship built on mutual risk-taking, where both parties understand that protecting the vision's integrity is more important than immediate returns or social approval.

Another thing I think about often in this context is a section in Jeff Bezos’ Amazon annual shareholders letter in 2021 on differentiation being key to survive today and avoiding being typical:

“In what ways does the world pull at you in an attempt to make you normal? How much work does it take to maintain your distinctiveness?

To keep alive the thing or things that make you special? We all know that distinctiveness – originality – is valuable. We are all taught to 'be yourself.' What I'm really asking you to do is to embrace and be realistic about how much energy it takes to maintain that distinctiveness. The world wants you to be typical – in a thousand ways, it pulls at you.

Don't let it happen. You have to pay a price for your distinctiveness, and it's worth it. The fairy tale version of 'be yourself' is that all the pain stops as soon as you allow your distinctiveness to shine.

That version is misleading. Being yourself is worth it, but don't expect it to be easy or free. You'll have to put energy into it continuously. The world will always try to make Amazon more typical – to bring us into equilibrium with our environment. It will take continuous effort, but we can and must be better than that."

The natural tendency of any system is to push us towards the mean, towards normalcy and what is safe and predictable. But building something special requires people who can resist this gravitational pull.

Today when ideas and thoughts propagate at light speed all over, and our thinking is being increasingly abrogated to algorithms and probabilistic models, the world starts to look even less distinctive than it already is. You see a lot of ‘safe and predictable’ everywhere you look. The proven business models, used playbooks, business school driven strategies, borrowed investment ideas, non original content, undifferentiated creators, the X thread bois, and the cringe inducing LinkedIn slop.

But every now and then you also see visionaries and their backers trying to stand out on their non conformist paths fighting this entropy Bezos talks about in the letter. We must cherish and respect their successes and failures, and support them when we can.

And if you did not know it already, Ruben Amorim has my full backing for this season, yet again. This is me not being delusional, but just a believer.

Until next time,

The Atomic Investor

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Exhibit A:

Google Cloud Next 2025 and the days following up to it where it made multiple announcements

• Its enhanced GPU and TPU lineup including Ironwood, along with new Nvidia Blackwell GPUs on Google Cloud

• Gemini Flash and Pro updates and its integration with their audio and video models like Chirp 3, Lyria and Veo 2

• Vertex AI - Its Agent ecosystem in the Cloud, including the A2A protocol (for multi-agent systems to communicate with each other), with data, security and coding agents

• On top of that Gemini and its AI integration within the Workspace environment

This full stack approach is now finally allowing Google to unify its presence across the infrastructure, model, orchestration and application layers to establish a coherent AI story. Bears labelled Google as not being nimble enough, too slow and too late to the AI models party and its core search business being under existential threat. The numbers do look quite lopsided for its biggest competitor in the model layer.

Could this vertical integration be an effective way to compete with much smaller players against their individual moats in each layer? After all it has the largest distribution moat, across various consumer and enterprise touchpoints (Search, Chrome, Android, Youtube, Workspace, Gmail and others).

All of this and we have not even factored in DeepMind and all the developments in their Alphafold series of models, or the progress on the Waymo front which is now at 250k autonomous robotaxi paid rides per week.

Exhibit B:

Open AI was in the news (no surprise there) for few different things. The most noteworthy of them was the Bloomberg leak on its discussions to buy Windsurf, an AI coding assistant for $3B, which was the valuation at which it was trying to raise its latest round.

AI coding assistants has been one of the hottest application layer areas that has been attracting capital left, right and centre from investors. Microsoft made that move early by buying out Github, which launched its Github Copilot last year. Others like Replit, Anysphere (Cursor), Magic, Supermaven, Poolside and Lovable have continued to raise fast (at >25x ARR in a lot of cases) at multi-billion dollar valuations.

Instead of just powering these assistants with an API, Open AI has now made a move upwards to enter the application layer itself. Jamin Ball had a nice writeup last week on it and some of the implications of AI labs moving ‘up the stack’.

The model cost curves have continued to fall from a cliff as inference price of LLMs have decreased between 9x and 900x since 2021 decreasing more than 10x every year (depending on the tasks).

As the commoditization of AI models and APIs continues, moving into “products” seems like a natural move for the model providers to maximise revenues and profits.

According to reports, Open AI is also working on its own social network trying to build an image generation social app . Seems ludicrous, but it is again another bite out of the application layer by owning an AI social app.

More importantly, it lacks what Meta and X have, the vast treasure trove of real-time user generated Instagram, Facebook, Threads and X posts that continuously feed and improve the Llama and Grok models. Something that is extremely valuable in the race for marginal improvements in frontier models, amongst other use cases.

Remember it also has a GPT App Store?

Exhibit C:

Anthropic, another leading AI Model lab released its own coding assistant, Claude Code powered by its latest Sonnet 3.7 model as it saw Claude was the preferred model for developers.

Then, it released its own AI application communication protocol (the MCP) which in its own words is like a USB-C port for AI apps, providing more context to LLMs connected to apps.

Last week it alos launched its Research assistant as well which is able to integrate with Google Workspace and search across both the web and internal work.

While it has not forayed deep into the application layer directly yet, the intent is clear. Extend your competitive advantage by providing tools across the stack.

AI is going vertical

We might no longer be able to look at model providers confined to their own territory as they begin to vertically integrate upwards. A few examples of this in previous tech cycles give us a good look into how this fared in the past:

The IBM Mainframe era where it designed and manufactured hardware (mainframes like System/360), developed the operating systems and core software, provided maintenance and services, and handled sales and leasing directly. They controlled almost the entire value chain for their customers.

Apple in the Mobile / PC era where it designs its hardware (Mac, iPhone, iPad), develops its own operating systems (macOS, iOS), designs its own core processors (Apple Silicon), controls the primary software distribution channel (App Store), operates its own retail stores, and offers integrated services (iCloud, Apple Music, etc.). While it outsources manufacturing, it maintains tight control over design, components, and the overall ecosystem.

In a way Neflix in the Streaming era could be similar where it began as a distribution platform (DVDs, then streaming licensed content). It strategically integrated backwards into content creation, becoming a major producer of "Netflix Originals." It now controls both the global distribution platform and a significant library of owned content.

Does it make sense though for this cycle?

Janelle Teng used Clayton Christensen’s Modularity Theory pretty well to answer the question in her recent piece.

In markets which are not fully mature enough (where a performance gap exists), competitive advantage goes to interdependent builds and vertically integrated companies since customers value functionality and reliability at this point.

Whereas in markets which have a performance surplus, competitive advantage goes to modularization through specialization, outsourcing, and dis-integration, since this is where other factors beyond core functionality, such as speed, responsiveness, and convenience, tends to win.

AI is still not in the mature bracket yet that needs modular architectures, and where products are not good enough, so a full stack approach or building a moat in multiple layers could stillyield significant benefits. Its all about building durable competitive advantages at this point, but it may change as we move along this cycle.

It could reshape the current ecosystem

With the model providers moving into the application layer, and incumbents like Google trying to compete across the stack, they are directly competing with smaller players in the application layer they are serving. This could reshape the current dynamics as one might wonder if the APIs powering the third party apps will have access to the best models as the one driving their own apps or tools?

Akin to IBM’s own software and database systems competing with third party software providers in the mainframe era, Microsoft’s tightly coupled OS and Applications competing with independent PC application providers in the PC era, or Apple’s own apps and tightly controlled ecosystem competing with third party apps in the Mobile era.

We are slowly leaving the “operator neutral infrastructure” territory.

Counterintuitive, but capital deployment in the Application layer might continue to trend upwards

Application layer AI native companies have attracted a ton of capital, which in 2024 was $8.2B, up 110% YoY. These companies are also growing much faster than their previous generation SaaS companies, with the AI cohort reaching $1M in revenue at a median of 11 months as compared to the latter at 15 months.

Model layer companies and other “integrated” giants are deep pocketed and more established than most of their smaller application layer counterparts. Quite naturally, investors would look at them now entering the space where they are deploying capital and start discounting the diminishing returns on the marginal dollar invested given increasing competition with integrated players.

However, it may be the case that now application layers start looking like attractive acquisition targets for model providers and internet giants, which might push more capital towards AI apps as a new exit avenue opens up for investors.

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The Davids vs the Goliaths

The Davids, the smaller AI application builders might be wondering where to go next as incumbents have started to enter their space early in the cycle and offer the “good enough features”.

Taking a page out of Christensen’s Disruptive Innovation theory in this story, they might have to focus more on simplicity and ease of use, focusing on customer niches that are small or too specialised for the large incumbents. They might also add more value if they can quickly become the best tool for a specific user in their vertical, iterating faster than larger players can.

The cycle has taken an interesting turn where moves made by AI labs and incumbents are showing signs of benefits of vertical integration outweighing the integration, market disruption and regulatory risks.

But what competitive advantages might sustain going forward in this AI cycle? And in what layers?

Until next time,

The Atomic Investor

2024 was a net positive year for the industry. The macro situation improved with rates trending down and inflation subsiding.

The markets were ebullient, pricing in the new government’s unleashing of animal spirits via lowering regulatory barriers and pushing dealmaking and M&A, which started to pick up after a 2 year lull.

The largest publicly listed U.S PE stocks, more exposed to increasing capital markets activity were up north of 20% (and some as much as 50%) from August last year to end of January this year, while the main indices returned between 10-15%.

A different lens for PE

I also like looking at the industry from a “Listed PE” lens, and see how the mega caps of this world have been faring. I believe it is a fare proxy for the overall industry and sentiment (not to mention they manage more than a quarter of global AUM and captured more than 60% of new funds raised in 2024, also being at the forefront of the most important trends in the alternatives space).

The megacap PE basket: BX, KKR, BAM, EQT, CG, APO, ARES (also the Mag 7 of PE) now manage $4.8T in assets across asset classes.

The market saw increasing activity last year and some signs of getting PE getting off the sidelines:

• Dealmaking: Megacap PE deployments increased considerably in 2024 - 18.6% YoY increase, whereas Private Credit deployments continued to go from strength to strength with 94% YoY growth.

More benign macro environment definitely helped, along with the buyer-seller valuation mismatches somewhat normalising, where buyers and sellers could be willing to transact without much hand wringing.

• Exits: Realizations (in value terms) were up by 33% as well after a tough couple of years.
  

The market slowly started to open up more, especially during the later stages of the year. But exit strategies have changed quite substantially. IPO markets are still quite dormant, which historically were the primary source of realizations.

Sponsor-to-sponsor deals continue to increase (especially in Europe) which may be a consequence of the dire need to deploy (and earn fees) for the buyers, and to exit (and realize the carry) for the sellers.

It also begs to be asked if and how the next sponsor can do enough to create value in an asset that has gone through multiple sponsor holds.

Like a football club changing managers very often and is stuck with a squad that is not theirs. Reminds me of my club at the moment.

• Liquidity: Liquidity continues to be a challenge with GPs not distributing enough capital back to LPs or not soon enough the consequence of declining deal activity and exits in the past. This naturally stops the PE flywheel in its tracks as LPs cannot recycle capital back as step-ups into flagships or newer funds.

  Distributions as a % of NAV continues to be around 10% (lowest it has ever been) with recent vintages moving much slower along the typical J-curve, increasing payback on commitments for LPs.

  

  

  
  Lack of organic distributions has led to other avenues, like Continuation vehicles which are now almost 20% of total distributions.

  

  Skeptics will argue about it being a circular flow of money (which theoretically is true). But given an increasing proportion of new fund inflows are coming from the “retail”/private wealth channel into “evergreen” funds or perpetual capital, continuing to hold some assets for very long could be more aligned to this new setup.
  

  How do you continue to create value through the cycles to create liquidity for your LPs in that vehicle? Dividend Recaps? NAV loans? Sell smaller stakes in the asset? None of this seems very sustainable. But an interesting dynamic to follow nonetheless.
  

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Macro musings and the AI conundrum

Although we are past the rate hike and inflationary regime of the last 3 years, economic growth is sluggish in most regions and we are now in late stage slowdown part of the cycle.

And now to top that off, threat of tariffs and geopolitical tensions is adding a further layer of complexity and uncertainty in the short term with a negative impact on GDP and rising inflation.

Public markets have taken a thump since the start of Feb (S&P 500 down 6%, Nasdaq 100 down 8%, Mag 7 down 14%). Consumer and business confidence is hitting new lows.

There has been clear shift in sentiment as the markets realized that asset prices are not the priority.

“Nothing to do with the market,”... “I’m not even looking at the market, because long term the United States will be very strong with what is happening here.” - Donald Trump

Does not look like M&A is either.

“Maga doesn’t stand for ‘Make M&A great again’” - Scott Bessent, U.S Treasury Secretary

But the opportunities loom large

Private market returns now look more compressed than from past cycles putting further pressure on GPs, given that the opportunity costs are higher with higher base rates.

This is pushing PE managers more towards opportunities arising from the current tech cycle, the range of outcomes (and consquently returns) for which is much wider than what PE might be used to.

But AI is still a looming opportunity that both public and private market investors cannot afford to miss out on.

The world still needs to build out the compute and energy infrastructure of massive proportions, especially now when we are moving towards real world use cases across the consumer and enterprise applications.

PE managers see this as a massive opportunity to deploy capital across the tech and infrastructure landscape. But would the ROIC be enough when capital light businesses are the ones that have outperformed over last decades?

“As we all know, the rise of AI is transforming industries and companies and has a potential to create tremendous value, and is already doing that. At EQT, with our Motherbrain team, we are going after opportunities both for efficiencies and costs, et cetera, but also to drive revenues across our entire portfolio sector by sector, and of course starting there with tech as we talked about before” - EQT 2024 Q4 earnings call

“Some of it may change. And the good news for our investors is we're not doing things speculatively. It's based on the demand signals from our tenants. That's when we go out and spend the big dollars to build these things” - Blackstone on how they deploy capital to build out AI infrastructure

Where does this leave Listed PE?

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It all comes down to earnings quality

Listed PE’s fundamentals continue to improve. Fee related earnings (FRE) were up 20.4% in 2024 while Distributable earnings (DE), a proxy for shareholder cash flow were up 17%.

Private market managers are no longer playing the traditional PE game. The fundamentals are very clear about this now being about scale and permanent, more durable sources of capital.

FRE, a less cyclical and more stable source of earnings now make up a substantial source of DE for these GPs, and for some all of DE is FRE.

FRE naturally grows more visibly as GPs scale and diversify their capital base.

Insurance and Private Wealth channels are now contributing an increasing amount to the marginal dollar raised by Listed PE.

This continues to reduce the exposure of Listed PE earnings to the cyclicality of capital markets but there still might be some near term impact.

“We expect the capital markets environment to remain relatively more challenged over the near-term, pressuring the group’s PRE (carry and realization related earnings) recovery and to some degree weighing on FRE growth amid risks of slower capital deployment, more elongated capital raising campaigns and moderation in retail flows - resulting in a mid-single digit negative EPS revisions across the group.” - Alex Blostein, Goldman Sachs”

The stocks have already been clobbered since the tariff threats became a reality.

Sentiment shift works both in your favour and against it as we can clearly see.

2025 seems to be another year where macro and industry specific challenges as above might continue to keep growth somewhat muted. However, scaled managers with differentiated solutions, like Listed PE seem better positioned than ever to deploy under distressed market conditions, if and when they arrive.

Until next time,

The Atomic Investor

This week we witnessed such a dramatic swing in the AI narrative triggered by an unexpected player from China - DeepSeek, an open source LLM provider funded by a quant hedge fund.

No more energy? No more capex? No more compute?

DeepSeek released its latest R1 reasoning model, the latest set of models that can reason and constantly check for logic in their output. It was apparently trained with much less compute and memory requirements (and consequently training costs) at both training and inference stages.

They used a method called distillation - think of it as creating a highly efficient student model that learns from a more powerful teacher model. This approach allows the student model to maintain most of the teacher's capabilities while requiring significantly less computational power and memory.

The concept was around for some time, but the reason it sent a shock through the public and private markets’ system was the disintegration of the currently held beliefs:

• That superior AI requires massive computational resources and energy and the access to compute was a constraint. Hence we need more data centres and more power. Big Tech can do it at scale spending billions in capex - the scale moat.

• That only the biggest tech companies could afford to compete. The conventional wisdom held that training state-of-the-art AI models required massive capital investments in compute infrastructure. DeepSeek's use of distillation - essentially teaching a smaller model to mimic a larger one's capabilities challenges this assumption head on. - the capital moat.

• That Nvidia's GPU dominance was unassailable - Nvidia's seeming invincibility in the GPU space for training AI models has been a cornerstone of the AI boom. Their GPUs are optimized for machine learning with superior FLOPS per dollar per watt metrics (used to compare the computational cost of different machine learning models, helping to understand how much processing power is required to run them). They are also most efficient when it comes to the networking between the GPUs themselves and the memory components. For better models you needed more sophisticated and more expensive chips that worked best with Nvidia’s own platform. DeepSeek's achievement using older, less sophisticated chips suggests this moat might be narrower than previously thought. - the GPU moat

The winner take all narrative in AI was also directly challenged as a new disruptor stepped into town.

The most important trend in finance in the past 40 years is the emergence of winner-take-all industry structures in technology. We live in a world of network effects and increasing returns to scale. In industries from PC software (Microsoft), internet search (Alphabet), online retail (Amazon) and social media (Meta) to high-end consumer hardware (Apple), most of the profits and market share have gone to one company. It is only a moderate exaggeration to say all that has mattered for investors in the past few decades was being on the right side of one or more of these winner-take-all stories. - Robert Armstrong, Financial Times

The markets reacted as they always do, wiping out almost a trillion dollars in market cap from the AI infrastructure basket.

Fresh narratives and fresher debates

We quickly moved on to new narratives as the DeepSeek moment updated our priors and opened a fresh set of debates.

Move over pre-training, Inference is here

An already evolving narrative being widely discussed in podcasts and research papers. That the marginal gains in model output and performance was reaching its limits due to data scarcity and lack of enough compute.

Models were moving towards Inference test time compute - the computational resources used when the model is actually deployed and making decisions. Reasoning capabilities enable the model to “think” through problems step-by-step (like the OpenAI o1 and DeepSeek R1 models) and , rather than simply producing an output based on its pre-trained knowledge.

New narrative:

• Lower barriers to entry: This shift potentially allows smaller teams with fewer resources to develop competitive AI models.

• Reshaping Investment Landscape: This change may impact capital allocation decisions in both public and private markets, particularly for companies heavily reliant on pre-training infrastructure.

AI for everyone

As barriers to entry are lowered and models become cheaper and more efficient there will be more low cost models driving down overall costs and increasing adoption across consumers and enterprises.

How low do these curves go?

New narrative:

• Adoption set to increase across use cases and verticals. Wrote more about it here

• Big boost for applications with a lot of model API calls like AI Agents

The Capex debate

Constant bone of contention every quarter. As Big Tech capex spend continued to go up, fears of unused compute increasingly lingered. I wrote about it here, here and here.

Estimates went up again across the board even post DeepSeek. The message is clear, we still need to build out the infrastructure they said, but it is likely to become more ROIC (Return on Capital Invested) oriented and not just disappear. Inference needs as much (if not more) scaling as did pre-training.

New narrative:

Compute demand could actually increase as inference demand spikes up and models are deployed to solve more complex tasks. Some early signs of it have emerged already.

Then, more accessible models lead to widespread adoption, which drives up inference computing needs. It's reminiscent of how decreased computing costs in the internet era led to exponentially more computing overall.

The Nvidia debate

The tide seemed to suddenly turn against the biggest AI darling due to all of the above. Pre-training is where its moat was the most fortified, and as we move toward smaller, less compute heavy models there will be alternative players competing with it. All resulting in the weakening of its monopolistic position and therefore lower steady state market share, pricing power and margins.

But its not asleep at the wheel by any means. Its new inference line of chips and architecture is designed for better performance at scale.

Our upcoming release of NVIDIA NIM will boost Hopper inference performance by an additional 2.4x. NVIDIA Blackwell architecture with NVLink Switch enables up to 30x faster inference performance and a new level of inference scaling, throughput and response time that is excellent for running new reasoning inference applications like OpenAI's o1 model. - Nvidia Keynote

I see this as a 2x2 matrix between the TAM for AI GPUs and Nvidia’s expected market share.

Amidst the AI infra market frenzy, developments in the robotics and EVs space are flying completely under the radar where Nvidia also dominates.

Plus its CHEAPER than when it all began!

What probability weighted scenarios from above are we picking and how are we discounting them to the present?

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The Application layer has arrived

Value creation vs value capture was another one of those ongoing debates. AI infrastructure providers seemed to be creating and capturing most of it (reflected aptly in their valuations and stock prices). I mentioned it here a while ago echoing the prevailing narrative.

Now when the consumer and enterprise facing apps see one of the largest line items in their cost structure shrink, does the application layer increasingly become the value creator and start capturing more value in the stack?

Two well known CEOs certainly had that in mind already, calling LLMs a commodity long before.

As AI becomes more capable and agentic, models themselves become more of a commodity, and all value gets created by how you steer, ground, and finetune these models. - Satya Nadella, Microsoft CEO

As increasingly powerful models like GPT-4 are being released, that view has actually not changed, in that LLMs are becoming more and more ubiquitous with the new models as they emerge. … The impact that we provide is upstream of those LLMs, and the transformational value of AI is really unlocked when organizations are able to deploy LLMs within their enterprise on their enterprise data in a secure way, allowing them to make operational decisions and actions using AI. - Alex Karp, Palantir

The DeepSeek R1 moment might be remembered as the catalyst that helped the market develop a more realistic understanding of AI's future landscape - one where innovation and efficiency matter as much as raw computing power and capital. Amidst these shifting narratives there were also some timeless lessons:

1. Technology is Deflationary: Cost curves in technology typically move in one direction - down. The question isn't if, but when and how quickly.

2. Never get married to your beliefs. Especially the ones where innovation and human ingenuity are trying to solve a problem.

3. And finally, the power of narratives:

   Even as the brain is dying, it refuses to stop generating a narrative, the scaffolding upon which it weaves cause and effect, memory and experience, feeling and cognition. Narrative is so important to survival that it is literally the last thing you give up before becoming a sack of meat. - From the book You Are Now Less Dumb, David McRaney

Until next time,

The Atomic Investor

It's a striking parallel, but one that masks fundamental differences in the underlying business dynamics.

The Great Infrastructure Flip

The most telling crossover is visible in Microsoft's capital expenditure trajectory. The once-quintessential "asset-light" enterprise software company now spends more relative to its sales than Verizon, with its capex/sales ratio surging to 25% while the telecom giant hovers around 13%.

This dramatic role reversal illustrates how Big Tech has evolved from being merely a tenant of digital infrastructure to becoming its master architect.

Here are how the numbers stacked up in 2024:

*Note: All figures in billions of dollars. Operating costs include cash operating expenses, software, depreciation and electricity

The spending spree shows no signs of abating. Meta, Amazon, and Alphabet have all indicated that their capital expenditure will be higher in 2025 than in 2024. Goldman Sachs expects hyperscaler capex to reach $264 billion this year before rising to nearly $300 billion the next.

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But here's where history diverges. Unlike the telecom companies of the dotcom era, today's tech giants are generating enormous free cash flows while making these investments. Their capex + R&D to operating cash flow ratios remain manageable, suggesting these investments are more sustainable than the debt-fueled expansion of the past.

Another key differential might lie in the shorter payback periods for AI investments and the continued robust growth of core businesses. Microsoft, Meta, and Alphabet maintain capex and R&D spending at 30-45% of revenues while growing their fundamental operations - a stark contrast to the telecom boom's aftermath.

But this also creates an interesting paradox: while these companies have the financial strength to fund massive infrastructure investments, unlike their telecom predecessors, the sustainability of this spending trajectory is becoming a key investor concern.

The Depreciation Conundrum

While these tech giants still generate substantial free cash flow even after their massive AI investments, free cash flow (operating cash flow less capital expenditures) across major tech companies is flat to declining. This matters - during previous cash flow downturns (like 2021-2022), these stocks significantly underperformed the market, with Microsoft being the sole exception.

Adding to the complexity is the opacity around how these investments will be depreciated. Ravi Gomatam of Zion Research Group points out that modeling future depreciation at big tech companies is highly complex. Data center investments comprise multiple components - servers, infrastructure, buildings - each potentially depreciated at different rates. The rapid pace of AI innovation adds another layer of uncertainty, as equipment obsolescence could trigger unexpected writedowns.

The gap between capex to revenue and depreciation to revenue is 11%, with the former growing much faster for now. But that gap when eventually hits the income statement the operating margins could take a considerable hit.

The historical cautionary tale of Corning's fiber optic business serves as a sobering reminder - after peaking at $4 billion in 2000, it took 18 years to recover to those revenue levels in nominal terms, and in real terms, they're still below those peaks.

Looking ahead

As we witness the next wave of AI infrastructure expansion, with hyperscalers estimated to have spent $222 billion on AI chips and data center infrastructure in 2024, the investor community stands at a crossroads. The debate is now evolving in real-time between scaling laws versus inference efficiency and whether capital intensity will remain the moat it appears to be today. The focus will likely shift from AI hype to harder metrics - investment levels, depreciation policies, and most crucially, cash flow generation.

"Investment, depreciation and cash, and not just AI hype, will matter for tech investors this year."

The telcos built the information superhighway, but today's tech giants are building something potentially more transformative - the infrastructure layer that will power the next generation of computing. The question isn't whether they can afford to build it - it's whether they can afford not to. And the answer might lie not in the amount of capital deployed, but in how effectively it translates into new sustainable competitive advantages.

It remains to be seen is whether this infrastructure boom will follow a different path than its predecessor, or if, like all cycles, it will eventually find its own equilibrium.

What is even more likely is the emergence of new winners (and eventually losers) in the next phases of the AI cycle. More on that in the upcoming pieces.

Until next time,

The Atomic Investor

When I looked at recent developments at Vivagym it started making more sense. It was yet another sign of the broader transformation sweeping through Europe's fitness landscape led by the increasing appetite of private equity firms for gym chains and wellness clubs.

The Vivagym Story

Spain offers a peek into the fitness market evolution currently underway, and Providence Equity's aggressive expansion through Vivagym illustrates the accelerating consolidation in this space.

The Fitup deal was Vivagym's fifth acquisition in 2024, positioning them as the fourth-largest fitness operator in Spain.

Providence’s entry into the Spanish market began with its buyout of Vivagym from Bridges Fund Management in early 2024, with another large PE fund’s foray indicating a fragmented yet promising market for growth. The transaction was valued at approximately €250-300 million, ~12x EBITDA, reflecting both market potential and operational value creation opportunities.

Post these five acquisitions since the buyout, Providence is transforming Vivagym from a regional player into one of the more dominant ones in the Iberian Peninsula.

Vivagym now operates 222 centers in Spain and 41 in Portugal, serving approximately 441,000 members in Spain and 110,000 in Portugal. The company is projecting 2024 revenues of €180 million, highlighting the scale benefits of consolidation.

But this is far from being the first PE-backed transaction in the space in Spain, and clearly there is an opportunity and consolidation potential that other investors are trying to capture as well.

*Estimated based on market comparables and strategic positioning

Market Dynamics and Investment Rationale

At the first principles level, there are some interesting dynamics underlying these deals.

Fitness clubs are the OG recurring revenue models. You pay a monthly subscription fee, and at times even the whole annual amount at the start of the year to tick off the new year’s resolution and rarely stop making that payment.

Customer acquisition / retention costs are not that high. Gym memberships are one of the first things you sort out when you move to a new city or a neighbourhood, and once you are in you rarely want to give up that convenience and benefits (based on location, facilities, social dynamics).

At the operational level, the business and the underlying infrastructure is quite scalable and at each centre you can expand and grow your customer base with increasing operating leverage, standardised customer experience and replicable results.

And its not just Spain or Iberia, this consolidation is happening in most markets in Europe led by notable PE firms orchestrating different strategies with some common themes around value creation.

But most importantly, for the consolidation / roll-up strategy to work well, the fitness industry's structural characteristics align perfectly with private equity's playbook:

Fragmented Market Structure

The European fitness market is highly fragmented with a lot of small operators. There are over 65k facilities across key EU markets, with the top 30% of them only controlling 25-30% market share.

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Secular Growth Drivers

Increasing awareness and demand:

Post the pandemic there has been a secular trend of growing awareness around fitness related activities, and health and wellness continues to maintain its spot in one of the most important consumer markets. There is a demographic shift towards wellness oriented lifestyles (Have you noticed the increasing number of run clubs in your city?)

European wellness and fitness market is worth more than €26B and projections suggest a compound annual growth rate (CAGR) of approximately 7-9% through 2030.

Digital integration opportunities:

A hybrid (physical + digital) model is one area where the runway for growth is still massive, with most centers still behind the curve creating a digital experience around workouts, advice and other services.

Premium segment expansion:

Some PE backed Fitness operators are now expanding from the traditional budget segment (cheaper generalised model) to a more premium, boutique, and a more specialized model with additional services. There is a growing trend towards other activities (Hyrox / Crossfit, Sauna, Pilates, Combat sports and the like).

Multi-format offerings is another area along the lines of the ones above, combining different subscriptions, digital services, individual or group sessions together. Services like Wellhub are a good example of this emerging space.

Operational value creation opportunities

Operational synergies are another important structural feature of this strategy that allows PE firms to enhance Revenues and EBITDA, and therefore the exit potential even further.

Operational Efficiency:

• Technology platforms consolidation: Consolidating IT systems like CRMs, Payments and other digital platforms.

• Procurement economies: Supplier consolidation and contract adjustments for equipment, consumables and maintenance services.

• Marketing efficiency: Combining marketing functions and systems to reduce customer acquisition and brand/other marketing costs.

• Personnel and Training: Optimizing the management layer and other admin. costs.

• Real estate optimization: Economies of scale in lease and contract negotiations.

Revenue enhancement:

• Membership optimization via cross-sell opportunities and improving retention.

• Scale and geographic density benefits to improve customer satisfaction and retention

• Improved pricing power (and possibly ARPU adjustments).

• Digital offerings to improve app engagement and reduce churn.

One of these large fitness operators, which went through the PE investment lifecycle offers insights into how a private buyer can create value in this sector.

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The Basic-Fit Case Study: Entry to Exit to maybe Entry again?

3i Group, back in 2013, recognised the European Fitness scale-up opportunity in the budget / value segment quite early on. They invested €110 million for majority stake at an entry EV of €275 million.

• Initial Footprint: 199 clubs across Netherlands, Belgium, Spain

• Revenue at Entry: €140 million (2013)

• EBITDA at Entry: €30 million (approximate)

The investment thesis centered on the growing European value fitness segment, where Basic Fit's scalable model offered significant potential for geographic expansion and operational enhancement.

3i's approach balanced aggressive growth with operational discipline, including strategic growth levers such as:

• Accelerated club rollout strategy

• Geographic expansion into France and Luxembourg

• Enhanced digital capabilities and member experience

• Standardized operating model across markets

And the results were quite extraordinary.

It expanded from 199 to 419 clubs, with a revenue CAGR of 23% and EBITDA CAGR of 38%, improving margins by considerably in the process. Memberships increased from 720,000 to 1.2 million validating both the market demand and the effectiveness of Basic Fit's value proposition.

3i exited (partially) by taking the company public in 2016 and crystallizing the impressive returns.

• Exit EV: €1.2B

• Returns:

  • IRR: ~50%

  • Money Multiple: ~4x

After almost a decade, it now might become a PE target again

Under the current management, Basic Fit has continued to demonstrate growth in fundamental metrics as a public company.

But the share price never really kept up post Covid and multiple compression on top of it led it to decline by more than 50% between Jan 22 and Feb 24 despite continued operational improvement and growth.

A couple of months ago, a Basic Fit hedge fund shareholder Buckley Capital Management urged the management and after holding discussions to put itself for sale to a PE buyer. Then one of its largest shareholders with 10% of outstanding shares, Impactive Capital, wrote an open letter to the management to buy back shares at the current price to create value for existing shareholders and narrow the current valuation discount.

Its trading at a cheap valuation indeed, between 6-6.5x EBITDA roughly half of the typical 11-12x deals we see at this scale in the sector.

Although Basic Fit now being much mature might not be an aggressive expansion play, under private hands it can experiment with the formats it offers and strategically reposition itself. It could also develop its brand portfolio development and extend the services it offers via digital and technology infrastructure enhancement.

Consolidation of smaller scale operators in major markets could be another opportunity (for add-ons) and it will be interesting to see how this develops. Just the multiple arbitrage play (buy low, sell at typical fitness market multiple) can help the PE buyer realize attractive returns.

PE is playing a pivotal role in reshaping the competitive landscape in the fitness and wellness sector. The combination of fragmentation, operational improvement potential, and secular growth trends continues to drive their interest, particularly in markets like Southern Europe where consolidation is still in early stages.

Watch out for 2025. PE might be very soon coming for some Gains at a gym near you.

Until next time,

The Atomic Investor

The latter could be linked to football clubs now increasingly being run and operated by entities to unlock financial value and growth. The former feeds off it, as clubs bring in tools and professionals with their refined playbooks to drive results which in turn drives commercial value for the club.

Football finances are at the heart of it all. In the short term, clubs have been scrambling to fix their finances after the governing bodies and leagues have introduced new Fair Play and Sustainability rules.

These new rules target limiting unfettered spending by clubs on wages and transfers to maintain competitiveness, and protect smaller clubs from bankruptcy as they try to bridge the ever widening on and off field gap with their larger peers. UEFA focuses on wage cost ratios and three year breakeven rules with a cap on three-year net losses to EUR 60mn , while the Premier League limits the combined losses over the previous three seasons to GBP 105mn (a brief intro to the rules here).

Squad costs have taken centre stage: player wages, transfers and other squad related expenses need to make the numbers work. In the long run this is pushing clubs to evolve their operating model, with youth players becoming an integral piece of this puzzle.

Cashing in on youth - Short term benefits

Spending on the squad has become an imperative for clubs to compete. Transfer and wage bills continue to balloon, putting pressure on the squad cost ratios and profitability of clubs. This year too, the story was no different. The top 10 leagues spent more than EUR 1.8bn (on a net basis) with the Big 5 European leagues taking a lion’s share of it.

The English Premier League continues to be the biggest spender with more than GBP 2.4bn of gross spend, but it was actually down 42% from a year ago as the new financial rules start to kick in.

The June 30th deadline for clubs to submit their annual accounts for the PSR assessment (which is also an accounting deadline for them for the financial year) saw a flurry of activity of player trading and sales between clubs to prevent any PSR breaches. As a result a number of homegrown youth talent changed hands (or turf rather).

Something to keep in mind here is that football accounting rules dictate a lot of the decisions clubs take when it comes to their squad. The full amount from a player sale can be recorded in the same financial year, whereas when you buy them you can spread the cost over the years of the contract (amortisation in pure finance terms).

But how have the homegrown or youth academy players suddenly become even more valuable financially? From an accounting perspective a sale of a player developed by the club or a youth player from the academy has a starting value of zero on the balance sheet. Selling these players allows clubs to record the full amount of sale right there and then. Another player who was bought by the club when sold instead records the difference between the sale price and the carrying value of the player on the club’s balance sheet.

“I didn't want him to leave but it is the rules. The clubs have to deal with the Financial Fair Play rules and the rules are not great. The rules are, I would even say, bad... it forced us to make this decision”. - that is Erik Ten Hag on the sale of Scott McTominay, another homegrown player and fan favourite sold this summer to avoid PSR breaches, amongst many.

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Clubs have now increasingly started to increasingly cash in on their youth or homegrown talent to balance the books or rake in additional player trading profits and be above the water for PSR. Some clubs have even apparently formed transfer alliances to help each other with comply with the new rules.

Everton sold — and the emphasis is on sold — Lewis Dobbin to Villa for £9million, a day after Tim Iroegbunam went in the opposite direction for the same sum. The clubs could have swapped the two youngsters, but two sales helped push them towards compliance. Profit is booked from sales but the outlay is amortised — spread over the length of the player’s contract up to a maximum of five years.

Villa have struck mutually beneficial deals with Chelsea, too. Ian Maatsen has become their new left-back for around £37.5million, while relatively untested rookie Omari Kellyman will go from Villa Park to Stamford Bridge for £19m. - The Athletic

And questions have been raised how clubs could be inflating the value of youth players (on both sides) to record a better number on the books for PSR compliance.

The third area of debate is the valuations of some of the players. Take Kellyman for instance. Chelsea rate the England Under-20 international very highly. But is it understandable they were prepared to spend £19m on a player that Villa picked up for £600,000 from Derby County’s academy two years ago, and who has made just six first-team appearances, totalling 150 minutes of action? Such a valuation is great news for Villa of course, who can register almost £19m of profit in this year's accounts. - BBC

While this might help clubs fix their financial issues in the short term, one could question what kind of impact this could have on the game.

There is an increasingly long list of clubs which do not meet the regulations, both at the top level and lower tiers.

Larger clubs which are financially comfortable (are well below the squad costs and net losses limits) might now be in a position to easily buy attractive youth players from the ones which are in a more precarious situation financially. Where does that leave the gap between the Big 6 or 8 and the rest in the long run, the very thing these rules were trying to target? As an owner or operator of a club outside the top 8, this puts you in a tough spot.

Additionally, there is an added intangible risk of developing youth players with an eye on just using them as commercial assets eventually impacting the squad mentality. You sell a vision of the long term project to your youth players, help them integrate with the club’s ecosystem and build a connection with the fans who hope they will break into the first team. High squad turnover with a short term myopic view offsets most of this progress, and is unlikely to sit well with supporters.

A club like Chelsea for example has slowly lost its lustre under the new ownership with the constant trading of players, no real identity on the field but some shrewd financial engineering and gains off it.

Numerous other Chelsea academy products have been sold: Fikayo Tomori to AC Milan for an initial £25million, Marc Guehi to Crystal Palace for an initial £18million, Tino Livramento to Southampton for an initial £4million, Ruben Loftus-Cheek to AC Milan for an initial £15million. Lewis Hall has joined Newcastle United on loan this summer and is expected to sign permanently next summer in an initial £28million deal. Bayern Munich have been in talks over a deal to sign Trevoh Chalobah before the transfer deadline and Conor Gallagher would have been sold had the right offer come for him (who was eventually sold to Atletico Madrid).

Even before add-ons and sell-on clauses, plus numerous other smaller deals, are taken into account, it is getting on for £200million in transfer revenue for academy products over a two-year period. - The Athletic

Diamonds in the rough - A long term play

The focus on youth is not recent development though. Clubs had long realised that the operating model around the squad had to evolve, from scouting for a few undiscovered stars to having a proper structure in place for youth training and development. The average age of squads has been constantly declining in the major European leagues, an indicator of where the focus is shifting to.

With higher demand for youth players, their values have also soared, both in top leagues and outside of it.

World class youth academies and have become crucial strategic investments for clubs as it covers both commercial and footballing aspects for the owners.

From a footballing PoV, youth players coming through the ranks are well versed with the club’s philosophy and style of play and can be shaped to fit into different roles the team needs.

Younger players are like diamonds in the rough. Their technique, skill and temperament can be tested internally, or developed outside at a different club via loans for example. This is hard to put into place for a player who you paid a handsome sum for, and signed them on a long term contract expecting them to deliver from day zero.

Financially, it is a long-term play to improve your squad costs and expenses. Younger players are less expensive as they start with a basic contract which is slowly improved as they become an increasingly important part of the first team squad, which meaningfully helps lower the squad expenses.

If their market value continues to improve, they become an even more attractive asset commercially. With a carrying value of zero on the balance sheet, they can be opportunistically sold to generate income. Player trading revenues from academies from some of the top clubs just goes to show how valuable a strong youth setup can be.

An interesting research paper tracking player transfers between 2015/16 and 2020/21 shows that the economic impact of homegrown player sales is the largest at the biggest clubs with more resources the scout the best talent and train them at their top level facilities. Their lack of necessity to sell whenever an offer arrives also helps keep the asset and increase its value.

But there ares still a whole bunch of clubs in the middle of the pack that have really turned their financials around, like AS Monaco going from -EUR 60mn transfer balance to EUR 161mn and Olympic Lyonnaise from -EUR 85mm to EUR 136mn showing the growing importance of homegrown players in the squad economically.

Youth training and infrastructure being exempt from the new PSR and FFP rules is an extra bit of tailwind to the long term trend already in play.

Academies and player development have become an integral part of a club’s setup. There are clubs like Borussia Dortmund in Germany, Ajax in the Netherlands, Benfica in Portugal which have a thriving youth setup and have made player sales to bigger clubs as a core part of their operating model. Other approaches with a blend of tranining the youth towards first team development and opportunistic sales are increasingly more common. Financially, the youth setup of the club could slowly become one of the engines of a club driving it forward.

The diamonds in the rough cannot be ignored in the modern game. Some might even call it an indicator of long-term success when assessing the potential of a club commercially and on the field.

Until next time,

The Atomic Investor

Deal leverage had to come down significantly with a big disconnect between buyers who wanted to buy low and sellers who were used to selling high (or had bought high and wanted to sell higher). Multiple expansion turned into multiple compression. A challenging macro environment meant both investments and exits almost came down to a halt, breaking the PE flywheel in its tracks. Some might even call it a necessary evil needed to eke out the excesses the industry got accustomed to in the ZIRP era.

The mega-cap PE managers recently reported their Q2 earnings which is always a useful pulse-check of the industry. Roughly 2.5 years into the correction, things have started to look a wee bit better.

The macro backdrop is much more benign than it was a even six months ago. Interest rates are on the way down. Dealmaking looks like it is finally bottoming, albeit with a different (and hopefully better) playbook. Some optimism seems to be creeping back in.

This is no roaring recovery, it is a slow and steady grind to the end of a long tunnel.

The chart makes it pretty clear where the problem still lies though.

Deployments are picking up the slack

It was a great quarter for deployments and putting that perennially dry powder to use. The top 5 managers alone deployed more than $180bn across all strategies/asset classes, with private credit continuing to take a major share in literally everything from deployments, to fundraising and returns.

As a result of our convictions, we decided to adopt a more aggressive approach to new investments. I'm pleased to report that in the second quarter, we deployed $34 billion, the highest level in 2 years, and nearly $90 billion in the last 3 quarters since the 10-year treasury yield peaked - Blackstone Q2 24 earnings

Turning to capital invested. We deployed $23 billion of capital in Q2. For the first half of 2024, we have now deployed $37 billion, which is almost double the first half of 2023. Real estate, in particular, had a strong deployment quarter across equity and credit. - KKR Q2 24 earnings

Private Equity deployment was strong although still lower on a TTM basis, while Private Credit grew 37% YoY, led by both organic originations and via credit GPs acquired over the last few years

Another major area of growth for Brookfield has been our credit business, which has grown to over $300 billion of assets. The growth of our credit business has been the biggest driver in our revenue growth over the past 12 months, with second quarter credit revenues up 19%. - Brookfield Asset Management Q2 24 earnings

Mega-cap PE has always been quick to adapt to market conditions. They are now leaning into the hottest sectors like AI. GPs are deploying across the capital stack and strategies to make the most of these opportunities - from lending billions to AI Cloud providers like Coreweave, building AI datacenters with their portcos, or even signing renewable PPAs with large hyperscalers for their datacenters.

If we think about our data center investment activity,…it's been one of the core investment themes for several years.…And that's not just in datacenter, it's also in power generation, transmission, renewables, technology, and then also Capital Markets plays a piece in this as well, as you heard from the CyrusOne example….we have 4 independent data infrastructure platforms, 1 in the U.S., 1 in Europe, 2 in Asia Pacific. - KKR Q2 24 earnings

Our portfolio today consists of $55 billion of data centers, including facilities under construction, along with over $70 billion in prospective pipeline development…Through QTS and our other holdings, we have a robust ongoing dialogue with the world's largest data center customers. We're also providing equity and debt capital to other AI-related companies. - BX Q2 24 earnings

Our Microsoft agreement is a good example of our value to customers. This groundbreaking agreement is almost 8 times larger than the largest single corporate PPA ever signed. Being at the intersection of AI growth and renewable energy development, positions Brookfield uniquely to capitalize on these trends. We can provide our clients with scalable, integrated solutions that meet their needs, both for advanced data centers and sustainable energy sources. - BAM Q2 24 earnings

Exits are still far from their very best

PE owned assets are ageing as hold times continue to creep up. While deployments now seem to be picking up, exits are still flat (at a deal count level, but with some growth at deal value level).

This has created an imbalance of sorts where PEs keep raising (and now increasingly deploying) on one hand with long held assets on the other, not converting the paper wealth into actual returns for their LPs.

Partial exits via continuation funds have picked up pace, where PEs move the asset to a new fund using the new LPs’ cash to pay out the investors in the previous fund (some would even say it is very Madoff-esque..).

Secondaries is another area that has gone through the roof as both LPs and GPs sell stakes in funds to monetize the NAVs and get some much needed liquidity.

And so that's why secondary's business continues to grow. And our business, which we started with 10 years ago at $10 billion has grown eightfold, and so there's a need for liquidity. And even today, if you look at the volume of secondaries at trade, it's 1% to 2% of the underlying NAV in funds, which is very low for most asset classes in terms of liquidity. - BX on their secondaries business

Mega-cap managers are however optimistic of realizations picking up, and now that the Fed has finally signalled a rate cut, the exit logjam might finally start to clear as M&A and IPOs slowly open up again for business. But it still might not be an “opening up the floodgates moment”.

The main message we shared with our investors is that we are seeing significantly greater market activity since the beginning of the year. The macro inflation and rates backdrop has improved, markets are open, and the deal market is back. To give you a sense, globally, year-to-date, leveraged credit issuance is up over 100%, IPOs are up nearly 50%, and announced M&A is up approximately 25%. - KKR Q2 24 earnings

The way we would describe the current environment, with rates stabilizing even now declining and liquidity improving, is we're just seeing that bid-ask close and that's going to lead to more transaction activity. We certainly wouldn't suggest at this point that we're seeing overly aggressive or irrational bidding behavior. - BAM Q2 24 earnings

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But mega-cap PE continues to grow

After a muted or no growth in distributable earnings last year (fee related earnings plus net realizations, a proxy for cash flow), the key PE metric is expected to return to growth in 2024.

Credit focused managers like Apollo and Ares, already having a stellar 2023, are expected to continue growing.

Other avenues like Secondaries has been a highlight for the industry, which does well in environments like these. Only 1-2% of global PE NAV is traded in secondaries, and GPs expect it to increase to 3-4% unlocking billions in secondaries opportunities. Secondaries or private solutions focused managers like StepStone or Hamilton Lane are expected to grow quite handsomely.

Private Wealth channel is still allocating to Alternatives as mega-cap PE launches more dedicated semi-liquid products and expands distribution channels and networks.

Our commitment to the $85 trillion private wealth market is stronger than ever. Multiple other areas of the firm are showing strong momentum today. - BX Q2 24 earnings

Retail PE products are still largely untapped, but there are some movements on that front. KKR for example has partnered with Capital group to launch a series of hybrid (public and private) products for the mass market beyond the accredited investor base.

The industry has come through a full market cycle in less than 4 years - of zero rates to inflation led rate hikes and peak interest rate in decades, to now the beginning of a rate cutting cycle. LP allocations have little room to expand beyond where they are, but other areas in Credit, Infra and mass market PE/private wealth might continue to unlock opportunities further.

Q3 should make it clearer whether they had finally reached the end of the tunnel.

Until next time,

The Atomic Investor

Both public and private markets seem to be relaying this sentiment as well. Stocks viewed as AI beneficiaries have been on a tear making them the only idea that seems to be working at the moment.

The S&P 500 gained 33% since October 2023 until June 2024, and 56% of those gains have been linked to AI beneficiaries - Nvidia, other semiconductor stocks, and Big Tech (Apple, Amazon, Microsoft, Google, Meta).

The gains have been even more concentrated since Q1 this year, with Nvidia, other semis and Big tech as a group up 45%, 17% and 11% respectively (until June 2024), while everyone else as a whole is…down 2%.

In Venture land, AI/ML deal values and valuations continue to climb, with Gen AI infrastructure and horizontal platforms (like model builders which cut across industries and use cases) attracting most of the capital.

These concentrated gains in just one area of the market are far from reflecting the true economic reality, and seem to be driven mostly by excitement, momentum and FOMO.

Given that estimates of future revenues and profits have not moved much, a lot of the gains have come at the expense of rising valuations which implies pricing in even higher expectations of AI delivering substantial value for investors, businesses and consumers going forward.

Underinvesting is a bigger risk

The AI cycle is not being played out in vacuum. The ChatGPT moment last year kickstarted a massive capex cycle as companies realised they needed to build out the underlying AI models and the compute infrastructure to suffice a surge in demand for AI use cases.

AI needs an enormous amount of compute resources (processing power). For reference, vs a simple Google Search, ChatGPT requires 10x the amount of compute, while a video generation model requires 10,000x of it.

This is translating into several hundred billions of $$ worth of GPUs, servers and data centre build outs needed to serve the AI infrastructure layer. Even a16z has decided to hoard some Nvidia GPUs (20,000 of them according to media coverage) to attract startups building in the space.

Big Tech, which also includes the 3 largest hyperscale Cloud providers is at the front and centre of the AI arms race, building out their own models (both proprietary and open source) and providing AI compute and Cloud-based tools for the rest of the world to build their AI services on top.

Given the breadth and depth of use cases of AI and GenAI and pace at which it is evolving, both consumers and enterprise demand has exceeded the existing compute capacity needed to serve it.

“The demand for all of these AI products and services is so high right now that we are just doing crazy things to make sure we've got enough compute to fulfill the demand.” - Kevin Scott - Microsoft CTO

Analysts are estimating a total of $1tn of AI related capex till 2030, an unprecedented ramp up in spending for a technology cycle. The YoY % change in capex tells you all where this is headed.

We have fast tracked to a point where not investing is a bigger risk given the lead time it takes to build out capacity, train the models and get them ready for inferencing projects. There is no turning back from the AI arms race, something that was echoed in the Q2 earnings calls this past week as well.

“It's hard to predict how this will trend multiple generations into the future, but at this point, I'd rather risk building capacity before it is needed rather than too late, given the long lead times for spinning up new inference projects” - Mark Zuckerberg (Meta CEO)

“When you go through a curve like this, risk of underinvesting is dramatically greater than risk of overinvesting” - Sunder Pichai (Alphabet CEO)

“The reality right now is that while we're investing a significant amount in the AI space and in infrastructure, we would like to have more capacity than we already have today. I mean we have a lot of demand right now. And I think it's going to be a very, very large business for us” - Andy Jassy (Amazon CEO)

Data centre buildouts are then leading to second and third order capex cycles - upgrading existing energy infrastructure and building out new grids to serve the additional power demand of these AI data centres, expected to push the overall power demand considerably.

“By 2030, the power needs of these data centers will match the current total consumption of Portugal, Greece, and the Netherlands combined.” - Goldman Sachs Research

Europe alone needs a $800bn to upgrade the old power grids to support AI data centres, while US utilities will need to invest billions to support higher power demands due to AI compute. Add $800bn or so on top for renewable energy capacity and you have $2tn of additional capex just to support the AI infrastructure being built out.

A classic example of supply scarcity combined with a pull forward in demand, eventually leading to overinvesting and possible oversupply in the future.

We have got to build it. However…

The economics of AI are not easy to digest

Sequioa came out with an interesting piece recently trying to estimate the required revenue for the payback on AI capex spend, opining that there is a $500bn revenue hole which keeps getting bigger as the AI spend creeps up (even after assuming the largest tech companies have $10bn of AI-related revenue).

Investors are starting to question by when and if even companies would be able to convert these invested dollars into revenues and cash flows as the current market structure and economics are not geared towards attractive returns. Notable hedge fund Elliot Management has already come out recently saying that AI is overhyped and Nvidia is a bubble.

Models are getting more expensive to train as they get larger. GPT-4 took $78mn to train, Google’s latest Gemini Ultra $191mn, while Meta’s state of the art Llama 3.1 $640mn.

“The amount of compute needed to train Llama 4 will likely be almost 10x more than what we used to train Llama 3. And future models will continue to grow beyond that” - Mark Zuckerberg (Meta CEO)

These are exponential increases in fixed costs with not much clarity on if those extra training costs are worth the marginal performance gain (and for what use cases or applications).

Then API pricing (cost to use those models) is already extremely competitive. Most independent model providers are losing money at this stage according to analysts.

Additionally, increasingly less differentiation with the one of the best models now being open source is likely to make it even harder to compete in the application layer, with everyone having access to the same (or similar) models. Commoditisation of AI products and services means low pricing power and barriers to entry where most of your profits get competed away.

The Application layer is not having an easy time either. In the decade of the Cloud, it had north of 70% Gross margins, but AI apps today have sub 50% gross margins.

Another risk that is less often discussed is how quickly the value of your equity can depreciate as every new model or GPU comes out and your capital is stuck in the last gen products or technologies which are now worth a lot less.

Big Tech seems to be well positioned to capture a lot of the value as their core businesses and products get better with AI, with almost all of their capital intensive R&D and capex projects funded with existing cash flows. But how much more are the additional future cash flows worth?

The current setup maybe has some parallels with another cycle in the past.

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We might have been here before

A $500bn revenue hole with not so good looking economics has put investors in a tough spot, and the markets are already starting to feel jittery.

Can we look at a previous comparable tech cycle to find some parallels to try to get some comfort with what is going on in the current one?

The Dot Com cycle of the 1990’s, the Internet era, saw massive investments in building out the connectivity infrastructure. Telcos and Cable companies like AT&T, Verizon, Cisco, Juniper, Sun and others spent $500bn in laying down new lines, installing switches and routers and also building out wireless networks.

Telco capex went from $47bn in 1995 to $121bn in 2000. Many small and large firms like Global Crossing and Worldcom went bankrupt as they could not service the debt they had used to fund the network buildout.

“One reason for the massive increase in the capacity of the long-haul network was bullish expectations for future demand. For instance, a CEO of Level 3 Communications, a large long-haul firm, stated that Internet traffic would double every three to four months. That didn’t happen, and estimates are that demand (in terms of volume of data, not in terms of dollars spent) may be doubling every year, which is a significant increase but is substantially shy of expectations.” - The Boom and Bust in Information Technology Investment

VCs also invested billions of dollars into companies with a lot of hype and unsustainable, unprofitable business plans as VC investment reached $98bn in 2000 from just $18bn two years earlier. A lot of capital was incinerated as many seemingly early winners of the cycle like Pets.com, UrbanFetch, WebVan and other dot coms went bust.

“At the beginning of 2000, there were between 7,000 and 10,000 substantially funded internet companies. Three years later, 5,000 of those companies had either been acquired or had closed their doors. The capital destruction amounted to trillions of dollars.” - Embrace the GenAI mania

The key here is that overinvestment led by expected demand growth for bandwidth created a glut and oversupply in just a few years leading to a price erosion for bandwidth. It has suffered massive declines every year since.

The infrastructure and service providers in the Internet era HAD to build the internet backbone, overestimating demand in the short run but maybe underestimating in the long run.

A capex cycle like this one therefore is likely to last for years. Scarcity leading overinvestment, turning into a glut which then leads to price declines is something we have witnessed before in the 1990s.

The comparable today would be inference costs which are priced as cost/million tokens (a token is a chunk of text the LLM generates or processes). Inference prices have already started to come down considerably.

“when Mistral released its Mixtral LLM in December, a flurry of inference startups began offering it at increasingly lower prices, and its price dropped from around $2 per million tokens to just 27 cents in a matter of days”

Companies like Character.AI, which was recently “Acquihired” by Google have reduced inference costs by a factor of 33x since 2022.

Then there are efficiencies in the compute intensity and data centres led by products like the Nvidia GB200 which is 25x more energy efficient than the previous gen chip. So the capex intensity and costs might further fall driven by demand fluctuations and more efficient hardware design and software.

On the adoption side, the 90s saw PC hardware and mobile handset companies (the enablers) like HP, Dell, Microsoft, Intel, Nokia etc. allowed people to get access to the Internet and increase adoption, basically starting from near zero.

ChatGPT allowed AI to break into the mainstream last year and we have both upstarts and incumbents giving people access to AI on a massive existing install base of PCs, Smartphones, Internet, Cloud and Applications.

Adoption today has gained good momentum for both enterprises and consumers, and companies are willing to invest in the space to experiment if they can create more value for their business and customers.

This might further be fuelled upwards by declining costs and oversupply going forward, which could then allow AI to penetrate smaller enterprises and individuals providing them an economically viable platform to build on top of.

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There are concerns galore, like every other technology cycle when it is still unclear if there will be enough value created and who the eventual winners might be (if any). Incumbents seem to be capturing most of the value, but just like the last cycle, new winners in specific niches and verticals might emerge that are not out there at the moment.

What we are also likely to witness is multiple corrections in the market along the way, with a lot of capital at risk being burnt and a few home runs that pull more even more capital in. Its almost like a feature and not a bug for every major technology epoch in history.

No cycle plays out exactly like the last one. But if the cost and the adoption curves continue on similar trajectories, and we can find ways for AI to deliver actual consumer and economic value, all hope might not be lost just yet.

Until next time,

The Atomic Investor

Then a couple weeks ago my suspicions were confirmed. Prime Video would start serving me ads during movies and shows, with an add-on subscription for an ad-free experience.

This made me wonder about how we have come a long way from just a few years ago when TV was being unbundled into a low cost SVoD (Streaming Video on Demand) product, with a lure and promise of an ad free experience for content on-demand. But that was in the era of cheap capital I thought, where billions of dollars worth of content was being subsidised to focus on subscriber growth and getting to scale. In Its not tech anymore, its TV , I wrote about the unbundling to rebundling reversion as SVoD was just beginning to embrace the Ads model after years of vehemently denying the need for it.

That was almost like a test case of this new (but still very familiar) growth engine for all kinds of companies, the potential of which is still underappreciated till today even after delivering profitable growth for many a firms like Netflix, Amazon, Uber, Walmart and others. Even J.P Morgan has an ads business now. (Yes that J.P Morgan!).

Today operators and investors are increasingly becoming more accustomed to the idea of using this growth lever as certain changes in the market structure have opened up a new avenue for profitable growth.

First, lets take a step back to how we got here…

Monetization of our attention is one of the oldest business models out there. This market was dominated by newspapers and print media, radio, and cable television in the pre-internet era where companies were offered a space to publish ads (ad inventory) and a platform to reach potential customers. Market and consumer research firms (the Nielsen’s of the world) would help advertisers find the target customer and measure the effectiveness of their ad spend (attribution).

But it was like casting a wide net hoping to catch the right kind of fish. I would see car ads on TVs or magazines even if I was not interested in one, but someone else reading the magazine might be looking for something similar. Plus the car company wouldn’t be able to measure the return on this ad spend with much precision.

In the post internet digital advertising world, online platforms could now aggregate demand and expand the potential reach for an advertiser. Imagine millions of Google Search or Instagram users in a city as opposed to a few thousand newspaper or magazine copies in circulation. They could also collect user data and build a profile of their interests and desires, and then track their clicks and online behaviour to further enrich that data.

For ad buyers, this was great as they could show their ads to a more relevant set of target customers. If I searched for car auto dealerships near me, then went onto to check a specific model and make on a car website, I am likely to be interested in cars or I am looking at buying one. They could also measure how many impressions their ad had made, how many potential customers had clicked on it, and then bought their product. The full circle.

As our eyeballs shifted to online platforms from traditional media, the digital ads market exploded and overtook traditional ads by 2018, reaching more than $350bn in online ad spend in 2019 (double of that in 2015).

First came Google in the early 2000’s dominating Search, capturing more than 70% of that market. Then came an upstart Facebook gaining share of attention in the social media and mobile world of early 2010’s, and together with Instagram took over the display ads market.

For platforms, user experience is paramount to show the user what they are really looking for or what is interesting for them to keep them powered on. The more engaged they are, the higher the ad “impressions” and higher the likelihood of them clicking it. For the other side of the transaction, the platforms need to be better at targeting the right customers with the right ads to provide the maximum value to their ad buyers.

Thus the importance of user data continued to increase over the years. Both data from time spent on own platforms (first party data) and from time spent on other sites and apps (third party) became increasingly valuable, especially the latter which would help ad platforms make the targeting models more deterministic. The user is a person who likes dogs, a result of a combination of first party and a variety of third party data points. A big difference from the user is likely to be someone who likes dogs - a probabilistic model.

Google and Meta were able to control most of this market with their ability to have multiple properties (websites, apps etc.) with a growing user base and increasing time spent on them (share of eyeballs), combined with integrating first and third party data across platforms and apps to have the best in class targeting. Remember that instance when you were discussing a trip with your friend and you start seeing ads from Booking.com right after?

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The ATT drama

In 2021, Apple played a masterstroke when it introduced ATT (App Tracking Transparency) in the name of user privacy, which was essentially a way to kneecap its biggest rivals in tech and shift demand towards its own newly launched iOS ad inventory.

With ATT, iOS users could now opt out of personalized ads on their devices. Then apps on iOS devices had to prompt users to “opt-in” again separately to allow tracking of user data (interests, demographics, in-app behaviour).

This critical third party in-app data was used by ad platforms for attribution, personalization and better targeting before. The supply of it was now basically cut off on one of the largest install bases in the world (2.2 billion devices according to some reports).

Advertisers could not just rely on Meta and Google anymore as they saw their ad performance declining. Targeting models suddenly became probabilistic, which meant more R&D investments from the platforms to improve their predictive algorithms (more on it in Its an imperative not an option).

First party data increasingly gained prominence as a result, as advertisers started to move towards other mediums such as email, gaming consoles, connected TV, streaming, and other two sides platforms. This redistribution of ad budgets has shifted the market structure by giving first party data owners an added advantage.

But data is not the “new oil” as it is repeatedly mentioned. It is the “sand” and you need to aggregate it in millions in a meaningful way to get any value out of it. Some companies have realised this and have started to build their own ads businesses to achieve different types of goals.

It’s understandable that any company with sufficient size and scale of data would want to build an ad network. Ad networks are highly profitable, generally running on very thick gross margins and mostly monetizing content and product surface area that already exists. So as the norms for data collection and aggregation change, favoring first-party contexts, the companies that can package together their first-party customer data to attract advertising revenue will do just that. - Eric Seufert, Mobile Tech analyst

Amazon: Capitalizing on the power of The Everything Store

Amazon, the modern day everything store is now one of the highest traffic websites in the world. A proper two sided marketplace connecting buyers with sellers with hundreds of thousands of SKUs across every category. And it has very relevant first party data. You search for what you really want or are looking for. You click on recommended products that might be interesting for you building a nice profile of what your interests are like.

Just as Google offers paid placement for web pages and links, Amazon started offering ad inventory and paid placement using this first party data to sellers who want to place their products on top when you search for something, and target relevant users with their product pages.

Amazon ads started attracting billions of dollars in ad revenue as “retail media” gained traction, and is are now bigger than the entire newspaper ads combined.

A $50bn business with AWS like margins (as opposed to low single digit retail margins) - something that is not usually discussed when talking about the company. A perfect example of monetizing first party data at scale for higher profitability and growth in cash flows.

As annoying it is for a user to now pay for ad-free viewing, it makes total sense for them to repeat this with Prime Video with more than 150mn users and over 100mn Fire TV devices (on top of Twitch, the game streaming site with 35mn users).

Netflix: Reaccelerating growth

Netflix was firmly in the no ads camp for very long until its user growth started stalling in key markets. Then it finally launched a cheaper ad-supported plan in 2022 to gain back its user growth, also allowing it to expand into new markets and customers (at this lower price point).

"Our ads membership grew 65% quarter on quarter (after rising nearly 70% sequentially in each of Q3’23 and Q4’23) with over 40% of all signups in our ads markets coming from our ads plan” - Netflix 2024 Q1 earnings

Netflix will now stop reporting subscribers numbers (the key metric the market tracks), and will start reporting user engagement instead. Goes to show it is not only important how many users you have, but in the attention economy it is key to measure how engaged they are.

Uber: The path to profitability

Uber, the ride sharing leader started offering ads on its app properties (sponsored listings, in menu ads, journey video ads, cartop ads) to small and large enterprises in 2019. Ad revenues have grown from $140mn in 2021 to $650mn in 2023 with expected ad revenue of $1bn in 2024.

The high margin ads business (already ~15% of annual cash flow) contributed meaningfully towards it finally delivering operating profits and free cash flows after years of struggling to get there.

“On average, it’s about two minutes that the consumer has the app open. So, it’s 100% share of voice in those two minutes, which is so much more than an advertiser sees on a social platform. And therefore, the engagement rates are super high, north of 3%. So, it’s really powerful – especially based on the data that we have about the consumers and how we can combine Rider and Eats data to really make ads relevant.” - Uber Advertising GM

Every company has a hidden ads business now?

Other large brands like Walmart (with $3.4bn in ad revenues) and ABInBev are using first party data across their brands and platforms to offer customer preferences and consumption behaviour data to their advertisers.

JP Morgan, with the first party spending data it has on its 80mn customers is now building an ads business to offer ad inventory on its banking apps and websites.

So every company with some share of eyeballs and valuable first party data has a hidden ads business now? Most of them think it is the case and we’re increasingly seeing more success stories of this growth avenue.

A strategic move by one large player and the increasing relevance of first party data shifted playing field from a two player dominance, and has reduced barriers to entry into the ads market. AI tools are reducing them even further by making ad targeting more effective, autogenerating ad campaigns and improving attribution models.

Companies are starting to realise the potential of the new growth engine. Investors should not sleep on it either with opportunities around harnessing first party data lurking around the corner.

Until next time,

The Atomic Investor

They were obviously pointing out to the rapid commercialisation of football and football assets over the last decade and the ever increasing interest these assets are attracting from investors. While there has always been a billionaire fan trying to get their hands on their favourite club’s ownership, the profile of club owners is changing as we see institutional capital and sovereign wealth getting involved, bringing a whole host of changes to the clubs’ and the respective leagues’ inner workings.

I get it. It is not easy as a fan to digest so much going on in the shadows. Imagine being an Everton or a Nottingham Forest supporter and seeing your club taken 5-10 points off them because of breaching financial sustainability rules, putting them close to or straight into the relegation zone. None of this for their performance on the pitch, but troubles off it. I’d be fuming as well.

Even the largest clubs in the world like Football Club Barcelona , Manchester United and Juventus are not pristine as they seem and have faced a multitude of problems recently, stemming from bad financial and operational decisions taken off the pitch. The latest victim of that is Reading FC (one of the oldest clubs in English football) which was docked 18 points, relegated to the third tier and is in a sorry state due to overspending under the current ownership.

We’re increasingly reading these tales of fines, restrictions and other similar impositions at football clubs, both big and small. Football leagues and associations are now suddenly having to clamp down hard on unsustainable operating and financial models at clubs as a wave of foreign and institutional money floods the sport.

The fabric of football, like it or not, is rapidly changing as we try to get our heads around on what it means to run a modern day football club.

A new era of excesses

Football entered a new era when the broadcasting rights market started warming up to European football and Sky signed a landmark GBP 300mn deal with the newly formed Premier League in the UK in 1992.

Premier League 1992 launch video. A whole new ball game.

Spreading of the sport globally got hundreds of millions of fans onboard and allowed the top leagues and its member clubs to get bigger and richer by attracting commercial deals, sponsors and other growth avenues.

The best players now could be attracted to join the biggest and the most successful clubs, which further fuelled the growth in global fanbases. The pace of this ascent was astronomical. In 1992, the combined revenues of the English First Division clubs (the Premier League) were GBP 170mn. In 2014, roughly two decades later it had surpassed GBP 3bn, growing 18x in that period.

The amount of money clubs spent on building their squads ballooned as well as a result. The top 5 European leagues spent EUR 280mn combined in the 1992/93 season, more than EUR 1bn 10 years later and a mammoth EUR 5.8bn in 2022/23.

The last decade has seen an acceleration in the trend and pushed this market into extravagance and excess.

Transfers might not impact the club in one particular year as the costs are spread (amortised) over the length of the contract. Player salaries, however, are real annual expenses that have a direct impact on the financial health of the club. Here too, the trends are just preposterous. Total salary expenses in the top 5 leagues almost doubled across the board and average salaries more than doubled in the last 10 years (2013/14 - 2023/24). It was a very different kind of wage inflation in football.

Another insightful chart from Arctos which shows that wage growth in the top 5 leagues has outpaced revenue growth considerably over the last decade. Business 101 says this might be unsustainable.

Throwing money at success, and now at survival

As new money led by a different league of investors has started pouring into football, the game has seen some structural changes. Success on the pitch is increasingly being defined by the ability to bring in commercial success.

The three relegated clubs (in 2021/22), Norwich (£118m), Watford (£87m) and Burnley (£92m), were also all among the six lowest spenders with each spending significantly less than the league average of £182m - Spencer Stuart, Moving the Goalposts

Players are also being lured by paying them ridiculous sums of money and signing other off the balance sheet deals.

One could argue that in the modern day there are more way to monetise these footballing assets (merchandising, sponsorships etc.), so investors can afford to pay these amounts. But with that the delicate balance between the commercial and footballing side of the game is tilting increasingly towards the former as the latter takes the backseat for a number of new club owners.

After all, the richest clubs get the best players, have the best academies and can afford to provide the best environment for talent and youth to develop - all of which are important ingredients of success on and off the pitch.

To stay competitive, unfettered spending beyond the clubs’ means is now becoming commonplace even amongst smaller clubs. It is a complex situation where on field success is closely tied to business success and vice versa, putting a massive strain on the financial and operational health of those clubs.

Most new investors come in with a big equity cheque or take on large amounts of debt to fund player transfers, stadium buildouts and other developments at the club to fuel revenue growth and ensure survival (on which most of the commercial success hinges), essentially spending more than they make in most cases. Chelsea FC for example, made GBP 534mn over last year and spent more than GBP 1.1bn led by the new ownership of Clearlake Capital and Todd Boehly.

Examples of Reading, Everton and Forest are other cases in point of this new investor playbook of spending for commercial success and footballing survival, where misaligned decisions taken in the boardroom stand to decide the fate of the club and its future.

Fans, as a result are increasingly becoming disillusioned by all of this happening, putting owners at the risk of losing the most important asset in football. Not the players but the fans that make football what it is (and also drive all of the commercial success which investors so eagerly look for).

Can the new regulations help?

UEFA and the domestic sporting leagues recently introduced new financial sustainability rules (Profit and Sustainability rules or PSR by the Premier League) to rein in uncontrolled spending by teams and force them to operate within their means.

The new squad cost ratio restricts clubs to exceed their squad costs above 70% of revenues by 2025/26 season.

But there’s a laundry list of who’s who of clubs that currently do not comply with the 70% number.

The wages alone in the top 5 leagues have flirted close to the upper limit for squad costs and have even exceeded that in some countries over the years.

The situation is even dire at lower leagues where just the wages alone exceed the 70% number, and go above and beyond 100% of revenues.

Both UEFA and Premier League have put in additional rules in place regarding club profitability. Clubs cannot show a cumulative loss of more than GBP 105mn in the previous three reported financial years (the PSR) and only 15mn of that 105mn can be funded from the club’s own earnings. The rest of it (90mn) would need an equity injection by the ownership or new investors, implying that first you cannot continue spending more and report losses, and then if you do it will be funded by new equity capital, not the club’s existing balance sheet (which is likely to further increase the influx of new club owners with varied interests).

But the average club in the richest Premier League made a pre-tax loss of GBP 31mn over four seasons until 2021/22. Lower division clubs fare much worse with hugely negative operating cash flows.

Now a new independent football regulator will be formed in the UK with the powers to remove “unsuitable” club owners with the track record of financial mismanagement and no involvement of fans in key off field decisions. This might be a step in the right direction but it does not solve all of the problems.

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Club ownership needs a new operating model

As the game, the product, the fans and regulations continue to evolve, clubs need a better operating model to thrive in the modern day. There are various examples today where new club owners are already implementing the following ideas.

Get the management structure right with competent people in the right roles:

A right management structure for the club is key for both the football and commercial side of the business. The appointments do not end at the head coach and the staff running the team. A club today needs competent leadership in charge of football operations with the knowledge of the modern game along with astute management and operational capabilities. The right board composition is also key to take objective strategic decisions aligned to the owner’s and the club’s plans.

Clean up broken finances:

New club owners need to clean up broken finances of their clubs first instead of doubling down on spending. Finding different balance sheet solutions involving the assets they own (club facilities, stadium, digital/media assets etc.) can unlock much needed capital that can be deployed elsewhere. A well defined transfer policy with rules around player wages could also allow them to bring down the squad costs. Operational sophistication is key to manage overall costs and expenditures and can go a long way to save up much needed cash for mid-sized and smaller clubs.

Create a vision aligned to the time horizon of the fans and your own thesis:

Owners need to define a clear vision for the club alongside their fans which is aligned to their own investment thesis. Football clubs do not need short term fixes and are rather long-term investments that take years to create value for all parties involved. A modern day fan desires instant results and therefore a plan that gets everyone on board is critical.

Formulate an optimal capital allocation strategy for your goals:

A top down capital allocation with a plan not for the next transfer window or financial year, but several down the line. Focus on not just getting the most commercially valuable players, but the ones that can add the value to the squad to get the desired footballing results. Then, a proper plan in place for off the pitch investments including club infrastructure, youth/talent academies, technology/R&D which can lower future capital expenditures and squad costs.

Make your fans an integral part of the governance structure:

Getting the fans involved in key club decisions is necessary as a highly engaged fan can not only lift the performance on the pitch but is also more commercially valuable. New governance models where the fans (or registered fan clubs) can have their say could be an interesting way for owners to stay accountable and stay on track on the long-term plan, and also keep the fans happy.

Football is rapidly changing and so is the business of it. As new owners and ownership models take helm at the centre stage, the need for operational excellence at football clubs is getting even stronger.

After two decades of unstoppable growth, Financial Sustainability, Integrity and Fan Engagement are the three pillars around which we are likely to see this space developing over the next decades.

After all, the beautiful game must stay beautiful.

Until next time,

The Atomic Investor

Different pools of capital, including pension funds, sovereign wealth funds, insurance companies and trusts have continued to increase private market allocations over the last two decades, chasing a steadier, diversified and less correlated return stream, which according to KKR, Hamilton Lane and various other sources has outperformed public markets over different economic cycles.

Private Market’s returns has become a contentious and a hotly debated issue over the last few years which is understandable as the investment universe becomes more barbell like - with passive stock and bond funds and ETFs at one end combined with private market allocations on the other. Active public market asset managers have lost considerable share to their private market counterparts, and it is not surprising to see managers and investors on this side of the debate to come out fighting and question private market returns.

Recent research on the topic uncovers meaningful insights to better understand private market returns and performance.

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The sum is greater than the parts in PE

One rarely spends much time thinking about portfolio management when looking at PE. Dealmaking is the holy grail and each deal in a fund is scrutinised and revered before investing, during the holding period and after exit. GPs look at deals in isolation most of the time by gauging the return and value creation (alpha) potential for each investment opportunity.

But a recent paper by Gregory Brown, Celine Fei and David Robinson suggests that portfolio construction, including deal size and deal timing has outsized impact on fund level returns, more than individual idiosyncratic deals themselves.

They looked at how GPs allocate capital inside a fund using a dataset of almost 6,000 portfolio companies for more than 450 buyout funds from 1999-2016 vintages. Ranking holdings from largest to smallest as a % of fund size, they found out that the largest allocations on average have the lowest returns, while the smaller allocations produce the best returns. Returns increase as they go from larger to smaller allocations. This comes across as counter-intuitive at first. Normally, fund managers would use position sizing to produce the best returns by allocating the most to their best ideas. But apparently not in private equity.

Going deeper, the researchers found out that average returns to average risk (or risk-adjusted return like the Sharpe ratio) was almost constant. GPs were essentially controlling for risk by betting more on less riskier (and lower returning) investments or betting the biggest on the “safest bets” rather than on the “best bets”.

The lower volatility that is part of the allure of private equity and is desired by institutional LPs is an important component of GPs’ portfolio construction strategy and therefore a driver for better risk adjusted returns as proclaimed by the PE firms.

On timing of capital deployment, they found out that GPs deployed on higher return (and riskier) deals earlier in the fund’s investment lifecycle and moved on to the safer bets later, which might suggest kind of a hedging strategy to cap risk as the IRR clock is running its course.

The icing on the cake, however, was the return attribution to skill vs other factors. They discovered that at the fund level, 40% of overall variation between fund returns could be attributed to GP skill. The same number dropped to a meagre 4% at the individual deal return level (and was more than 90% to luck or other factors not in the GP’s control at the deal level).

This provides strong evidence of PE returns being driven largely by the GP skill in portfolio selection, construction and timing and not just by individual deal success. To put it simply, PE fund managers can have blockbuster deals that outweigh the rest of the fund but if they do not size their cheque and manage risk appropriately, it can the dampen performance of the fund significantly. This is hardly spoken about enough to give its due merit. Quite fascinating indeed.

Its all about the Power Law in VC

Venture Capital returns have a different profile. Empirical studies have found that there is high persistence in performance of VC managers. i.e success begets success.

This has driven LPs to look at past performance as an indicator of future success in VC investing, and made manager selection an important factor contributing to the decision to invest. The performance dispersion in the top and bottom quartile funds is the highest in VC than any other asset class, which further increases the importance of the jockey (the manager) more than the horse (technology, sector, companies).

In addition, returns in VC follow an extreme power law distribution - the best bets are exponentially worth a lot more than the all the rest of the portfolio.

This suggests that they should constantly be looking for home runs and bet the house on them. But given high risk of failure (losing your money) in VC land, investors spread their bets across opportunities and increase their allocations as the company is de-risked, i.e matures and reaches the subsequent stages of its lifecycle.

How much of the “home runs” can be attributed to skill? There is not much empirical data on this but on the surface it seems like luck goes to play an outsized role on you hitting the exponential return goldmine.

Other studies have shown that asymmetrical information (knowing something about the about the company, industry or the technology that the average VC does not know) and the ability to win competitive deals differentiates fund managers from the rest of the pack.

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Private Market outperformance is likely to continue being a contentious topic as the debate on the veracity of its returns lingers on. I tend to err on the side of caution and give merit where it might be due and caveat the hubris that comes with it at the same time. Private markets are increasingly becoming a larger proportion of institutional portfolios, which is likely to follow suit on the retail side as well.

Understanding the role they play in the portfolios and how they achieve what they do seems like a better use of time for all, be it public/private market managers, armchair experts, enthusiasts and the rest of the finance world.

Until next time,

The Atomic Investor

To kick things off, BlackRock, the public markets asset management behemoth with more than $10tn in AUM bought Global Infrastructure Partners (GIP) in a deal valued at $12.5bn, adding more than $100bn in Infrastructure assets to its private markets business. Then General Atlantic, the Tech focused Growth Equity investor bought Actis, a specialist sustainable infrastructure asset manager, to expand into Real Assets - investments backed by physical assets such as real estate, power and energy related assets, digital infrastructure (data centres, mobile towers, fibre networks) and transport.

These two and other notable deals last year like CVC-DIF and Bridgepoint-ECP strongly highlight the very evident shift away from the traditional PE business. As I mentioned in Not Just PE Anymore, a diversified stable base of fee earnings is the north star for asset managers that has been guiding their recent activity in the markets in a bid to achieve scale faster and command a better valuation.

To further drive the point home, Arctos published an insightful chart showing the correlation between the distributable fee earnings (DE) multiple and the percentage of Fee paying AUM from traditional Private Equity for publicly listed asset managers.

The trend has driven managers to diversify into private credit and insurance, which has been all the rage recently. But after the BlackRock-GIP deal, which makes BLK the second largest infrastructure investor by AUM, the Private Infrastructure asset class might have turned a corner on its way to become an integral part of investor portfolios.

A Transformative deal

The transaction has the makings of another transformative deal for BlackRock and comes on the back of BlackRock’s ambition to capture share in the high growth asset classes outside the traditional equities and fixed income realm, as it tries to replicate its success in ETFs and active funds at scale in private markets.

“Our acquisition philosophy has always been about growth. With GIP, I truly believe that this will be the case again,” - Larry Fink, BlackRock CEO

Before the deal, BlackRock’s private market assets had ~$50B private Infra. AUM. Post closing it will get 100% of management fee from GIP’s funds and 40% of carried interest from future funds, tripling its current AUM (by adding $106bn of GIPs assets) and doubling the fee revenues.

But its not just about the numbers. GIP has established itself as one of the largest infrastructure investors with a wealth of experience managing and improving hard to operate assets like airports, green energy, pipelines, data centres and the like.

Both Larry Fink and Adebayo Ogunlesi (GIP Chair and CEO) believed that private markets were entering a consolidation phase where size, resources and deep expertise would be needed to get access to the best deals.

“BlackRock and GIP will be able to connect our clients with bigger and better opportunities while also accelerating growth, diversifying revenues and generating earnings for our shareholders” - Larry Fink

As we’ve mentioned before, scale is everything in asset management. Even more so when it comes to Infrastructure, as this chart from Arctos shows. The top 5 Managers command more than a third of the total AUM raised over the last decade.

BlackRock can use their presence in both retail and institutional channels to now distribute Infrastructure funds unlocking the next phase of growth. On deals where they were competing before, GIP and BlackRock can now combine their resources to win the access and operate these assets.

It was a shared vision of Fink and Ogunlesi, who met first when they both worked at First Boston, that Infrastructure would be the fastest growing part of private markets. There are multiple tailwinds currently helping their vision get closer to becoming reality.

The changing nature of Infrastructure

Infrastructure investing was considered a low risk, stable asset class for a long time delivering predictable returns over a cycle. There was little competition for these assets as access to deals and room for growth was limited. Most large infrastructure assets were publicly owned and operated and are generally highly regulated. More involvement of public entities is an added factor that makes these assets harder to manage. But these challenges slowly started turning into opportunities.

Higher debt burdens: Debt burdens of governments across the world kept climbing higher, restricting their ability and capacity to invest and maintain infrastructure assets.

A large funding gap of trillions of dollars (estimated to be $15T by 2040) has opened up which led to whole or parts of these assets being divested to private investors. Private Capital is now increasingly being preferred for new greenfield infra. projects as well, where pension funds, Private Fund Managers and sovereign wealth funds of countries with surplus cash are stepping up.

Increasing investment needs: New infrastructure assets related to energy transition and green energy, digital infrastructure and supply chains has unlocked a massive amount of capital need and brought it to the forefront.

• Decarbonisation: The global economy needs an estimated $9.2 trillion in annual average investment in physical assets (energy transition and green energy generation) to achieve net-zero emissions by 2050, and Europe alone needs almost $2 trillion a year between 2031 and 2050 to meet its targets.

• Digital infrastructure and connectivity: The world still needs more high speed connectivity and compute infrastructure in the form of satellite and fibre networks, towers, data centres as we move onto the next technology epoch. Brookfield’s 49% stake in Intel’s new semiconductor chip factory is classic example of how the scope of digital infra. investments is only expanding.

• Supply chain resiliency: The global supply chains and trade networks are being reorganised for resiliency and security and the nearshoring/friendshoring tailwind is boosting private investments in manufacturing and transport infrastructure. For example, Mexico and Canada have overtaken China as primary suppliers of goods to the U.S. Nearshoring is expected to bring “billions of dollars of export growth to other LatAm countries, including Argentina ($3.9bn), Colombia ($2.6bn), and Brazil ($7.8bn).” Other regions in Asia are experiencing similar trends.

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Active asset management: Infrastructure investments playbooks have been updated to include active management of the business and operations to create more value and increase the return potential. GIP shares an interesting example where it reduced the airport security times at the Gatwick Airport by using bigger trays, so that flyers spend more time in restaurants where the airport owner (GIP) gets concessions on every purchase.

Additionally, these assets have long been natural monopolies which creates sufficiently high barriers to entry making a stronger case for investors to get in first. Being previously owned by governments brings forth new operational value creation opportunities - remove inefficiencies, realign capital and resource allocation towards growth and increase focus on customer service via digitalisation and technology led improvements, amongst others.

The changing nature of infrastructure - from low risk stable asset class to a dynamic higher return potential with more room to actively manage operations to create value is attracting investors to allocate a larger portion of their assets to this asset class.

Private Infrastructure AUM increased at a 20%+ CAGR from 2016 to 2022, crossing the $1 trillion mark. While lack of low cost financing has slowed down fund raising and dealmaking recently, the AUM is still expected to keep growing and cross $1.5T over the next 5 years. Regional funds are inviting the most interest and capital for both sustainable and essential infrastructure investments. Europe presents a better opportunity set, where the AUM is expected to grow at the highest rate in the medium term.

Two recent megacap fund closes - Macquarie $8B European Infra. fund and KKR’s $6.5B Asia Infra fund are clear signs of strength in Real Assets, where the interest in and demand for the asset class is now getting real.

Until next time,

The Atomic Investor

I tried to capture the ones that caught my attention in this post. Charts to highlight the data and the narrative, accompanied by some questions to ponder upon for this year.

Before we dig in, a quick update. The Atomic Investor turned three! More than 75 posts with countless drafts and ideas that never made the cut. And I have enjoyed every bit of it.

For those who have been reading and following the updates on here, thank you! If you like what you saw, share it with your friends.

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If you are here for the first time, feel free to hit subscribe 🙂

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It was a good year for the stock markets

Not just good. It was a great year for the markets. After one of the worst drawdowns in 2022 driven by interest rate hikes and inflation, the markets came back to life pretty much everywhere.

The S&P500 was up by 26%, Nasdaq100 55%, Euro Stoxx 600 15.5%, Emerging Markets ex-China 19% and Japan 28%. They surged even higher when major central banks started talks of rate cuts and pauses in rate hikes.

It was hard to lose money if you were a passive index fund or ETF buyer in these main indices. Except China, which is still dealing with the government crackdown and geopolitical tensions, leading to foreign investors rushing for the exits.

Interestingly, Japan is attractive for both public and private market buyers again after a long, long time. The regulators have pushed for corporate governance changes over the past years, making the markets more shareholder friendly, forcing companies to improve capital allocation policies, reduce cross-holdings and return more cash back to shareholders.

The main public markets index is only 0.4% away from its peak going back 20 years ago and 13% away from its all-time high during the 80’s bubble.

Private markets deals have also continued to increase as investors look for high quality assets at attractive prices.

A great year for the markets everywhere even in the face of a whole lot of bad news and an impending recession.

Can this run continue into this year? A lot of the good news (lower inflation, rate cuts, no recession) seems to be priced in already, and what if that good news is delayed or is not as good?

Magnificent 7 continued to deliver big time

The Magnificent 7, the biggest tech stocks - Apple, Microsoft, Google, Meta, Amazon, Tesla and Nvidia, were on fire last year and drove the markets higher. Nvidia’s terrific Q2 numbers where demand for GPUs was through the roof made the markets come to terms with AI’s potential impact on Mag 7.

It was not just hype. These companies were at the forefront of investing in AI and had the capital and labour resources to outcompete everyone else. Their positioning in the compute heavy infrastructure layer and existing user base in the application layer is a natural moat for them. Most of them were therefore solving for the model layer, investing in/partnering with startups or building them in-house. They, unlike many other large corporations, were shipping at startup speed. A lot of ideas combined with impressive execution. The markets had no choice but to reward them with higher share prices as they became the best publicly listed AI plays.

Higher valuations than the rest of the market are also backed by much higher free cash flows and earnings.

Now that a lot of the future gains in revenues and earnings might already be reflected in the stock prices, there is very little room for disappointment. Can the Mag 7 keep beating high expectations and build on the progress made during last year?

Inflation came back to down to levels that allowed central banks to take a breather

The biggest story of 2022 that continued into 2023. “The great inflation” as everyone was calling it roiled the markets and the economy as central banks led one of the steepest rate hiking cycle in history.

As the impact of it trickled through, labour market cooled down, wage inflation stabilised, energy and commodity prices came down and supply chain pressures eased. Inflation in the developed world was on its way back to the target levels.

Central banks started hinting pausing and even cutting rates in 2024 giving a major boost to both stock and bond markets. The “soft landing” scenario, bringing inflation down without pushing the economy into a recession, started seeming like a reality. 85% of the economists were predicting a recession in 2023. No one knows anything.

But supply chain pressures might already be coming back to haunt us. Container shipping rates for routes from Europe to Asia spiked 173% in December as ships started diverting away from the Red Sea due to conflict in the region.

Have investors positioned themselves for lower inflation and rate cuts prematurely? Will “higher for longer” become “higher for even longer”?

Dry powder continues to remain dry

It was not an easy year for Private Equity. Higher rates made financing very expensive, while economic pressures posed another challenge for valuations and growth for portfolio companies. Dealmaking was tepid most of the year which made exiting existing investments tough.

Dry powder, the committed capital not yet called for investing, is now close to $4tn and continues to increase.

But lack of exits has made distributions to LPs scarce, the rate of which is down to 11% from mid 30’s before 2022. Companies are being held for longer, which is forcing PE firms to undertake a whole host of new measures to keep holding onto them or extending their runway.

They have a lot of work on their hands to kickstart the PE flywheel again.

Will PE dealmaking recover in 2024? Will GPs adapt their approach to operate in a higher rate environment or continue to play the waiting game for rates to come down?

Gen AI takes a big leap forward

Some media publications named LLM the word of the year. Maybe rightly so. Generative AI took a big leap forward last year as AI became a mainstream phenomenon. Enterprises across the world started charting out their Gen AI plans. The world realised its potential and its implications on work, life and society in general.

Investors, including Big Tech, continued to fund startups building in the space, with some massive cheques being written for OpenAI, Anthropic, Mistral and other LLM providers.

New, improved models were launched by most companies involved in the space. AI progressed on an accelerated timeline. Expectations are now riding high, especially for the largest firms like Nvidia which crossed a trillion dollars in market cap last year.

However, generalist chat bots have lost some momentum after the initial spike in demand. Consumers and enterprises might now be looking for applications and tools that address specific problems.

Will business models mature to convert ideas into sustainable revenues and profits? Will new players emerge that can capture/create differentiated value?

Weight loss drugs - The Gen AI of healthcare

GLP-1 drugs like Wegovy (Novo Nordisk) and Zepbound (Eli Lilly) were all the hype last year as they showed great efficacy against obesity and diabetes and related co-morbidities. They are almost like the Gen-AI of healthcare, with significant implications on human behaviour and society. These drugs could also be potentially used to treat addictive behaviours, extending their impact from consumer foods and beverages to alcohol and gaming industries.

The markets started to price in winners and losers from this breakthrough with GLP-1 drugmakers gaining as much as Magnificent 7 or AI stocks, while fast food, food retailers, beverage manufacturers, Insulin pump makers heavily underperformed the markets.

How fast will these drugs penetrate the U.S and the rest of the world? What first order-second/third order impacts could this have given the expected changes in human behaviour?

We’re not over travelling yet

2022 was all about “Revenge travel” post the pandemic. 2023 saw international travel fully recover to pre-pandemic levels as the world still kept travelling for tourism and live events.

Travel stocks did exceptionally well too as companies posted a recovery in their numbers with improving outlook for next years.

As extra consumer savings come back to pre-pandemic levels next year (expected H1 2024), do we still keep travelling as much as we have over the last two years?

Remote work is here to stay

A contentious topic for employers and employees. A new model of work was put to test during the pandemic and there are still debates on whether it worked or not. Employers and employees have different ideas and might have to meet in the middle on this.

The pandemic tilted the balance of power towards the workers which is now reversing again as the economy slows down again and unemployment starts to pick up.

Will we ever go back to full-time office work? The trends suggest otherwise.

Guess who’s back, back again

And finally, something that was a star of 2020-2021 ZIRP bubble came back strongly this year. Bitcoin was one of the best performing assets of the year, racking most of its gains after the Fed’s rate cut hints.

The impending approval of the Bitcoin ETF in the U.S also kept a floor on Bitcoin prices.

But what does the future hold for Bitcoin and crypto? It is no inflation hedge, nor is it alternative gold or a currency replacement. Then what is it?

A very eventful year, to say the least and a lot to look forward to for 2024. What are we putting our moneys and our mouths on?

Until next time,

The Atomic Investor

No, this is not a parody but an actual video released this past week by Blackstone called “The Alternatives Eras Tour”. Maybe its the worst take on today’s pop culture. Maybe its just the sign of the times in Private Equity.

PE or Private Equity has almost turned into a misnomer these days. As the video makes it very clear, the largest firms in the business very proudly call themselves “Alternative asset managers” now. They are multi strategy, multi asset private markets investment solutions providers. Yes it is a mouthful. The frequency of mentions of the traditional PE business have increasingly declined as it becomes a much smaller portion of their overall AUM.

It is not just about PE anymore. The “Alternatives era” is being defined by other private market asset classes - credit, real assets (real estate, energy, infrastructure) and now insurance.

AUMs of the largest alt. asset managers paint a very clear picture here.

Incremental inflows are also being dominated by these other asset classes.

The greatest demand today is for private credit solutions, as Steve discussed. And our credit, insurance and real estate credit businesses comprised over 50% of total inflows again in the third quarter. - BX Q3 earnings call.

Apollo, for example, is now more of an originator of credit and retirement services provider (annuities, insurance etc.)

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Scale is everything

The rationale behind the shift towards these other asset classes is not that hard to follow. First, as I alluded to in The Magnificent Seven of PE, the largest PE firms (I still call them that) turned into publicly listed corporations to achieve scale, which in an asset management business is more so about growing the investable asset base than investing itself.

This scale can be achieved not only by adding depth in one asset class (exposure to different regions in PE for example) but also by adding breadth - products across asset classes via opportunistic M&A and organic fundraising.

Second, once you go public, to command a better valuation the markets demand your earnings be more predictable and stable. Traditional PE, with its two components - management fee and performance fee can be quite cyclical. The drawdown fund structure - raise, invest, operate, sell, repeat can be impacted in a lot of ways in a market downturn, as we all saw recently. But being a diversified manager allows you to raise capital and find deployment opportunities in all kinds of environments. Therefore, managing more capital via longer term and “perpetual funds” lends stability and predictability to their earnings, with which they can reinvest in the business and also return more capital to shareholders.

As LPs move towards investing beyond traditional buyouts, the firms see this as an attractive opportunity to become a “one stop shop” private markets manager to both increase the wallet share from existing clients and penetrate new client types at the same time.

But the underlying growth driver in achieving even larger scale and entering a new growth phase is the replacement strategy.

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The replacement strategy

Institutional investors like pension funds, insurers and other investment companies for a long time used the textbook 60/40 approach to match their duration and return targets, which can be noticed in this snapshot of their allocation from 2001.

PE firms have been following a replacement or replication strategy ever since. They looked at the public equities portion of pension funds (for example) and provided a private markets solution for it - long term funds with less volatility and smoothed returns, with more control and less correlation with public markets. Everything you need for better risk adjusted returns. PE allocations therefore started replacing a portion of actively managed public market equities, going from ~5% allocation to higher than double digits in a decade. Then Indexation and a shift towards passive in listed marktes accelerated this creation of a barbell of cheaper passive products on one hand combined with alternative assets on the other to target lower correlated return streams.

Something similar is taking place in the world of fixed income investing. Private credit was mostly targeted towards replacing different forms of high yield credit originated by banks - levered loans, direct lending, asset backed financing etc. But now PEs are taking this “debanking” a step further and calling it “fixed income replacement” targeting the big chunk of institutional fixed income portfolios in other investment grade fixed income securities or fixed income funds.

We have, to date, as a financial press and as an industry, talked about private credit as if it meant to be -- if it is levered lending, sometimes called direct lending, was private credit. Let me tell you. This is a fraction of a fraction of what debanking will produce. This piece of the business of lending to buyout sponsors, sometimes it's a very good business.….When I talk about private credit, I'm really talking about the secular change as a result of debanking. I start with the notion that everything on a bank balance sheet is actually private credit. What we've seen so far and what the press is focused on is levered lending, which, as I said, is a fraction of a fraction. I think we're going to be talking about this for the next 10 years. And the vast, vast majority of what we're interested in private credit is actually investment grade. - Marc Rowan, Apollo CEO

That means going from a ~$2 tn market just in U.S and Europe to substantially larger ~$15tn investment grade bond market.

You could use a similar lens to view other specialist investment strategies in Infrastructure, Climate linked, Renewables, Energy transition etc., where they are replacing traditional public sector funding by institutional capital.

This replacement/replication has expanded to Retail (for now only High net worth) portfolios which traditionally had a very low exposure to private markets. $140 tn+ worth of investable assets globally, some of which will increasingly be replaced or replicated by private market products.

As you know, I've said publicly, certainly, for high net worth families, family offices, I think they will be 50% plus alternatives over the next 5 years, and we're seeing that kind of uptake in traction. - Marc Rowan, Apollo CEO

What other large investable asset pool could this strategy have been applied to?

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PEs ❤️ Insurance

The global insurance asset pool is more than $40 tn and continues to grow. A majority of these assets are invested in fixed income securities.

This structurally checks a lot of the boxes that PEs like to see. First, it is “permanent capital” in a way. Insurance redemptions are scarce and people and businesses rarely stop paying their premiums for life or property/casualty insurances. Plus there is a huge reinsurance market for insurers to take risk off their books. And all these assets need to be invested to generate a return to match future expected payouts.

Alternative managers have been now investing in insurers to set up their insurance operations where they get a fee to manage these assets. Then they can acquire more blocks of reinsurance assets and invest them to earn a return. As long as the cost of servicing liabilities (payouts) is lower than returns, they can earn the “spread”, adding an additional layer of earnings via the insurance segments.

Since 2021, almost 15 alternative asset managers have entered the space and acquired almost $800 bn in assets. Fee revenues is not the only play here. PEs can replace a portion of insurance assets invested in public fixed income and equity markets with private market products and earn a higher spread. Additionally, the insurance assets can help other areas of the firm reach scale much faster (like Private credit) without them having to go for a fundraise, since majority of insurance assets are invested in fixed return securities and funds.

One analysis found that US PE-backed insurers generated 62 basis points (bps) higher investment yield than the industry average. Within three years of acquisition, 80 percent of these insurers had increased their allocation to asset-backed securities (primarily collateralized loan obligations), and over half of their investments were in private loans (compared with 37 percent for the industry)

KKR last week acquired the remaining 37% of Global Atlantic and has created a new operating segment to report its insurance related earnings. This love for insurance assets and operations seems set to continue for PEs.

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Its different than it was a decade ago. The asset classes of the past have matured significantly. The largest firms have achieved scale in multiple product segments and continue to build new ones. Fee earnings are more stable, albeit with a very different profile.

As non-traditional PE continues to contribute a larger proportion of the earnings base, investors are trying to get their head around a new set of risks. Shareholders would also start contending with a different set of implications for the stock price and valuations. The stocks are all currently trading within a narrow range of each other for most of the firms (on Price/FRE).

But what is now clear that it is not just about PE anymore.

Until next time,

The Atomic Investor

Take trying to forecast the impact of a disruptive technology on our economy or our lives, for example. A big change in the environment, in ‘evolution-speak’, forces economic agents (us) to adapt and find the best fitness function i.e the way we interact or perform certain activities impacted by these changes. The ones who can adapt better have higher chances of survival and winning, while the rest are left behind facing the downsides of being in an environment not ‘fit’ for them.

This forecasting exercise becomes a highly complex problem where a multitude of inter-related factors are at play. We’re not only dealing with the tangible, physical aspects of the change, but also with the changes in the existing social fabric induced by a big change and the subsequent interaction between the two of them.

Understanding what is likely to happen is hard. We try to create order from all the disorderly info we absorb to get us to a reasonably perfect answer. We might use a linear framework to understand the impact of the x variable (input) on the y variable (output). We could combine that with our heuristics, anecdotes, personal experience, history and other available evidence to form an opinion on the solution. Additionally, we might also try to find patterns in the similar sets of changes in the past to allow us to speculate how things might transpire in the future.

But big disruptive changes rarely follow a ruleset. There could be long phases of small incremental changes that go unnoticed, a string of negative surprises (things don’t go as they were supposed to) and then sudden step changes upwards. There might not be a fixed order of developments either. A lucky or an unlucky turn of events can reverse the course or slow down the pace of change (or even accelerate it).

In business and investing, you are navigating change at every step along the way. Understanding it and its potential impact early in the cycle is therefore, key.

Our deductive approach might still be helpful and work with decent success. But for a different perspective, a couple more frameworks or lenses might come in handy as alternative ways to understand big changes and how we might position ourselves to be fit for them.

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The disruptive innovation lens - The Clayton Christensen model

The disruptive innovation theory was first developed by Clayton Christensen in 1995 (later updated multiple times), and has seen its acceptance and application in tech and finance grow ever since. Now, nearly every major and minor breakthrough is conflated with the concepts of being “disruptive” as in the original innovation theory.

The correct understanding and nuances of it is important as different types of disruptive changes require different approaches by investors and operators, and cannot be applied with a broad brush to everything.

Disruptive innovation emerges from one of the two segments: unserved/underserved customers overlooked by incumbents or complete non-users. Disruptors will either start by capturing the low end of the market and move upwards where incumbents play, or create a new market for their product where none existed.

However, it is not just a point-in-time instance or a single product that is disruptive. It is the evolution of it through its development lifecycle.

Additionally, these innovations do not catch up with the mainstream consumers until they meet their quality standards and needs. Incumbents usually focus on sustaining what they have and add incremental improvements that make the current set of profitable customers happy.

Disruptors on the other hand focus on big ‘step changes’ and improve the product until is equal or better than the incumbent which is where they start gaining market share as consumers start switching.

Time to give this lens a test run.

The entire investor community today is trying to pick winners and losers in the disruptive change introduced by AI. Although it has been painted by a broad ‘disruptive technology’ brush already, AI and machine learning models have been around for decades.

If you go back in the AI timeline, you would notice sustaining, incremental improvements in models over years bringing in more precision, speed and capacity accuracy to computational problems. The developments over the last decade or so got us to the current state large language models (LLMs like GPT-3, LLama, Claude etc.) which are trained on billions of inputs and can accurately understand the nuances and complexities in human language to generate different forms of output. AI (Gen AI), the underlying enabling technology, would therefore be more of an incremental improvement (sustaining innovation) than being totally disruptive.

The products and innovations being built on top, however, have the ability to be truly disruptive. Entrants are currently competing in low value areas (simple consumer apps) as they reach the quality of the product and effectiveness of the use case to be adopted by the mainstream. Chat-GPT has shown some promise but the average search customer still ‘Googles’ things.

Incumbents like Google, Amazon, Microsoft and Meta are sustaining their business models by focusing on where the profitable customers are. But recognising the potential of AI they are using the enabling technology in house, or are partnering with the entrants to make their existing products and core business better for both consumers and high value enterprise customers.

Given low barriers to entry (with open source models) one can expect multiple entrants vying to build the next app or business model. However, the need for data troves to train the models and the sheer compute capacity means partnering with incumbents or well funded players with access to capital and resources increases their likelihood of success. According to the theory, sustaining innovation puts incumbents at an advantage most of the times, which is what we see playing out..so far.

Another interesting personal observation here. The arc of the development of a truly disruptive product also depends on the incremental improvements in the underlying enabling technology (the LLMs).

The framework does not give you the right answer straightaway but does help you build and inform your strategy, whether you are an operator or an investor, with a more informed perspective on changes driven by these innovations.

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The Paradigm shift lens - Carlota Perez’s tech revolutions framework

Carlota Perez’s “Lifecycle of a technological revolution” is a widely used framework to analyse the development of an emerging technology and technology cycles. It was written in 2002 right after the dot-com bubble burst, and was validated and vindicated with the success of the internet paradigm in the decade that followed.

The theory views long-term shifts as new paradigms that have a lifecycle of half a century until they get exhausted, and further divides the trajectory into different phases (as in the image below).

Phase 1, where there is an explosion of new products and growth and innovation is expanded to different industries. (irruption)

Phase 2, where the technology flourishes along with new industries and infrastructures, and sees intensive investment and market growth. (frenzy)

Phase 3, where the technology starts to be fully deployed across the market potential. (synergy)

Phase 4, the paradigm reaches maturity, growth starts slowing with decreasing returns on invested capital. There is again a demand for new, better technologies and products forcing innovators to start tinkering again. (maturity)

How would you view the new tech paradigm kickstarted by the advent of the internet using this framework? The irruption would be the early days of the dot com era where the internet burst onto the scene, with companies building products and services around the enabling technology.

We have all lived or read about the frenzy phase, with the flood of capital and speculation into the space leading to one of the biggest tech bubbles in history.

The synergy phase came after the dot com bubble burst, when real internet enabled business models were deployed, the internet era companies took over the stage and created generational wealth for both investors and operators. This phase, and the innovations enabled by the foundational technology, also gave birth to other tech cycles which we now call the Mobile revolution, the Cloud era etc.

Finally, the internet is now a commodity and the market, mature.

Does AI seem to the be the next paradigm? If so then the launch of Chat GPT might have started the irruption phase (termed as the ‘big bang' moment needed for irruption). Given the hype around the space, we are quickly marching towards the frenzy phase.

AI mentions on earnings calls are at all time highs.

VC money is being invested at stratospheric valuations.

Where would Big Tech (and the rest of the market) be if Gen AI was not in the picture?

A few other interesting highlights from the text to put the apparent big changes in this cycle into context:

Profound change: Long term changes driven by innovation are involve intense interaction between the economy and social institutions and profound changes in both. The changes occurring in the techno-economic sphere imply a huge social cost in loss of jobs and skills as well as in geographic displacement of activities.

Why change occurs in revolutions: The favourable conditions for the next revolution are created when the potential of the previous one approaches exhaustion. Once the valid trajectories for new products and processes as well as for their improvement are known, successive and successful innovations will follow. They will be compatible among themselves, they will interact smoothly, they will find the required supplies, qualified personnel and market channels and will encounter increasing social acceptance based on learning with the previous products.

The turning point: The frenzy stage leads to a bubble which when bursts acts as a turning point for the tech cycle, which creates an environment for the deployment phase.

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Thinking about long term change is fraught with challenges as these cycles rarely play out like the last time. The changes in the environment and the interaction among its variables happens in real-time. Even if you could get the cycle right, you still cannot time the turn of a phase perfectly.

However, even though the nature of change might be different, some things like human behaviour stay mostly the same.

Until next time,

The Atomic Investor

While the first private markets investor to go public was American Capital in 1997 (which listed as a Business Development Company, and was acquired by Ares Capital (another BDC) 20 years later), the real trend of going public as a private markets asset manager was kickstarted by Blackstone in 2007, raising $4.1bn at a valuation of more than $30bn. Fortress Investment Group (which was acquired by Softbank in 2017 and is now close to becoming a part of Mubadala) and KKR followed in 2007 and 2010 respectively, led by others like Carlyle, Apollo, Oaktree, Ares, EQT, TPG etc in the following decade. Today there are more than 15 global publicly listed PE managers (probably a lot more if you include VC and other closed end investment funds) with trillions in combined AUM.

As the latest earnings season rolled over, I looked at the top 7 publicly listed PE stocks by AUM managing about $4 trillion and having a combined market cap over $300bn - Blackstone (BX), KKR, Brookfield Asset Management (BAM), Ares management (ARES), EQT AB, Apollo (APO) and Carlyle Group (CG).

These seven are now well and truly diversified asset managers, investing across asset classes like PE, credit, real estate and infrastructure and across the globe. Why only seven? So that I could borrow the big tech moniker and call them the magnificent seven of PE.

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Post IPO performance

Stock performance of these listed PE names has been mixed since their IPOs. But returns alone do not always tell the whole story. Blackstone for example, went public right before the financial crisis of 2008 and reached a low of about $4bn during the market drawdown of 2008-10, whereas KKR was listed right after the crisis in 2010 in a bull market which clearly acted as a tailwind for the stock price.

Others like ARES and EQT have used their stock currency to expand globally via M&A into other regions and asset classes, allowing them to grow their AUM (and their revenue base) rapidly.

Additionally, these stocks are highly sensitive to the macro environment (rates impacting fundraising, deploying capital and exits) and the overall economic conditions, making them high-beta names.

Here’s a quick look into the performance in the table below.

Long-term trends for the asset class and these stocks have been positive over the last decade. The pace at which the asset class has gathered AUM has been outstanding. The largest PEs have diversified their capital base globally and responded extremely well to institutional investor demands, built investment solutions they needed and delivered the performance they wanted in a low interest rate environment, making it an important component in institutional portfolios.

PE stocks are still impacted by sector related and idiosyncratic factors, like any other publicly listed corporation. The fundamentals of the PE asset manager business give you a framework to assess these stocks.

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The business of PE

Like every other asset manager, fee revenues are an integral part of a PE business. The 2 & 20 model (2% management fee and 20% carried interest from realised investments) is well known, but today things are quite different. Firms offer institutional investor discounts, co-investment opportunities, fee holidays and other attractive deals to get them to commit capital for their funds.

On a blended basis, fee revenues are around a 100 basis points given the competition that exists in the industry. However, large brand names still have some pricing power on specialist funds or strategies, backed by their scale to source deals and deploy capital on attractive opportunities.

To put it bluntly, no, I think scaled managers with strong performance, strong track record leadership positions. Those aren't the ones feeling the pressure. And I think we're quite fortunate to have leading platforms across all our core asset classes. I candidly, I expect where that narrative is coming from is smaller or less scaled managers who are doing what they need to, to break into the space.
- BAM on whether they are feeling a pressure to reduce fees, Q2 earnings call

The markets love growth in fee revenues and higher fee margins given the stability and predictability in earnings it provides. The large listed PEs have raised a huge amount of capital, expanded into different strategies and products, and some of them have bought other private market managers to grow their asset base, and in turn their fee revenues.

For instance, ARES has compounded AUM and fee revenues at a CAGR of 19% since 2011 via organic growth by building credit businesses and inorganically by buying up PE and Infrastructure managers.

Fee related earnings (FRE) is another important metric tracked by analysts which gives you a sense of the operating efficiency of the firm and how much of the value stable revenue base can actually be distributed to the shareholders, after deducting employee and other expenses. KKR and BX for example, have best in class FRE margins of 61% and whereas ARES’ is a bit lower at 40-45%.

Realised investment income is the investment gains on exits, the lumpy and pro-cyclical part of a PE business’ earnings. This segment was down YoY for the entire sector given the volatility in the markets over 2022 and this year, driven by rising rates and a tight credit environment impacting exits, M&A and deployments.

Finally, distributable earnings (DE) is the sum of fee earnings and income (after expenses) from realised investments. It is used as proxy for distributable cash flows, and most firms would target giving back more than 90% of it to their shareholders via dividends and share buybacks.

These fundamental metrics are also used to construct multiples - Price/FRE and Price/DE for these stocks. Managements (in earnings calls and presentations) try to communicate the visibility of their stable sources of revenues to the market as a signal for the stock to trade at a better multiple of earnings.

Analysts would use these to compare PE businesses with each other or the rest of financial services sector. The premium or discount on the multiple could be a reflection of the confidence the market has in the prospects of the business and in the ability of the management to execute.

Here’s another table comparing key metrics for the magnificent 7.

So how are PEs driving growth in their asset base and fee revenues, and operating in this new market environment?

Some noteworthy themes emerged from recent earnings calls.

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Private credit continues to shine:

Private credit has had a stellar year and a half since the start of the rate hiking cycle. The banking crisis in the U.S accelerated demand for lending products as banks retreated from the syndicated loan market. According to Ares, 85% of buyout financing activity happened in the private credit market this year.

Blackstone’s direct lending platform is now as big as $100bn, and it called it a “golden moment” of private credit. Apollo weighed in too by stating,

This is a great time for private credit. This is not a quarter, that's a great time for private credit. This is secular change. Not only do we have higher base rates and regulatory change and change in market dynamics, we are in the beginning of a secular shift in how credit is provided to businesses and a shift that I believe will continue to gather speed. To be successful in this market, you need a recurring supply of unique origination.

It had $23bn of originations in this quarter alone, whereas ARES’s credit assets have grown by more than $200bn in the last 5 years (incl. M&A)

KKR put a neat slide in one of its investor presentations showcasing the case for direct lending for PE.

You could expect further growth in this segment for the largest players as they leverage this asset class’ growth and expand beyond buyout and direct lending into Asset based finance and investment-grade credit products.

New products for new channels:

PE had always been an institutional asset class that was closed to individual investors. Blackstone, back in 2011, was one of the first movers to launch new products (funds) for a new Private Wealth channel - the high net worth individuals who want to invest in private markets. The BREIT, the Blackstone real estate income trust now alone has more than $65bn in assets. It subsequently launched BCRED, a private credit fund for this channel which has now $48bn in assets.

The private wealth channel has grown at a staggering pace with the others following suit. PEs are expanding their reach by offering products to wealth management platforms, distributing to HNW individuals via investment advisors (RIAs) and partnering with banks and non-bank financial services firms to reach investors who previously had little access to PE. Ares expects Individual AUM into Alternatives to grow by $9 tn over the next 10 years.

Insurance has emerged as another channel for PE to diversify their AUM base away from cyclical institutional money. The largest PEs have either bought insurers and annuity providers (like Apollo-Athene, KKR-General Atlantic, Brookfield-AEL) or strategically partnered with large insurers or reinsurers (Blackstone, Ares) to manage their asset book for a fee, and invest this capital into their suite of private markets products.

These channels have unlocked hundreds of billions of assets for the largest PE managers. But most importantly, they are a source of “Permanent capital” - another metric that is closely tracked and disclosed in their results.

Individual and insurance assets are permanent for that they do not have a 10 year fund lifecycle like traditional PE funds. Private wealth funds are semi-liquid funds that can be redeemed by investors upto a certain % per quarter. This is another competitive advantage for managers who can, at scale, build these distribution channels and capabilities and lend more stability to their FRE, and therefore DE.

Last week, Blackstone combined its Credit and Insurance units with $295bn in assets and expects it to grow to $1tn by itself.

The following excerpt from Ares’ Q2 earnings call highlights what the largest PE managers are doing in the space:

We have ambitious global product expansion plans, and we've made the investments in personnel and infrastructure to launch certain credit products into the European and APAC wealth management markets later this year and into next year.

We also formally launched Access Ares, a comprehensive online platform that offers product agnostic and educational content and thought leadership for the wealth channel.

Within our affiliated insurance segment through Aspida, we saw $2 billion of new annuity premium inflows from both our retail and reinsurance platforms in the second quarter. In the first half of 2023, Aspida has issued $3.6 billion in annuity premiums and has grown to approximately $9.5 billion in AUM.

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and finally,

Operating and driving value creation in this new environment:

From EQT, but the idea has been echoed by almost all of the magnificent seven

While the last decade was about low rates, large fund raises, leveraged returns in a bid to get bigger, this period seems considerably different. It is underlined by a more normal rate environment, breadth of products, scale, innovation, consolidation and getting better at managing capital and driving value creation. Interesting times lie ahead for the magnificent seven of PE.

Until next time,

The Atomic Investor

Some of it is driven by a change in narrative around how they operate and a lot of it is the exuberance around AI. As I opined in Big tech passes the first test , these companies were put to a test by the markets if they can find more sustainable ways of growth and prioritise projects with a clear path to profitability. And that the new tech epoch of AI, being as capital intensive as we are all discovering it could be, would be the next big test for them.

This quarter was another glimpse into this balancing act of maintaining the cost discipline (that the markets handsomely rewarded them for) vs investing in AI.

Cash flow remains an undisputed king

Cost controls and prioritising cash flow was a recurring theme this quarter. These companies cut employee headcount or kept it flat to manage their opex growth. Some of them also slowed down on expansive capex projects. (Note the trend in TTM capex spend in the chart below)

MSFT grew revenues by 8% (with Azure still growing by 27%) while increasing opex by just 2%, operating cash flow increased 17% and FCF increased 12% YoY. It also kept headcount flat allowing it to expand operating margins.

META continued to execute its “Year of efficiency” program. It decreased its headcount by 7%, and lowered its capex forecast by another $3bn and added an extra $4.8bn of free cash flow (on a TTM basis), all this while growing its revenues by 11%. Core business expense growth was also much lower than revenue growth.

GOOG increased its top line by 9% (with Google Cloud growing by 28%) while growing opex by 4%. It also reduced headcount by more than 9000 employees. YTD it has added more than $8bn of FCF additionally.

AMZN saw 11% growth in revenues and further reduced its capex spend to get back into positive FCF territory of almost $8bn.

Investors love to see this. Revenue growth is picking up again from the lows of 2022. Big Tech continues to get leaner and is prioritising spend which is directed towards where the puck is going. They’re also generating higher cash flows in the short term which investors are putting a much higher value on.

To put it in context, we’re paying 23x-40x LTM free cash flows for these stocks today.

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The balancing act might not last for long, but this is what it takes

The companies also reiterated how their spend will pick up again as they further invest in building their AI related infrastructure and other tools to support their highest growth areas. These excerpts from their earnings reports/calls made it pretty clear.

“To support our Microsoft Cloud growth and demand for our AI platform, we will accelerate investment in our cloud infrastructure. We expect capital expenditures to increase sequentially each quarter through the year as we scale to meet demand signals.” - MSFT

"We expect elevated levels of investment in our technical infrastructure, increasing through the back half of 2023 and continuing to grow in 2024. The primary driver is to support the opportunities we see in AI across Alphabet...” - GOOG

“We expect higher infrastructure-related costs next year. Given our increased capital investments in recent years, we expect depreciation expenses in 2024 to increase by a larger amount than in 2023. We also expect to incur higher operating costs from running a larger infrastructure footprint.” - META

If the macro environment does not change and rates are still high around the same time next year, I would not be surprised to see much higher spends by Big Tech giving the market shudders, like they did last year. However, I believe a prolonged focus on short term cash flows over long-term growth is real risk for these companies and their shareholders.

The flattish trend in their revenue growth rates is hard to ignore. It is not only a sign of the scale at which they operate now, but also of their core business being mature or close to maturity.

One could take the other side of the argument above and mention growth rates and the potential for Cloud, but the trend is directionally similar.

At this point in their corporate lifecycle, I’d rather these companies spend and invest in high growth opportunities (prudently, of course) than spit out higher cash flows consistently. These are exceptional businesses with robust core markets that put them in a position to outspend the rest.

Their ability to spend for growth at every point in the business/economic cycle is one of their strongest “moats”, which allows them to stay ahead entering into new tech cycles.

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This is what it takes

In Big Tech’s Biggest Bets (a must read essay if you follow these companies), Matthew Ball shares some fascinating insights into the gargantuan $$ amounts it took for Big Tech to get where they are right now.

Meta has spent approximately $56bn on its AR/VR Reality Labs division while generating $7bn in revenues in the segment on a cumulative basis. Its cash gushing core advertising business on its social media platforms allow it to do that.

Amazon has spent more than $35bn on content alone. Does Prime Video generate billions in cash flows by itself? Nope. But it provides its now enormous $40bn Ads business even more ad inventory to keep growing. AWS took tens of billions just to get started, and has a run rate of $90bn with more than $20bn of operating profits alone. It further allowed it to invest in products like Alexa, which had cumulative losses of more than $20bn from 2012-2022.

Google has racked up cumulative losses of $35bn in its Cloud division which is now finally profitable and it now expands the TAM for its product ecosystem massively.

Microsoft has already spent almost $20bn on AI since 2019, and recently offered to buy Activision out for $75bn for its gaming division.

There are numerous other examples of successes and failures of Big tech spending big money to find the next growth platform. But this is what it takes to create trillions in shareholder value.

Jeff Bezos put it perfectly in a shareholder’s letter in 2017,

“As a company grows, everything needs to scale, including the size of your failed experiments. If the size of your failures isn’t growing, you’re not going to be inventing at a size that can actually move the needle.”

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As these companies ramp up spending again, it is still unclear when the returns on these AI investments will materialise. AI infrastructure seems to be best positioned to win today, but would it be the next platform for big tech for sustainable growth?

Mark Zuckerberg’s answer to the question around Reality Labs losses is how we should be thinking about the billion dollar bets these companies tend to make, like the current one on AI.

The balancing act of today might not last for long. But for those who are concerned, this is what it takes.

Until next time,

The Atomic Investor