In this episode, I spoke with Dr. Samuel Weinstein. He’s a professor of law at Cardozo Law School in New York City. Previously, he was an attorney in the legal policy section of the antitrust division of the U.S. Department of Justice.

He has written numerous articles on antitrust, competition, and innovation policy, including a recent co-authored paper titled No Exit, which discusses the new types of deal structures that big tech companies are using to acquire startups at least partly in response to shifting antitrust policy enforcement.

He is a graduate of Haverford College, and received both his J.D (Order of the Coif) and Ph.D. in U.S. History from U.C. Berkeley. His research interests include antitrust, competition policy, innovation policy, financial regulation, corporate law, and the history of the regulatory state.

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We covered topics including -

• What changed between the first attempt and recent successful acquisition of cloud security startup Wiz by Google for $32 billion

• Shift in antitrust policy enforcement over the years and more recently, from the Biden to the Trump administration

• How Big Tech is adapting to the changing policy environment and industry forces and redesigning how it invests in innovative startups

• Why startups are staying private longer

• New startup sale structures or ghost exits through centaurs and reverse acquihires

• Whether these new structures are here to stay

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References

• No Exit by Broughman, Brian J. and Wansley, Matthew and Weinstein, Samuel, No Exit (June 23, 2025). 100 N.Y.U. Law Review 1481 (2025), Vanderbilt Law Research Paper No. 5316792, Cardozo Legal Studies Research Paper No. 2025-18, Available at SSRN: https://ssrn.com/abstract=5316792 or http://dx.doi.org/10.2139/ssrn.5316792

• The Uncomfortable Truths About Reverse Acquihires: Why tech companies may have no choice

• Antitrust Division of the U.S. Department of Justice

• Federal Trade Commission

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I am sending this newsletter issue out on a weekend this time just to mix it up. A request here -if you enjoy reading this article, I would be so grateful for a like, share or recommendation.

Hope you’re having a wonderful weekend. -Sonia

I’m definitely space obsessed at the moment. As I wrote in my previous article “Who Built Artemis II?”, lunar exploration company Intuitive Machines (IM) was one of those that contributed to the Artemis II mission. It used its space data network to track radio waves transmitted by the Orion spacecraft throughout its journey around the Moon.

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IM’s role in this mission was fairly lightweight. It was more of a supporting actor playing a volunteer, unpaid role. Mostly, a marketing moment. But, prior to Artemis II, IM was involved in something much more ambitious.

In February 2024, its first lunar lander called Odysseus successfully touched down on the Moon…..but then tipped onto its side. The issue was that a safety laser switch had been incorrectly set before launch causing it to descend too fast. It lay damaged but still transmitting data before losing sunlight and going dark. In spite of this, the company and NASA called it a success. After all, it was the first US-based lander on the Moon since the 1970s.

Almost a year later, in March 2025, IM’s second lander called Athena, kinda did the same thing. The altimeter failed during descent, Athena struck a plateau, tipped over, and was dead within a few hours.

At first sight, both these efforts might appear to be failures. I mean the landers didn’t quite get past that early toddler stage on the moon. But for NASA it still checked a few boxes. 1. The science got done. 2. The test worked enough to get continuation dollars. 3. On a relative scale for NASA, it was able to launch faster and at a lower cost than its traditional missions which cost billions and years.

Ok, so those were NASA’s possible success criteria. But what about the stock market? Why was it rewarding the stock? The company has never turned a profit. In 2025, revenue actually fell 8% to around $210 million and net losses were $106.8 million.

From its 52-week low of $6.75 last April to $27-ish today, the stock has tripled in twelve months. And look at that jump since the Artemis II rocket launched on April 1, 2026.

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But, who cares about the past?! The future outlook is expected to be brighter starting 2026.

And that brings me to the key point that I want to make in this article, which is that

Investors are paying now for a story about the future. And the story is based on a current plot twist. Stories are increasingly driving investment when the evidence is not available or accessible or when the data shows something else.

This is not always a bad thing. Some investors see this as one of the few opportunities to capture asymmetric gains. Waiting for data was conventionally the smarter move but clearly, some now believe, rightly or wrongly, that waiting has a cost.

Let me explain why.

This is my observation and not investment advice. IM is my guinea pig here, a case study if you will, to exemplify the point rather than a story in and of itself.

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Not all Investments are created equal

You can categorize investments into three broad tiers, based less on their size and other characteristics and more on how much information is available and who has access to it.

Tier 1: Story-driven investing, available to few

The first tier pertains to the private market, i.e. startups and privately-held companies. The least amount of information is available about these companies but the most dramatic wealth creation has also happened in this tier. Think Google, Meta (then Facebook) when the value of discovering them was very high and before they IPO’d.

Due to the limited information available at this stage, investors make a judgment about the story and how good the founder is at telling that story. Because information asymmetry is the highest, risk is the highest (90% of new startups fail, a well-known statistic) and the rewards for the story coming true in the future are also the highest.

The catch, however, is lack of access to these companies at that early stage. To be an investor, you have to be ‘accredited’. Even if you are, you have to have access to deal flow, i.e. a way to find and reach these companies and their founders. Simple in theory, much harder to make it happen unless you have that kind of Rolodex.

It’s also not an option for many non-accredited retail investors because the SEC does not allow it. p.s. there are now some new ways for non-accredited investors to invest in startups, a topic I will cover in my new newsletter called The Startup Investor. And yes, this is a shameless plug for it.

Tier 2: Story-driven investing, open to all

The second tier is the one that Intuitive Machines occupies. IM became a public company in 2023. This tier signifies the early story-driven phase of the public company’s life when the current numbers cannot sufficiently confirm or deny the story. The business model has not yet been validated by consistent revenue, profitability, or the kind of data that makes it easier to evaluate the future potential of a company.

There is still a good amount of information asymmetry and still some amount of discovery value available to reap. For the ordinary investor locked out of private markets and priced out of proven winners, this provides a window of opportunity.

A good example here is Amazon in its first few years of existence as a post-IPO public company. Amazon lost money for years while investors funded Bezos’ narrative about owning the infrastructure of commerce while it was still an online bookstore.

Pre-2015 NVIDIA is an even stronger example and one where the signals were more subtle. It spent years being dismissed as a gaming chip company until researchers started using its chips to teach computers to ‘think’ (the beginnings of current AI) in the mid 2010s. That is when a handful of technically savvy investors funded a quieter narrative that the world was about to need massive parallel computing at scale.

In fact, I like to call Tier 2 investing the ‘NVIDIA Effect’. Now that NVIDIA is in the third tier, which we’ll get to shortly, and has been the massive success story that few saw coming when it was the size of IM, everyone is trying to spot the next NVIDIA in Tier 2 hoping that it will bring them similar outsized returns.

The ‘narrative of the narrative’ has caught on.

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Tier 3: Numbers-driven investing

The third tier includes public companies whose stories have been validated or evidenced by the numbers. That’s the NVIDIA of today. Analyst coverage for companies in this tier is deep. The stock price reflects consensus about the company’s intrinsic value.

Mispricing can exist but is usually lower, especially for US-based companies with a ton of constant attention. Information asymmetry is much lower compared to the first two tiers. Measurable risk is lower. You can still make returns, perhaps even high returns if you are an experienced investor, but the opportunities are slimmer.

To sum it up regarding the tiers, risk and access are inversely related to information in the tiers. The less information available, the higher the potential return but also the higher the chance of being wrong.

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What does it mean to invest in a story?

There are times when the word “story”, in the context of investing, conjures up a mental video of speculative behavior or gambling. In some cases, that description is apt. As in the case of meme stocks where the company looks exciting for a very short period of time. Or because somebody on the Internet says that the stock is going to the stratosphere and people buy into it. That’s a different kind of ‘story’, one which can end badly.

Story-driven investing, at its best, is more intentional and deliberate than that. It accommodates risk and the lack of data. That is precisely what creates the opportunity. Because to wait until hard data arrives means that you missed the timing; you didn’t catch the early NVIDIA.

Looking at the companies such as Amazon and NVIDIA that rewarded early story-driven investors, a few common conditions emerge for an investment to qualify as story-driven -

• It requires that the investor understands that they are making a bet on a narrative about the future rather than data-based evidence. Of course not everyone buying IM or similar plays today meets that bar but many conscious, early story-driven investors do.

• The underlying reality has to be physically possible and not just fantasy or science fiction. The future NASA contracts for Intuitive Machines proves that. It shows that its customer believes that it has the capability and it has the contracts signed.

• The story has to keep developing. In the case of IM, for example, its contribution to new missions, new contracts, new milestones become plots twists without which investment inflows will start to wane. All of this while the company is in the Tier 2 phase.

Intuitive Machines, right now, meets all three. Both Amazon and NVIDIA met all three conditions. The investors knew what they owned, the underlying reality was physically possible, and the story kept developing

But a few things have changed since Amazon and NVIDIA were in their infant stage. For one, there are a lot more retail investors active in the stock market today. As of late 2025/early 2026, retail (i.e. individuals as compared with institutions) investors accounted for approximately 20% - 35% of daily US equity trading volume compared to 10% a decade ago. The surge seen during the pandemic has not abated.

Retail investors tend to make faster decisions, especially when there’s a compelling, easy-to-understand story, because they are unencumbered by the time-consuming protocols required of institutions. Especially when they believe that the cost of waiting for evidence can mean missing out on the next NVIDIA.

These moves and the story also cause a bunch of other types of investors to jump in to take advantage of the opportunity such as institutional investors, momentum traders and short sellers. But, I won’t get too much into the technical details here so as to stay on track.

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So, what does Intuitive Machines actually tell us about story-driven investing?

The quote above sums it up beautifully I think. In the gap between Tier 2 (the story) and Tier 3 (the evidence), there might be economic opportunity for those who understand what they are looking at. Of course not everybody does and it’s hard to know whether prediction based on that understanding is correct until the future arrives. There are also no courses available to learn this.

For most people, in most situations, waiting for Tier 3 proof is the prudent choice. But things are different now and this is how many people are making decisions today. Storytelling itself has also become a very powerful tool that founders and business managers are increasingly using to get attention from all their stakeholders, whether investors or customers.

Whether someone should be acting on a story versus consistent data is a personal choice. Everything mentioned in this article is simply an observation about how the world of investing has changed as more individuals and institutions seek value early and before it looks obvious.

Whether you love that story or not, it is worth understanding why so many people do and what that tells us about the way markets work.

A quick end note here. I have been writing aka typing a lot more as I develop more articles. What has made my life much easier, to type as well as to think more clearly as I type, is to not type at all! I’ve been using a tool called Wispr Flow. All I do is speak and it converts my speech to text very quickly. It also learns as it goes. Full transparency: I am such a fan of the tool that I signed up to be an affiliate. If you want to check it out, try this link. Yes, I make a wee fee if you sign up but it does not affect the price you pay.

p.s. Nothing in this article should be interpreted as investment advice. This post is for information and entertainment purposes only. Please speak to your investment or financial advisor before making any investment decision.

• Some Magnificent 7 companies are financing the very technology that could disrupt their own business models

• I mapped the Mag 7 companies using a strategy framework to ask not who wins the AI race, but who is structurally built to outlast it

• My hypothesis: multiple growth engines or a deepening moat leads to long term resilience, even at the cost of short term efficiency

• NVIDIA and Meta are the efficiency story now. Apple may be the most defensible

• This is a thought experiment, not a prediction.

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Unlike the Internet, another disruptive technology in days of yore, which grew organically in a decentralized manner, modern AI development is concentrated in the hands of a few technology companies. These companies, mainly the Magnificent 7 (Mag 7), are financing and/or developing AI technologies. OpenAI and Anthropic are not a part of the Mag 7 but are recipients of their massive investment.

The Mag 7 also happen to be the most valuable public companies in the world right now; namely Alphabet, Amazon, Apple, Meta, Microsoft, NVIDIA and Tesla. They currently account for about a third of the value of the S&P 500. p.s. Mag 7 usually refers to these companies’ stocks. I’m using it to refer to the companies themselves inclusive of their operations (not just their stock valuations).

Four of them, Meta, Alphabet, Amazon and Microsoft are aggressive contenders in the AI race, competing on AI products, platforms or services as direct competitors in some areas and indirect in others.

The fifth, Apple, is an AI integrator bringing AI into its product ecosystem.

The sixth, NVIDIA, makes the AI infrastructure that everyone else relies on.

The seventh, Tesla, is AI dependent and running its own race in autonomous vehicles and robotics. It is a bit of an outlier in this group because it is more of an asset-heavy manufacturing company than a technology company. Most of the discussion here applies less to Tesla and more to the others. But, I didn’t want Elon to feel left out :) So, in it goes.

Most of these companies are spending at a scale that would have seemed implausible even five years ago. Because today, AI is an imperative. In doing so, a few are also financing and/or building the very technology most likely to challenge the foundations of their own competitive advantage.

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Which Mag 7 has the structural insurance to outlast the AI race?

The high valuations for these companies shown in the chart above assume longevity. If you believe, as many do, that AI will be a winner-takes-all race (or at most, a couple of winners take all, like the mobile platform or search engine wars), what becomes of the non-winning Mag 7 companies?

Winning entails getting to some version of Artificial General Intelligence (AGI); i.e. AI that can match or exceed the cognitive abilities of human beings across any task. Whoever gets there first also gets the first mover advantage of potentially controlling the foundation technology and the next computing platform.

It is hard to imagine that, toward the end of the race, the non-winners could continue with business-as-today. It is equally hard to imagine that any of them would fade into the sunset in the foreseeable future. No, ma’am!

Not with the intention of making any prediction, but to provoke debate and as a thought experiment, what if we apply a different approach to analyzing these companies?

Instead of the trillion dollar question that everyone seems to be asking about who wins the AI race, what if we ask how each company's structure is being reshaped by AI and what that could mean for its future?

Let’s walk in, methodically.

Of Strategy, Structure and Performance…

When I was a doctoral student eons ago, I learned a framework that offers a practitioner-friendly approach to analyze the interrelationship between company strategy, structure and economic performance. It was presented by an academic scholar Richard Rumelt who has written multiple articles and books on this topic.

I’m going to use his work only to kick off our thought experiment here. There is beauty in organizing thinking into a well-reasoned and proven framework; a framework with solid edges that provide meaning.

Rumelt's framework was conceived for stable times. We are clearly not in stable times. But the underlying logic, that the nature of a company's core factors drives long run performance is what I want to use as a starting point to think about the Mag 7 in the AI age.

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What is this 50-year old framework?

A quick primer without getting too much into the weeds. But, if this is too much theory, feel free to jump straight to the sections below on the individual Mags.

Rumelt’s early research (1970s/1980s) focused on the performance implications of diversification. Diversification refers to a strategy where a company enters new markets or develops new products or services, moving beyond its existing operations.

The takeaway from his work on this topic was that related diversification, i.e. when those products/services/businesses are more related to the core business, leads to better corporate performance by leveraging core competencies across similar businesses. Shared core competencies preserve value.

He found that the reverse was true for unrelated businesses; i.e. they underperformed.

But, he also put some numbers around it and categorized companies based on those (See! I told you it was practitioner-friendly.). Here’s how.

There are two relevant metrics here that help us map company positions to understand their structure

• The Specialization Ratio (SR) viz. the proportion of a firm’s revenues attributable to its largest single business. High SR means concentrated, low means diversified.

• The Related Ratio (RR) viz. the proportion of revenues from businesses sharing some tangible strategic or operational link to the core.

He then classified companies based on how they scored on each of those metrics.

• Single Business (SR >0.95)

• Dominant Business (SR 0.70–0.95)

• Related Constrained (RR >0.70, businesses all linked to the core)

• Unrelated (RR <0.70).

From this grouping, he found that overall, strongly Related businesses such as Dominant Constrained (one dominant business with minor ones connected to the core), Single Businesses and Related Constrained firms, in that order, outperformed all others on long-run profitability.

Why? Because they preserved value due to the nature of their ‘core factors’; i.e. how idiosyncratic, how intangible, how defensible and how related they were. That is what ultimately drives the performance differences he observed.

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Resilience or Efficiency? A New Hypothesis for a Disruptive Time

I repurposed Rumelt’s framework to apply during a period of disruption. That requires a different hypothesis.

Disruptive technologies, such as AI, force even the most successful companies, including the Mag 7, to fundamentally reimagine their businesses. As the market shifts, the core factors that built their dominant positions must now prove that they can survive transformation.

My hypothesis is that the companies least vulnerable to AI disruption are those that either

1. have multiple core factors as growth engines or

2. they can leverage AI to deepen their existing dominant core factors.

The first provides resilience through related diversification. The second is a higher-risk bet. It could work profitably as in the case of NVIDIA right now. Its concentration in chips has moved AI development forward in leaps and bounds. But, it leaves less margin for error if the core is undermined. More on this later.

In the near term, this distinction carries a cost. Concentrated, single business companies, on average, tend to deploy capital more efficiently, while more diversified companies appear to spread resources thin. But that apparent inefficiency might be the cost of resilience.

During disruptive times, those optimizing purely for near-term capital efficiency may be underpricing the long term vulnerability of highly concentrated companies and undervaluing the durability of those with multiple engines of growth.

I’m sure there are many people who will disagree with this take. It is possible that I will be proven wrong when the verdict is delivered by the world’s greatest jury - time.

Until then, let me illustrate my points.

Structural Evolution and Performance in the AI Age

I plotted the Mag 7 using Rumelt’s 2x2 framework with Relatedness Ratio (RR) on the vertical axis and Specialization Ratio (SR) on the horizontal axis. SR and RR were calculated using segment revenue from the companies’ annual SEC filings.

Since the goal is to visualize how Mag 7 structures are being reshaped in the AI age, I plotted them across three points in time

• 2019 - The pre-AI baseline. All seven companies are operating their core businesses without AI as a structural force.

• 2022 - The AI ignition point (ChatGPT launched in Nov 22, Copilot and Gemini in 2023).

• 2025 - Where we are now (the most recent full year).

Thank you to my co-worker Claude AI for its help with generating this image. In complete control-freak style, I pushed it 50x to generate the same chart with multiple changes. I’m glad it’s not human enough as yet to direct its wrath at me. Stay kind, AI!

I know there is a lot going on in this chart. Let me break it down.

The Bubbles

Note that this is a zoomed in version of the chart since predictably, there were no Mag 7 in the Unrelated category below 0.70 on RR. Per Rumelt, they are all Related businesses with an RR score >0.70. They are all strong performers, which is consistent with his framework.

As shown in the legend the smallest, lightest colored bubble refers to the position of the company based on its RR and SR in the baseline year, 2019. The medium-dark medium-sized bubble shows the position in the AI ignition year of 2022. For these two years, the sizes of the bubbles don’t mean anything.

The largest bubble with the dotted outline indicates the position of the company in 2025. For this year only, the size of the bubble shows the level of the company’s Return on Invested Capital (ROIC). ROIC measures how efficiently a company generates profits using its invested capital (debt and equity) and to some extent, this metric captures value creation too.

I included it to show that all the Mags (except Tesla) are currently using capital efficiently irrespective of their position on the map. Separately, I also calculated the Weighted Average Cost of Capital (WACC). A company creates value when ROIC > WACC and destroys value when ROIC < WACC. All Mag 7 except Tesla have a positive spread. Those details are not shown here to avoid cluttering the map.

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Who is special(ized)?

The arrows in the chart show the direction and shift in SR positions from 2019 to 2022 to 2025. Not shown here are movements of the positions on the RR over those years. Most companies held their positions in terms of relatedness. There were some minor movements for a couple of them but they weren’t significant enough to be meaningful, based on my interpretation.

The meaningful variation is, in fact, in how these companies have evolved in the AI years on the SR axis, not the RR axis. There is more concentration or specialization in business activities as you move from left to right.

Like AI, however, these positions are evolving. We are currently in the middle of this AI race. A lot could change in the next three years. The discussion on the individual Mag 7 companies below is in the context of the hypotheses I mentioned earlier, albeit with some assumptions.

NVIDIA

NVIDIA's dramatic rightward shift makes it the clearest test case for my hypothesis. It is the most concentrated of the Mag 7 right now, which is also what makes it the most efficient (see that huge ROIC bubble).

It moved from a lower specialization ratio in 2019 to becoming much more concentrated in 2025 at extraordinary speed, driven entirely by the AI build out.

In 2019, NVIDIA was a gaming and data center chip company, more diversified than it is today. Gaming made up the largest segment in 2019 and largest by a hair in 2022. Nvidia’s SR moved from 0.36 to 0.46 as data center revenue started accelerating but had not yet dominated. As of 2025, data center revenue makes up almost 90% of the total. Between 2019 and 2022, gaming revenue almost doubled but between 2022 and 2025, it stayed mostly the same.

NVIDIA’s core factor is Compute Unified Device Architecture (CUDA). CUDA is NVIDIA’s software and programming platform alongside a hardware and networking stack which makes it very difficult and costly for developers to switch to something else. Nvidia has now moved beyond selling chips to selling entire AI infrastructure systems.

The rightward move has worked for NVIDIA. It has the highest ROIC in the group. But, it may create a concentration risk moving forward. Nvidia’s top customers are hyperscalers who are simultaneously its biggest threat. As the hyperscalers develop their own AI competencies such as Google’s TPUs or Amazon’s Trainium, they are unlikely to usurp NVIDIA’s throne in the short term. Current demand is too high for that and will continue for a while.

In the long term, however, they represent the most credible threat to Nvidia’s margins. Developing alternatives to NVIDIA’s products gives the hyperscalers and other customers the ability to negotiate better prices, reduce supply dependency, and manage costs. There’s also competition from other chip companies like AMD, Broadcom, etc.

NVIDIA has been making a series of acquisitions to diversify its customer base and strengthen its moat. Its concentration seems durable for the next couple of years. Beyond that, it will depend on how competition shapes up and how the company itself evolves and ensures that this move rightward has not structurally jeopardized its future profitability.

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Amazon

Contrast that with Amazon. Amazon has been playing the long game for so ….well, long that it has ended up with one of the most complex corporate structures. In the visual, it has moved leftward since 2019, as in becoming less specialized by default as AWS and advertising have grown relative to its online stores business. It is also launching satellite internet service soon and expanding drone delivery, among other initiatives.

In some ways, that's a kind of structural resilience my hypothesis is arguing for. But, it may not be for everyone. The market may actually be penalizing it for that right now, in the form of a conglomerate ‘discount’.

Amazon is becoming a technology conglomerate, which refers to businesses which are more independent but tied together by the corporate chain. Historically, conglomerates, especially in the United States, have experienced a conglomerate discount.

Amazon’s core factors are the hardest to define because they vary based on the business. For example, for its online retail business, the core factor is its fulfillment infrastructure in the prime program. AWS offers cloud infrastructure at scale combined with developer tooling, which is its core factor. Amazon, taken together with all its different segments, its core factors come together in the form of a reinforcing flywheel. That is difficult to replicate. Even if one business segment performs very poorly in a particular year, growth in the other segments can subsidize any losses.

Amazon is expected to spend $200 billion on data centers and other goals in 2026. It is also making investments in removing constraints in AI infrastructure development by buying or investing in the previous step in the supply chain. For example, it recently signed a deal to buy copper from a revived U.S. copper mine in Arizona because copper is one of the most important raw materials used in data centers. It is also looking to build energy capabilities in-house because energy is another scarce resource that is required by AI.

Given the level of capital expenditures in various businesses, there is a lot of skepticism that Amazon will provide returns in the short term. Sounds about right with simple math. But this is the argument my hypothesis makes. In addition to developing AI, the investments are also targeted at removing the specific constraints in the AI build out. By vertically integrating, Amazon will control significant parts of the supply chain of AI development. That is assembling the flywheel for the next decade. It continues to play the long game.

Alphabet

Alphabet has been moving leftward on the chart as the Cloud business grows alongside the AI buildout.

Alphabet’s core factor is the ability to “to organize the world's information and make it universally accessible and useful”. It has mostly delivered on this mission statement. This ability is built on proprietary infrastructure and monetizing information at scale. Over decades, Alphabet has made search quality improvements, refined its indexing capabilities, and honed its advertising mechanics. As billions use its services like Gmail, YouTube, and other products, it owns a massive data layer as well.

In fact, it is in a very unique position. It controls many of the ingredients for the AI build out. It owns the training data, the compute infrastructure, the distribution platforms, and the consumer relationships. It can capture value at each of these layers and the flywheel amplifies that value. Gemini is trained on Google, YouTube, other products’ data; it runs on TPUs it designed; deploys it through Android and Chrome that it controls; and monetizes it through an ad platform it also owns. While the current evidence points to the conclusion that AI is deepening its moat, that picture is not without doubt.

There is concern that people’s information searching habits are changing as AI chatbots are starting to be used in every part of our personal and professional lives. So if AI changes how people access information, if they shift from search queries to direct answers, how does Search, its largest revenue driver, continue to get monetized?

In reality that concern hasn’t manifested yet in terms of revenue. Alphabet has figured out how to monetize its Gemini AI features by embedding ads into AI overviews and AI mode. Google search revenue is actually growing faster because of AI. Gemini AI is increasingly getting favorable reviews from users.

Yet, those concerns might not be unfounded. While revenue might not be impacted much, there is data which shows that click-through rates have dropped as queries with AI overviews and 93% of AI mode queries end without clicking to reach the external website.

Alphabet is expected to spend around $185 billion on capex in the coming months, a large portion of which is AI-driven. Although Search might have been the dominant business so far, Alphabet also owns YouTube, Google Cloud, DeepMind (research unit) and a few moonshot ventures such as Waymo (autonomous taxis), Google Fiber (high-speed fiber broadband) and others. It has assorted investments in private companies like Anthropic and SpaceX (soon to be public). While most of its other bets aren’t profitable, some seem to be promising enough to potentially have high upside in the long term.

Microsoft

Microsoft has moved slightly rightward as its Cloud business grows. The big question is whether Copilot AI will deepen its enterprise moat or whether it will turn out to be just a feature layered on top of a core that's gradually becoming less differentiated.

Microsoft’s core factor is enterprise relationships. Its software is deeply embedded into organizational workflows and human work habits. Switching costs are thus very high.

Anecdotally, for months after Copilot was launched, several enterprises did not permit their employees to use any other AI because they were concerned about security issues. Since the rest of their data and processes were built on top of Windows and Office, that made them feel more secure using a Microsoft product. That is still true for some. But others have adopted enterprise AI versions from companies like Anthropic and OpenAI.

Some of that actually benefits Microsoft which owns a significant stake ~27% in OpenAI. The relationship with OpenAI goes beyond financial. It remains Microsoft’s frontier model partner, and Microsoft holds rights for both models and products extended through 2032. Additionally, OpenAI has agreed to purchase $250 billion of Azure services in a circular arrangement that the Mag 7 are becoming notorious for.

Microsoft is working to integrate Copilot as AI infrastructure into all its various products and make customers pay for it. Customer reviews of Copilot are mixed and it has the lowest market share in spite of being available on every Windows personal and enterprise computer.

A funny story here; Microsoft recently got trolled on social media over Copilot’s terms of use which stated

“Copilot is for entertainment purposes only. It can make mistakes, and it may not work as intended. Don’t rely on Copilot for important advice. Use Copilot at your own risk.”

The company is planning to change that language soon to better reflect Copilot’s upgrades. Make it quick, Microsoft!

If the company is not able to make Copilot a success, it risks falling behind in the AI race. A hedge of sorts is the stake in OpenAI worth around $228 billion at current valuations. The twist here is that Microsoft is simultaneously competing with OpenAI and also depending on it in some sense. What Microsoft looks like in 2030 will be defined by Copilot’s outcome and how this Open AI relationship unfolds.

Meta

Meta has barely moved on the chart since 2019. It is the only solidly single business in the group. So far, its strategy has been working extremely well for it with high returns each quarter in spite of high expenditures on what turned out to be failures. Such as the Metaverse. As of today, it is very difficult to debate whether AI will deepen its moat or not.

It is pumping billions into the AI build out. Meta is projected to spend between $115 billion and $135 billion this year, with the vast majority dedicated to AI. It is looking to develop AI infrastructure, in-house chips, and AI products. It just launched a proprietary frontier model, Muse Spark, which is a significant strategic pivot away from its open source Llama models, which underperformed expectations.

It is also planning to spend over $600 billion over the next few years to build data centers. All of this indicates a very aggressive push into AI. Much to Zuckerberg’s chagrin I’m sure, so far, rivals like Open AI and Anthropic have produced more impactful AI products.

Meta’s core factor, as we all know, is the set of social network platforms on which 3.5 billion people’s connections, behavioral data, and engagement patterns are monetized through advertising. While Meta is currently very financially secure, in my estimation, it is potentially fragile. Being a single business company, its future depends on continued engagement on its platforms. Marketing budgets are limited. If user behavior shifts; for example, if engagement on ChatGPT increases and more marketers put ads on ChatGPT, will that affect Meta’s business?

This is not counting the on/off threat from TikTok and the Federal Trade Commission’s ongoing battle against Meta “that for over a decade Meta has illegally maintained a monopoly in personal social networking services through anticompetitive conduct – by buying the significant competitive threats it identified in Instagram and WhatsApp”.

If user behavior does shift in a left-tailed scenario, Meta will indeed be financing the very technology that will threaten its position. That does not mean that its core factors will disappear overnight but its productivity could gradually decline. Given the current heavy use of Meta platforms and its excellence in ad targeting, this sounds inconceivable right now.

But, it has happened before. Its profitability dropped in 2022 because of high expenditures on failed projects, decline in ad spend overall, competition and other events. Facebook, once their flagship platform, is no longer as popular as it used to be for personal and social uses in the high revenue generating United States market. While the 2022 issues did not indicate a structural shift in the business, the loss of users on Facebook did.

That being said, it is unlikely that any of this will materialize in the next couple of years. User behavior shifts take time. And advertising takes some more time to follow the users. If that happens, its moat no longer remains defensible. Currently, it has no other growth engines although it is trying to develop a wearables business and attempting other initiatives.

While Rumelt’s study showed that single businesses do well and earn above-average returns, that might not apply during disruptions. Or, one could argue that during disruptive times companies become more vulnerable if the particular technology does not deepen its moat.

Tesla

Tesla makes a slight right and then left movement. I’m not going to allocate too much room in this article for Tesla. As I mentioned earlier it is a bit of an outlier in this group. To put it briefly, its core factor is its integration of vehicle hardware, software, and the energy built on its proprietary technology stack. This makes it unique although other EV companies and non-EV companies are catching up. In theory, AI should deepen Tesla’s moat. In reality, it depends a lot on how successful Tesla is in delivering on it.

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Apple

You’ve probably heard the saying, “one rotten apple spoils the barrel.” What about a fresh Apple?

Apple is almost nobody's pick to win the AI race. It is not as big an AI spender. It is not competing aggressively at the frontier model level and is dependent on partners to bring AI into its products. It has received its share of criticism and skepticism for that.

But, ironically, its strategy-structure converts AI capability into deeper competitive advantage rather than competing with its own business model. Its leftward shift on the chart as its Services business grows is small but meaningful, and unlike every other company here, it just needs AI to keep making its ecosystem more indispensable.

Apple’s core factor is the integration of hardware, software, and services into a seamless user experience that generates deep switching costs. You’ve probably experienced this personally or with friends. Once a Mac user, always a Mac user. Once an iPhone user, always an iPhone user. No matter the price of the product. People will stand in line at 6 a.m. or earlier to get into the store every time Apple launches a new product. To be clear, I admire the company but am neither a Mac nor an iPhone user. I am just too lazy to switch.

As for the products themselves, they bring together excellence in silicon design, operating system development, application ecosystems, and even retail experience. The company has been around for a while but no competitor has been able to replicate its success so far.

In some ways it reminds me of NVIDIA’s CUDA and the ecosystem around it. The difference, however, is that NVIDIA is smack in the middle of this AI wave and now focused on that one narrow aspect. Apple, on the other hand, is the best example we have of a company whose complex and interdependent ecosystem is widening around the same core. Despite being a related diversified company, as opposed to a concentrated one that is expected to be more capital efficient, it is actually extremely capital efficient with a high 68% ROIC.

It is likely to remain intact through the AI disruption. Integration of AI from Google’s Gemini, which the company has recently announced, will actually strengthen its core factors and moat. Under this multi year collaboration, the next generation of Apple foundation models will be based on Google's Gemini and cloud technology. It means that Apple’s 2.5 billion users can get access to a more personalized Siri on their handheld devices.

To sum it up, Apple has a very defensible moat that, at this point, seems to be the least at risk from AI disruption. On that note, look at this chart published in the Financial Times with projections into 2028!

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So, what’s the takeaway?

This was never meant to be a prediction. It is a thought experiment. Just one lens among many, to surface questions about corporate strategy and structure that financial metrics alone do not reveal.

I adapted an existing study to frame hypotheses around the risk of concentration during technological disruption years for the tech companies at the forefront of this disruption. Without any knowledge of the future, I thought through scenarios based on the defensibility of core factors to support the hypothesis; with a ton of personal opinion and interpretation thrown in.

Reliability of returns for any of these companies would depend on the time horizon that you are looking at. If you consider the next couple of years, Meta and NVIDIA are clearly crushing it right now in terms of capital efficiency.

If you’re thinking about longer term resilience and you believe we’re in a prolonged platform transition, the multi business architecture is the stronger position because it compounds in ways a single business might not and hedges against disruption at any one layer. Such as in the case of Amazon.

Somewhere in between those two groups, for the medium term are Alphabet and Microsoft, with a lot of ifs and buts.

The one having its cake and eating it too, in my opinion, is Apple. It is well positioned to reap the benefits of AI regardless of which one or two of the Mag 7 end up winning the AI race.

Obviously, this is not a comprehensive analysis or a 360-degree approach to understanding corporate strategy, structure, and future potential. But I hope you agree with me that it brings up some food for thought.

p.s. Nothing in this article should be interpreted as investment advice. This post is for information and entertainment purposes only. Please speak to your investment or financial advisor before making any investment decision.

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A note on ROIC calculation - ROIC calculated as NOPAT divided by total equity plus total debt, with no adjustment for cash holdings. This produces conservative estimates for companies with large cash balances like Apple.

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Most highly successful companies have moats.

Let’s define a moat further for clarity sake. In medieval times, moats were deep and wide ditches surrounding a castle or town. They were filled with water to protect it from enemies easily reaching their target.

The analogy lends itself well to the business world. A business or economic moat refers to barriers that protect a company’s profits from competition and other threats. The barriers make it harder for competition to ‘cross the moat’ and overtake the company’s advantages. So, the company can continue to earn higher returns with lower threat to its position in the market. At least for a period of time.

These barriers could be erected through brand effects (Coca-Cola), switching costs (Windows/Microsoft), distribution size (Walmart), network effects (Meta) and other such advantages.

But wait, there’s less.

My goal in this article is not to get into the types of moats. In fact, this article isn’t about moats per se. It is about Reddit and the reasons for its wide & deep moat that positions it well for success in the near future.

Why Reddit

Reddit has one of the most unique moats in its industry. It is unmatched in its competitive edge even if, in my opinion, the company has just about started capitalizing on its moat to earn higher returns.

So let’s talk about Reddit!

Social media forum platform Reddit was founded in 2005. Unlike Facebook (now Meta) which was founded in 2004 and went public in 2012 or Twitter (now X) which was founded in 2006 and IPO’d in 2013, Reddit went public by listing on the New York Stock Exchange in March 2024, 19 years after birth. Compared to its social media peers, a bit of a late bloomer in this and some other aspects.

Since then, its stock price has increased from around $46 to ~$260 after a few major swings. It has over 1.2 billion monthly active users (MAUs) “with over two billion posts and over 20 billion comments through December 31, 2024, and an average of 1.2 million posts created daily and 9.7 million comments created daily in December 2024” as indicated in its annual report.

These are smaller numbers than the number of users on Facebook, Instagram or TikTok. But, Reddit fosters much deeper engagement than that seen on the other social media platforms. More on this later.

For the uninitiated, I am going to share a quote from Wikipedia on why someone would use Reddit:

“Registered users submit content to the site such as links, text posts, images, and videos, which are then voted up or down by other members. Posts are organized by subject into user-created boards called subreddits. Submissions with more upvotes appear towards the top of their subreddit and, if they receive enough upvotes, ultimately on the site's front page. Reddit administrators moderate the communities. Moderation is also conducted by community-specific moderators, who are unpaid volunteers.”

Beauty lies in the eyes of the beholder

I wasn’t an avid Reddit user until a couple of years ago although several folks in my orbit clearly were and still are. I did attempt to use it a few times in the past to get answers to questions (especially when Google Search would show its links right on top with the promise of the perfect answer).

Even then, I would read the response, not linger for too long and certainly not get drawn in enough to engage further by posting my own queries or responding to others.

I think I was initially deterred by how cluttered the site was (it’s improved since then but still lacks the aesthetic quality of a welcoming website or app). But, for its regular user base, here content is royalty and design is the peon.

The entire site also has its own rules of play as do the subreddits. It can be a bit of a learning curve before you get the hang of it. In other words, not a great first time user experience, according to me. But once you do get the hang of it, it really serves a need that I think none of the other social media platforms do and therein lies the root of its moat.

You know a business is creating ample value for users or customers when it doesn’t make it easy to use at first but still has a sticky audience that keeps coming back for more!

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The 3Ds of Reddit’s Moat

Quoted from a post on LinkedIn “The tougher the advantage is to imitate, the wider the moat. The more value to the customer, the deeper the moat.” Reddit checks off both these boxes.

Not in any particular order, here are the three key reasons for Reddit’s moat.

1. Data Library

To be more specific, human experience data library.

I recently came across this interesting post on LinkedIn. It’s been cut off mid way (too long) but I’m sure you can find this individual on LinkedIn to view the rest of it if you are indeed seeking business ideas. And I’m sure that they would appreciate a follow if you are so inclined :)

You may or may not be searching for business ideas on Reddit but it is an ideal place to get a feel for the human experience on any topic because users are much more intent-driven than on any other platform. Even Facebook Groups don’t compare to the deep engagement and authenticity shown on subreddits.

Data for people

As the company says in its annual report, “Reddit is a global, digital city where anyone in the world can join a community to learn from one another, engage in authentic conversations, explore passions, research new hobbies, exchange goods and services, create new communities and experiences, share a few laughs, and find belonging”.

When users visit Reddit, they have a goal in mind - to seek an answer to a pressing question, to share their experiences honestly, to learn about a topic and so on. Since most subreddits don’t allow promotional content, which is extremely common on other social media platforms like Instagram, the content on Reddit communicates ‘signals without the noise’ seen elsewhere.

So, you get breadth due to the vast variety of topics and depth because each topic is narrowed down into niche communities or subreddits…all in one place!

You might have noticed that it is difficult to idly scroll and explore on Reddit if you ventured on it without a purpose. There are few pretty photos like on Instagram. Why would you spend time on it if you didn’t need to?! And yet people do to create and absorb meaning on a particular topic. That’s intentional action by users that creates a massive database of valuable, text-based data. And it keeps bringing back existing users and new users.

As in the LinkedIn post above, people who are looking for qualitative, anecdotal and experience-based information can harness that data to achieve a goal. Sure, it takes some mining for insights. With AI, that too has become an easier task.

Data for AI

You can find a lot of qualitative data in a digital city like Reddit. And no one is more aware of that than the foundational GenAI and LLM companies. Reddit has a partnership with OpenAI that allows it to train its AI models using Reddit content. It has a similar deal in place for Google to train its own AI models. It is also in talks with other AI companies for similar arrangements.

There are currently more than two billion posts and 20 billion comments on Reddit. These numbers will only keep increasing as more users get on the platform for natural, human conversation and connection. That means even more data that you cannot find elsewhere.

Since AI models will always need the kind of specific data that few like Reddit can provide, it has intangible and tangible assets that create a competitive advantage. The advent of AI is starting to erode the moats of some companies. For Reddit, it is actually enhancing its moat.

Expanding the ‘data’ set

Compared to its social media rivals which have a significant international presence, Reddit is late to the party. Majority of its users have historically been in the United States. If the company is able to grow its presence in other markets, its user base could double quickly.

The company acknowledged this opportunity and its plans to tackle global growth in its recent annual report, stating

“We see a massive opportunity to grow in geographies outside of the United States and in languages beyond English. As of December 31, 2024, approximately one billion posts have been translated in various languages, including Spanish, French, Italian, German, Portuguese, Swedish, Dutch, and Filipino. We are continuing to make more content available in many more languages, including Polish, Hindi, Bengali, Thai, Malaysian, and Vietnamese. Additionally, we are curating the Community Tab, Trending Today carousel, and sending highly relevant push notifications to help international users find and engage regularly with locally relevant and trending content.”

While the efforts to translate posts are a great first, it is the last point that is key to bringing in international users, i.e. ‘locally relevant and trending content’. This won’t be easy but recognizing its potential is the first step.

The takeaway - The largely reliable human experience data available and increasing on Reddit is a major asset that adds tremendous value and drives its moat. Used by individuals, companies and AI models, it is difficult for any competitor, incumbent or new company to come along and quickly build the same rich resource that Reddit has.

What is also pertinent to note is that late bloomer Reddit took the slow and steady path to developing this advantage. In a world where technology startups get rewarded for blitzscaling, Reddit built its moat more sustainably, brick by brick. The lesson here is that sometimes, creating valuable assets and resources just takes time. And many times, it is worth it.

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2. Deep Engagement

I now participate in several subreddits on various topics. I will admit that I have, at times, posted something in a subreddit without carefully reading the house rules and paid the price for violating them (I can be a shallow engager at times, especially when stressed for time).

One such time was when I posted an AI-generated image in one of the data visualization subreddits. I was looking for an AI tool that could ingest a photo of a hand-drawn image on a napkin and convert it into a professional looking one in seconds. I was so excited when I discovered that Grok Imagine could do it and how much time it would save me that I decided to share it in the subreddit.

Within minutes, I was admonished severely by a dozen Redditors, my original post was deleted by the moderators for creating chaos in the group and I have been forever banned from posting in that subreddit again. Yikes!

I was surprised to be unceremoniously kicked out by a bunch of anonymized, virtual strangers. Either way, I want to use this anecdote to make a few points here. Redditors, i.e. those who are regular Reddit users, are a deeply engaged bunch and highly invested in their community.

Engagement is one of the buzziest words around when it comes to social media. Views. Clicks. Likes. So many ways to measure it! Yet, these are indicators of shallow engagement.

Comments, if thoughtfully presented, may indicate more than shallow engagement. But, intent-driven engagement to participate in your community is deep engagement.

The high degree of that engagement is enabled by Reddit’s platform-specific features.

• Not designed for mindless scrolling. When it comes to social media, content consumption has become a parallel activity for many of us. There are many who spend hours mindlessly scrolling videos on Instagram or TikTok. A lot of that time, they are scrolling while waiting for an Uber to pick them up or a friend to show up, between business meetings or some other activity. It is of course much easier to do that with video content which permits partial attention much more than text content. Text content needs more effort. That brings me to my point about Reddit. The platform makes it difficult to idly scroll its text-based content. You have to pay attention which results in deeper engagement.

• The in-built, volunteer-driven moderation system works really well most times, particularly in more active groups. People care deeply about keeping their community ‘safe’ and on point. What that does is create a certain level of trust and solidarity with the community.

• People’s identities being hidden permits real freedom of speech. Often times on LinkedIn or Facebook, people are careful about what they say and how they say it because they are known by their real names. Reddit is different. You can openly share your opinions without shame or worrying about your professional or personal reputation. Yes, it could get moderated out but there is still a much higher level of freedom of speech. The same applies to replies. With that shield of anonymity, users feel free to engage more fully.

• You get the important information right on top because of the up/down voting mechanism which creates a merit-based content system. According to a Seeking Alpha writer, this “allows for the most helpful, thoughtful, or funniest content to bubble to the surface across a breadth of content that no centralized mechanism can manage rationally.” In addition, it encourages users to engage more meaningfully.

The takeaway - The multiplication of these factors drives deep engagement on Reddit. It also creates user stickiness. This user-stickiness contributes to a moat that cannot be easily replicated.

3. Dynamic Community

A relative’s daughter is 19 years old. She is your typical Gen Alpha; tech savvy, open-minded about everything from sexuality to music, a Tik-Tok fan and an AI native. She and her numerous friends frequently use Reddit.

It’s been around for so long (19 years) compared to young TikTok, which is a big draw for her generation, that I unconsciously assumed that middle aged people like me used it the most. We kind of adulted alongside Reddit now, didn’t we?

Anyway, her friends rely on it to get answers, feedback and whatever else they are seeking from others. In fact, it’s one of the first stops for her peers.

Reddit’s early users have grown older but younger users are still coming to the platform in spite of it being mainly text based. In comparison, Facebook has fewer younger users in the US in spite of being launched around the same time.

Why this matters

Having multi generational users is extremely beneficial for many reasons.

• Ensures platform longevity as generations continue to cycle through. This makes for a more reliable and sustainable revenue model. Aside from new generations adopting the platform, the existing users grow with it (this point captures another dimension of user stickiness).

• Enables the product to get more inclusive and keep improving in terms of utility and value to users. The more people that use the product, the better it gets and the more likely it is to attract and retain users.

• Multi generational users increase the platform’s reach and relevance. Facebook started with younger audiences in the United States but now it skews middle aged to older. Since a primary revenue source for Reddit is advertising, it creates opportunity for marketers and advertisers serving different types of potential customers.

• The variety of users makes it more resilient as the external market shifts and trends change. Even if it loses some users to new trends, the user base is broad enough which limits the impact of the churn.
  

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Why look for moats?

…To understand what makes a company tick outside of its financials.

Here’s the thing about financials. They are the most important indicators of success of a company over time and during a given period. They show you what moats can deliver in money terms.

That said, the financial performance of a company pertains to results in the recent past. Forecasts, if grounded in fundamentals, give you some idea of what to expect in the future. But, they are also vulnerable to shifts in the internal or external environments.

In order to get a more reliable picture of what the future may hold, you need to marry the current and forecasted financial picture with a more qualitative assessment of other factors like moats.

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Unlike its peers, Reddit took longer to achieve widespread popularity and build a large and influential user base. Its moat has developed over several years. It will continue to do what moats do - protect the company’s competitive advantages in the near-term at least. Moats don’t last forever unless the company continues to update and upgrade them.

Moats are also not enough to bring superior returns over the medium to long term unless the company proactively continues to monetize them. Reddit has historically been slower in successfully capitalizing on its moat. Maybe it is the pressure of being a public company but it has recently done a much better job as shown through its recent, improved performance. For more on this, read its annual report.

One of the risks to Reddit’s goldmine of human experience data is the growing prevalence of bots on all social media platforms, including Reddit. They are notorious for spreading misinformation and spam. Reddit has put measures in place to detect and ban some bots but it is work in progress.

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Like most events with winners and losers, the answer depends on whom you ask.

‘Reverse acquihires’ have quickly become the favored model for tech companies that acquire only some parts of an AI startup, namely the key talent and occasionally, some technology.

These deals are problematic for multiple reasons. For the startup, now a vestige of its former self, the rank-and-file employees left behind have to begin from square one again. No windfall exit gains for them! Venture capitalists and other investors get some returns but not as much as when a startup exits via an IPO or a traditional acquisition. Often, the startup ends up pivoting in a less ambitious business direction or shutting down.

As for the reverse acquirer, one of its goals is to bypass regulation that typically kicks in during mergers and acquisitions of a certain size and character.

Clearly, these are uncomfortable truths. We can continue the age-old debate about the (un)fairness of capitalism. But, there’s no time for that. The AI race is on and it’s fierce.

Are speed and competition an excuse to break things? Certainly not. And pretty much every article covering this topic in the media points that out.

Just for argument sake, I decided to take a contrarian view to write this article. I put myself in the shoes of the ‘reverse’ acquiring tech companies which are currently deep in the throes of the AI arms race. If speed and battling the competition are their priority, reverse acquihires or some version thereof, might be the only way for tech companies to scale quickly with AI. Or they risk falling behind.

Through that lens and with judgement set aside for just a moment, I discuss the reasons why reverse acquihires make sense right now and what the AI hiring landscape could look like once the dust settles.

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The theory and practice of reverse acquihires

If you hadn’t heard the term ‘reverse acquihire’ prior to 2024, you are in the majority. Its origin can be traced back to mid 2024 when tech giants Amazon, Google and Microsoft partially acquired certain AI startups.

The word ‘acquihires’ (sans ‘reverse’) has actually been around for a couple of decades or longer to refer to deals in which struggling companies are acquired primarily for the talent (hence the word ‘hire’ in the name). In typical acquihires, due to the poor performing nature of the business, the acquirer can get the talent (and occasionally other assets or capabilities) for a lower price.

‘Reverse’ acquihires occur when the typical acquihire model is flipped. Purchasers acquire talent or license technology from high performing or promising companies and usually pay premium prices. By doing so, they bypass the regulatory issues that arise during traditional full-company, high-priced acquisitions.

Just to seal the definitions discussion, in traditional acquisitions (that are neither characterized as acquihires or reverse acquihires), the buying company acquires the target to get access to its products or services, talent, customer list, brand, property and everything else. Like the time in 2014 when Facebook bought all of WhatsApp including its 450 million Monthly Active Users (MAUs) at the time for $19 billion.

Selected examples of recent AI reverse acquihires

• As one of the first examples of a reverse acquihire, in March 2024, Microsoft absorbed most of the employees from Inflection AI to start its own consumer AI unit. It also paid $650 million to license Inflection’s technology. Inflection, which until then had been developing multiple AI products, decided to pivot to merely licensing its technology to other enterprises once its leadership team left.

• In June 2024, Amazon hired most of Adept AI’s staff and paid $330 million to license its technology. Some of Adept’s employees stayed back in the startup to manage the licensing business. However, after the brain drain, Adept was no longer in the same business that it started with.

• In August 2024 Google considered fully acquiring Character AI. Eventually, it settled on just licensing its technology for $2.7 billion and poaching its co-founders and other executives. Character AI is still in business as a separate, independent company.

These initial reverse acquihires, in comparison to the recent spate in mid-2025, can be considered a good deal. All three companies were starting to struggle and putting themselves up for sale seemed like the best outcome.

Adept, for example, was valued at $1 billion in 2023 but had lost some steam by the time Amazon hired its staff in 2024. Inflection was once valued at $4 billion and building its own AI foundation models. But the costs of scaling had disempowered it, signaling a shaky future.

Repaying the Investors

In each of these reverse acquihires, the buying (or poaching) company paid a licensing or other fee to the target company. The payment also provided an opportunity for the startup to pay its investors.

In most cases, venture capitalist and investor return on investment wasn’t as high as it would have been with a traditional startup exit. When Microsoft reverse acquihired Inflection, investors were returned 1.1 to 1.5 times their investment, a far cry from the desired 10x+ for early stage investments. Certainly not how much you would expect to get from a startup that was valued at $4 billion barely a year ago.

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In the previous three examples, the reverse acquihire model of acquisition was used once the possibility of an outright acquisition dimmed for various reasons. They weren’t initially intended to be pure talent grabs.

At some point soon after, Amazon, Google, Microsoft and their tech rivals noted the merit of using reverse acquihires. From then on, we have seen it emerge as a way for the acquiring companies to have their cake and eat it too. Here are some examples.

• Meta paid $14 billion for a stake in Scale AI to entice its CEO Alexandr Wang to lead Meta’s newly created Superintelligence unit. This led OpenAI and Google to end their contracts with Scale AI which also let go of 14% of its remaining workforce. Evidently, the move was a big hit to Scale AI’s current and future earnings.

What is fascinating is that it isn’t only Big Tech that is resorting to reverse acquihires. Fast growing startups are also using them to compete on more solid ground, especially in the enterprise space where their reach is limited. Here are some examples.

• OpenAI (well…..it’s still considered a startup albeit a mature one by now) almost bought all of Windsurf for $3 billion. The plan fell through either due to the role played by Microsoft, a key investor and partner of OpenAI or because Windsurf was trying to push more for OpenAI to invest in it. We don’t quite know the exact reason.

  
  But, soon after, it was announced that Windsurf’s leadership team and some AI researchers/engineers had moved to Google which offered $2.4 billion to license its technology. The rest of the company was swallowed up by AI coding startup Cognition for a small fraction of what OpenAI would have paid.
  

  

  
  

• In July 2025, AI coding startup Cursor’s parent Anysphere bought a startup called Koala. Not all of it. Just its top engineers! Unfortunately, in this case, the four-year old, AI-based CRM startup Koala is shutting down its operations in spite of recently raising funds to continue operations.

• In August 2025, Anthropic brought on most of the team at Humanloop, a prompt management platform. None of the other assets or capabilities were acquired by Anthropic.

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Why reverse acquihires might be the only way

……for now. It won’t be the only way for too long. Especially as regulators start cracking down on them. But, at this stage in the AI race, they seem to be the optimal strategy for the acquiring companies. Here are some reasons why.

1. This AI race is different from previous tech wars

The unfolding AI saga is different. Every big tech company is highly motivated to dominate. And that is putting it mildly.

Many articles that discuss competition in the AI sub-industry refer to it as an ‘AI arms race’. It definitely resembles one.

In military parlance, an ‘arms race’ refers to a competitive buildup of military weapons between nations or groups. Each side continuously escalates its capabilities in response to the other's strategies.

In today’s AI world, there is intense competition between the Big Tech companies to develop the most advanced artificial intelligence capabilities such as AGI and Superintelligence.

AGI as “Artificial general intelligence (AGI) is a hypothetical stage in the development of machine learning (ML) in which an artificial intelligence (AI) system can match or exceed the cognitive abilities of human beings across any task. It represents the fundamental, abstract goal of AI development: the artificial replication of human intelligence in a machine or software.”

Artificial Superintelligence as “Artificial superintelligence (ASI) is a hypothetical software-based artificial intelligence (AI) system with an intellectual scope beyond human intelligence. At the most fundamental level, this superintelligent AI has cutting-edge cognitive functions and highly developed thinking skills more advanced than any human.”

Their underlying belief is that the winner will take all. And whoever gets to AGI first also gets the first mover advantage of potentially controlling the foundation technology and the next computing platform entirely.

It’s quite the prize. Hence, it’s quite the race!

The release of advanced Generative AI and Large Language Models (LLMs) in 2022 intensified the competition as this technology not only became accessible to everyone but also that individual users and enterprises saw the immense opportunities to change their lives with it.

Almost everyone by now recognizes that if we combined the cognitive capabilities of a human with the speed, memory, reliability, and scalability of high-performance computing, every human would become superhuman and who doesn’t like that idea! Businesses, in particular, would achieve much more productivity with fewer resources and lower costs. Profits are then likely to follow.

Competition for resources and capabilities to develop AGI is at its highest

The convergence of multiple factors, the combination of compressed timelines, massive resource requirements, scarce talent, and both corporate and national security implications make this competition uniquely intense compared to past tech battles like the mobile platform wars or search engine competition.

Slowing down for any of these ambitious tech athletes would mean falling behind in the AI race. They also need to achieve the intermediate goals toward AGI such as market leadership in AI applications (search, productivity, agentic tasks), control over data access and of course, getting the best talent on board to help achieve them.

Once they achieve a milestone, the need of the hour is to scale quickly before a competitor catches up and grabs market share. Tech companies are being proactive but also extremely reactive to a competitor’s move, which catalyzes all actions.

In reality, this race is also between nations, particularly the United States and China. But, that is the focus of a different article.

2. Reverse acquihires are easier than risking the slow and unpredictable regulatory process

…especially if you don’t want anything else from the target.

A key reason why reverse acquihires have become popular in recent months is that this format allows the acquirer to avoid the slow and unpredictable regulatory process.

Take the example of Adobe and Figma. In 2022, the two companies reached an agreement for Adobe to fully acquire Figma for $20 billion. After a year of a regulatory, anti-trust review, the deal had to be called off in December 2023. In this case, it was the European regulatory bodies that had deeper concerns that the U.S.

Back on point, the Biden administration, which was in the White House when the first three reverse acquihires took place, had started scrutinizing them and becoming more stringent with technology deals. Until then, startup acquisitions were not on the radar as much.

According to a research paper on alternative forms of exits, “Between 2012 and 2019, enforcers challenged only three startup acquisitions. Between 2020 and 2023, they challenged fourteen. Lawsuits or the credible threat of lawsuits killed deals between Adobe and Figma (design software), Sanofi and Maze (pharmaceuticals), Qualcomm and Autotalks (automotive), and Google and Wiz (cybersecurity). The chilling effect spread across Silicon Valley, putting would-be acquirers, founders, and VCs on notice that acquisitions by large incumbents were risky.”

This regulatory concern gave rise to creative new forms of making acquisitions; in some ways a better one. Reverse acquires offered an optimal way to get the talent, access to the technology and avoid the regulatory process. It also allowed companies NOT to buy the parts they didn’t want.

In 2025, reverse acquihires have only increased in number and payouts. European regulatory agencies have already put in rules that reverse acquihires are ‘reportable transactions’ that will undergo review. In the United States, although the Federal Trade Commission and the Department of Justice’s Anti-Trust division under the Trump administration have not taken action as yet on reverse acquihires, they are watching and asking questions. It is only a matter of time before they ask many more.

3. The time and price is right

Given the competitive dynamics related to AI, the right time to acquire talent, is as soon as you spot it. Because if your company does not bring them on, your competitor will.

That is exactly what happened with the OpenAI - Windsurf - Google case. When OpenAI’s acquisition process for Windsurf slowed down, Google swept in and hired Windsurf’s leadership team overnight. By that point, Windsurf was on the industry radar as a prime candidate. If not Google, it would have been one of the other tech companies. There are dual goals here; acquiring talent but also neutering competition in one fell swoop!

If you start doing the salary math at any point, it would be natural to wonder why Google would pay $2.4 billion just to hire a few people from Windsurf. That is another trend that differentiates the AI race from any others in the past. Companies are willing to pay outrageous amounts to recruit talent. According to many reports, Mark Zuckerberg personally approached star AI founders or operators and offered some of them 100s of millions of dollars in compensation to join Meta.

Which brings me to the point that the price for talent in dollar terms is ridiculously high. But, the hiring companies don’t seem to blink at it because their focus is on how quickly that will help them move forward toward AGI. Presumably, it would be higher if they bought the entire company outright. So, the price, regardless of what it is, is right.

To sum it up,

• IF you were in an intense AI arms race battling hyper-competitive, well-endowed rivals; and

• You wanted to fill the talent gaps in your arsenal; and

• You wanted to avoid regulatory reviews which would slow you down; and

• You were willing and able to pay a high price to get what you want; and

• A reverse acquihire were the easier solution that checked the above four boxes, would you go for it?

That might make sense for an acquiring company. But,…..

Why would talent go from being a big fish in a small pond to a small fish in a big pond?

In other words, why would talented founders and star researchers leave their prestigious position to become just another employee in a large, tech company?

As some of those founders discovered that after some initial success, building foundation models or coding platforms is costly. Startups don’t have the technical infrastructure that can support them. They create it as scale, often after raising and spending more money.

Although the plans starting out might have been promising, the reality of building startups in capital-intensive areas like AI is much more about navigating pitfalls.

Finding a buyer willing to pay a high price for a startup that is only half baked (or still baking) can also be difficult. So, why not accept a good offer from a larger company where access to funds is easier, the infrastructure already exists and you get to continue to work on the same or similar problems as your original startup! Why then wouldn’t you pick the cushier life after having tried the harder one?

What the AI hiring landscape will look like when the deal-making dust settles

An article in the Wall Street Journal (WSJ) published yesterday reported that Meta has now frozen hiring for its AI workforce. They had already hired ~50 AI experts using various means including reverse acquihires. Hard to say how long the freeze will last. If I were to predict, it would be until a competitor makes a move that feels threatening. Tis the way things seem to be happening in this industry right now. Many proactive actions but an equal number of reactive ones.

At some point, the hiring frenzy will abate. According to another article in WSJ titled ‘Big Tech Is Eating Itself in Talent War’, the writer suggests that startup culture itself might get eroded if the current hiring patterns continue.

“If the reverse acquihire trend persists, there is a reasonable chance many people who would have been bold enough to join a risky startup will give more weight to their other options. They might instead go directly to big tech companies, in what might be a safer route for themselves but one that makes the pool of available startup talent shallower.”

Although that article sounds reasonable, I might not go that far. First, I don’t think this trend will persist beyond 2026 although several other articles seem to believe that.

Regulators will eventually act and get in the way. Sure, they haven’t had a great track record of regulating the technology industry in the past. And yes, the current administration is seen as being more pro-business than the previous one. Even so, the Trump administration is moving forward with the antitrust cases against Google, Amazon and some others. They could start cracking down on reverse acquihires at any point if the trend continues.

Regardless of whether it persists or not, I agree that some people will try to go directly to big companies. But, not because joining startups will seem risky due to reverse acquihires, as the article contends. People join startups for many reasons and job security isn’t top on the list. It will be because Big Tech is best situated to ‘win’ the AI race compared with any startup out there.

Big Tech has the resources, the infrastructure and their eggs are not all in the AGI basket. Each of them has other businesses that drive revenue, whether it is Google, Meta, Amazon or Microsoft. They will continue to be sought after as desirable places for those who want to put their fingerprints on AI, regardless of the reverse acquihires trend.

That also means consolidation of talent and AI technology in the hands of a few. With that, we have come the full circle back to the starting point and yet another uncomfortable truth.

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In June 2025, Wix announced that it is buying vibe-coding AI startup Base44, a 6-month old, bootstrapped, mostly solopreneur-led startup for $80 million.

Wix has acquired companies before, including a couple of AI ones like GenAI video creation platform Hour One or e-commerce gift card company Rise.ai. What makes the latest deal different is the veritable hype around its acquisition target - Base44.

This article is about this acquisition; specifically why it is a very smart move by Wix, what makes Base44 a special target and what other companies can learn from this acquisition.

What I’ll cover -

• Brief overview of Wix

• More about Base44 with some fascinating details

• The acquisition - goals, structure and price

• Learnings and takeaways for large companies and startups

Let’s jump in!

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Mirror, Mirror on the Wall, Who’s the Prettiest Website Development Platform of Them All?

Wix! That was my impression anyway. Apparently most of the Internet agreed with me. Wix makes it easy to design cool-looking websites using drag and drop tools.

Here’s what PC magazine thinks about Wix as of 2025.

Full disclosure - one of my websites is built and hosted on Wix. I vetted a few different ones such as Squarespace, Weebly and Wordpress before settling on Wix. I finally chose Wix for the same reasons that most of its users do - because it had attractive templates and I did not want to hire anyone to put the site together.

The DIY process was super simple for someone like me who is tech-savvy enough but neither writes code nor has the patience to spend hours setting up a website even when using no-code tools.

Brief Origins and Growth Story

Wix was started in Israel in 2006 by Avishai Abrahami, Nadav Abrahami, and Giora Kaplan who realized that it should be much easier to build a website (Hear! Hear!).

The founders wanted to democratize website creation such that anyone could build a beautiful one without using code or hiring expensive developers. At the time, competition was sparse when it came to design capabilities. Most other platforms lacked inspiring design options and were harder to use.

These two priorities of design flexibility through the drag-and-drop editor and customized templates as well as aesthetic ways for people to tell their diverse stories is what made Wix stand out.

This focus also helped it build a loyal customer base pretty early on with almost 500,000 users in its first year and $1 million in revenue within 3 years of launch.

These days, that seems like a long time to hit that number, given that Base44, the company it acquired, reached $1 million in just three weeks after launch with mostly one person at the wheel - its founder Maor Shlomo.

But, in the mid-2000s for Wix, it was a different ballgame!

It is believed that there are 8 million live websites created with Wix, more than Squarespace (~6.5 million) and some of its other popular competitors but not as many as Wordpress (~31 million), which is the biggest one out there! In spite of that, Wix is the market leader with roughly 24.3% global market share (as of 2023) and over 250 million registered users (2024).

Wix monetizes its business in multiple ways through web hosting, website domain sales, tiered subscriptions, e-commerce tools and apps - now pretty typical for most website development companies.

Its target audience has been mostly small and medium sized businesses and solopreneurs. It continues to develop new features that solve customer pain points related to, not just building a website, but also operating one, such as customer relationship management and marketing integration tools, among others.

By now, Wix has built an ecosystem for website development and management. This playbook isn’t very different from most new companies that start with a niche and a goal to solve a specific pain point. They then expand to add more features and a complementary suite of tools after listening to users.

Innovating with AI

Wix has been embedding AI into its tools for some years now. Consistent with its beginning objective to make it super easy to develop appealing websites without code, in 2016, it launched Wix ADI (Artificial Design Intelligence) to take its no-code capabilities deeper. ADI made it even easier to construct a website with users answering a few questions to get a fully designed site up and running. This helped them acquire and retain users.

Recently, it formed partnerships with tech biggies like Google to offer Gemini to its users and Meta to allow integrations to WhatsApp and Instagram. And one with Alibaba to offer drop shipping through Wix with the expectation that it could attract new users.

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Going Public

Wix went public in 2013 by listing on the NASDAQ. Like most companies that go public, it now had the funds to grow faster. You can see a snapshot of its stock price movements over the last few years.

Like most companies with online business products, the pandemic years were good for Wix’s stock price. That peak is obvious in 2021. Things stabilized a year later and the stock found its new mean. In the more recent past, there is an almost 75% increase in stock price in 2024, which was around the time the company started to double down on bringing GenAI into its ecosystem. Clearly, that speed to market is paying off.

Let’s dive into Base44 next.

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The special case of Base44

Base44 is an AI-based, no-code app building platform started in Israel by Maor Shlomo. You can use natural language (i.e. text prompts) to build an app on Base44. You can also create websites, games and the like.

It is one of the fastest and craziest AI startup stories - it went from 0 to 400,000 users and $3.5 million in revenue in less than 6 months. Of that, it reached $1 million in Annual Recurring Revenue in just 3 weeks, never raised capital and did not go on a hiring spree.

The company launched around January 2025. By May 2025, it had profits of around $200,000. The founder ran the show pretty much the entire time. Toward the end of its single life at the 6 month mark, it had about 8 employees.

This is what makes Base44 special - that a solopreneur could take it from 0 to $80 million+ in 6 months!! And it definitely won’t be the last such entrepreneurship story. We are likely to see many more of these in the near future.

Ok, back to what it does. While several other tools out there offer some of the same design capabilities as Base44, only a handful currently include the back-end infrastructure or “batteries” including integrations and management tools.

In other words, you can use Base44 to develop and deploy your idea pretty much end-to-end into a functional application instead of using one tool for design, another for integrations, a third for analytics and so on. It’s like having your own development team on one platform….except that it’s not a human team.

Here are some more highlights about Base44 -

• Starting up - Shlomo started the company as a side project when he went looking for a solution for his girlfriend who wanted to build a web app for her business. He tried drag-and-drop website development platforms (like the type that Wix offers) but the mobile versions would get messed up and not meet expectations.

• First users - The first users were Shlomo’s friends and members of his social groups. Those early customers started creating cool new apps and SaaS businesses and sharing them on social media. New users kept coming. Shlomo also incentivized users to share their creations on Base44 with the public by giving them extra credits to use the platform.

• Growth - Most startups take off slowly at first and follow the hockey stick path like the image below. That path often takes years. Not for Base44! After an initial launch on Product Hunt in mid-January that didn’t perform well, Shlomo started building in public (LinkedIn) which is when it really took off. Building in public refers to sharing your startup journey on social media openly with the ups and downs. Nowadays, it is a common marketing strategy for individuals and entrepreneurs.

Exploding user growth is definitely desirable for a startup but it also brings with it a litany of issues. Many of those additional users need help with troubleshooting and other service needs. Rapid growth can also strain existing infrastructure and resources which need flexibility to expand and scale. And service disruptions can occur even as speed becomes of utmost importance.

To respond to these imperatives, startups need to pour more resources into the business and hire more people to tackle the different moving parts. In other words, startups need more funds at this inflection point. And founders have two alternatives -

• raise more money from VCs and investors or

• consider being acquired

As you know, Base44 decided to go with the second option. Some older and/or large startups consider going public through an IPO but that is a less feasible option for very early stage startups for many practical reasons.

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The Acquisition

Even before Wix actually approached Shlomo, people were posting on social media that Wix should buy Base44 before it got “too big”. Sometimes, the public just might know best?? Eventually Wix got in touch, the companies had many discussions and the acquisition went ahead.

As Shlomo told the Calcalist “There’s no better fit. Wix is probably the only company that can help us scale and distribute globally without slowing down our development, maybe even accelerating it,” clearly acknowledging the need to address the issues mentioned previously.

And so it has. Since the acquisition went through in June 2025, Base44 has released job postings, improved payments integration, upgraded security and put together a roadmap for its new future. As Shlomo said on LinkedIn “We’re also, finally, building a world-class GTM and engineering team around Base44.”

For now, Base44 is likely to be a stand-alone entity, i.e. a part of the Wix corporation but its product won’t be an integral part of Wix’s website development platform. Of course, that might change quickly in the future.

Deal structure

Under the publicized terms of the agreement, Base44 was purchased for an initial payment of $80 million with potential additional earn-out payments based on its post-acquisition performance. While the earn-out payment details are not public, the incentives are high for Shlomo and his team to continue to innovate and grow the business. On that note, $25 million from the initial payment was set aside to be distributed to employees to retain them.

Strong strategic fit

Wix and Base44 have a strong strategic fit which increases the chances that this acquisition will have a multiplier effect for both.

• Similar startup story and goals - Both Wix and Base44 set out to create no-code ways to develop websites and apps respectively. Over time, each company wanted to ensure that users would have a one-stop shop for development, deployment and management of their product. They not only had strongly aligned goals but also aligned paths on how to achieve them. As Shlomo said it best himself, “Same product category, same vision, same DNA, same customer obsession. Many overlapping user profiles.”

• Focus on using AI - Both companies recognized that the future is AI-driven and building AI capabilities for users should be the main ingredient in any business plan for the next few years.

• Founder Proximity - In a world in which distributed teams work smoothly across the globe, being in the same geographic reason should not matter. But, in some cases, it should and it does. Like this one. This is just the beginning of the journey for a larger, publicly listed tech company and a 6-month old startup that, for majority of its short life, was run by a solo founder. If the plan, for now at least, is for Base44 to run independently, staying close to the home base will be useful as the relationship develops further. On the note of ‘home’, which is Tel Aviv, Israel for both Wix and Base44, having the same cultural background can not only help but also avoid some of the barriers that international businesses often face.

  
  

  

Learnings and takeaways from this acquisition

For larger companies like Wix

Increasing competitiveness through related diversification

The tech world is changing very quickly. With the rapid growth in GenAI, most companies are under pressure to adopt it as customers seek AI in all products and services.

With the website builders market slated to reach $3.58 billion by 2031 (from $1.97 billion in 2023), did Wix have room to grow without the Base44 acquisition?

Possibly.

If it continued to add more AI features to its suite and take a percentage of the expected growth in the website builders market, would the additional costs of new features for the same or incremental users be significantly outweighed by revenues? Or does it make sense to also broaden its reach and conquer related, adjacent new worlds?

The company addressed some additional challenges of staying competitive in its annual report (2024) as -

“In order to remain competitive, we must continue to develop new solutions, applications and enhancements to our existing platform, which require us to compete with many other companies for software developers with high levels of experience in designing, developing and managing cloud-based software, and emerging AI driven technologies. Our principal research and development activities are conducted from our headquarters in Tel Aviv, Israel, and we face significant competition for suitably skilled developers in this region, in particular given the growing number of local companies that are expanding their development activities, and the growing number of multinational corporations establishing a presence in Israel.”

All things considered, there were at least two options in front of Wix as it faced the future -

• to continue to add more AI capabilities to its website and app (version of the website) platform, whether by building, buying or licensing them from existing companies; i.e. adding more depth to its current product offerings
  or

• to jump into the vibe coding, app and AI-assisted software building future. In this case, the apps aren’t just mobile versions of the website as in the previous point but apps that are software that anyone can build for work or play; i.e. adding more breadth to its current product offerings

While continuing to do option 1, Wix also chose to diversify its business into a related area viz. the second option. There are some clues to that end in the quote above. At that time it released its annual report in 2024 though, Base44 did not exist.

Rather than only competing with other website development companies, the acquisition allows Wix to participate in the shifting market by opening its doors to a related market of users who aren’t looking to build new websites but want to develop new stand-alone apps for themselves or their businesses.

Additionally, Wix was able to find those AI capabilities right at home in Israel, where Base44 is also headquartered, thus solving their other issue of being able to find the right talent close to home.

Timing the market

Whether the stock market or the housing one, they say that you can never time the market. Makes sense. All ‘timed-right’ decisions get that qualification only once they have been tested over time for ‘rightness’. That being said, if you can make data-driven decisions in the present after taking all relevant factors into account, you increase the probability that a certain decision will be proved right.

By all reports, Wix’s decision to buy Base44 got the timing right for two reasons.

• It was able to spot the opportunity and act quickly to buy a fast-growing, profitable, home-grown, AI-native company at a time when every company is looking to adopt AI inside the organization and use it in its products or services.

• In sync with the public posts which suggested that Wix should acquire Base44, before ‘it gets too big’, Wix bought Base44 for $80 million which is a 22x multiple on Base44’s revenues of $3.5 million.
  

  That multiple is pretty high for a startup of that size and age but it is not an extraordinary multiple for tech companies. (Figma was reportedly offered a 40x-75x multiple by Adobe although that acquisition was eventually blocked by regulators).

  The price point of $80 million is low compared to what Meta paid for a stake in Scale AI ($14.3 billion) and OpenAI almost paid for 4-year old AI startup Windsurf ($3 billion) or what Google eventually ended up paying for Windsurf’s talent team ($2.4 billion). Had Wix waited a few more months or even years, it would have had to pay much more for Base44.

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For startups like Base44

Increasing competitiveness by leveraging an established user base, markets, distribution

Base44 had so much momentum when it got acquired that it could have taken an alternative route, raised venture capital (VC) money and continued to grow. But then, it wouldn’t get the benefit of Wix’s user base, access to its international markets, existing infrastructure and brand.

VC funding also comes with high expectations of steep month over month growth, which is its own kind of pressure. Some startups like Lovable, which offers similar services as Base44, have taken VC funds. Lovable has grown to $100 million in annual revenues in just 8 months with 45 employees. Clearly, a different but also promising path for some.

Being acquired by a larger company might not be the best strategy for every startup out there. For ones that are run by very small teams and want to grow with the support of an established infrastructure, becoming a part of one could work well.

Structuring a favorable deal

Getting paid a 22x multiple and $80 million for your 6-month old startup is a great achievement. But, the additional earn-out payment that Shlomo will get if Base44 reaches certain performance milestones might be where the real payoff lies. Those details have not been made public but presumably, it is significant enough for him to have accepted the deal.

There are many cases of founders who have refused buyout deals in the past. Yahoo offered $1 billion for Facebook several years ago. Facebook now has a market cap of $1.8 trillion. Facebook tried to buy Snapchat for $3 billion in 2013. Snap now has a market cap of $16 billion. Google tried to buy Yelp for $500 million, according to some reports. Yelp now has a market cap of $2 billion. There are many such stories. The founders in all these cases knew that there were higher financial benefits to holding on than selling their companies. Finally, the decision boils down to where founders want to take their companies.

A final note

All friendly acquisitions look rosy in the beginning. If the actual or expected returns, as the case may be, weren’t high, why would either company voluntarily get into it? But, business life is as unpredictable as personal life. Plans can change. Markets can change. Unforeseen challenges can emerge and force change.

It will be interesting to see how the Wix-Base44 relationship evolves. At this point, the future looks promising. Only time will tell.

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Note - None of the articles are in this newsletter are written by AI. Ever! I might use it for research on certain topics but that is where the use of AI ends. Also, none of the articles are sponsored posts unless explicitly stated up front. In some cases, we might earn an affiliate fee if you make a purchase through a link at no additional cost to you.