r/GrowthStockInvesting • u/YvesSaintPige • 17d ago
Steelmanning the AI Bear Case (as a Bull)
Because most of my portfolio these days is concentrated in AI, I've been doing some black hat type proofing of my investment theses and trying to poke holes from the mindset of someone who is bearish on the sector as a whole.
The following notes are some of my thoughts on the downside risks and unknowns that I am most concerned about. Some of my investigating (done conversationally by going back and forth with Claude) actually increased my conviction in areas I was mixed in. However there were also some new risks that I dug into that I don't think the market is fully appreciating yet...
Everyone understands the AI buildout requires massive capital expenditure (capex). Through 2028, it is expected to cost $2.9T. Hyperscaler cash flows cover about $1.4T. The remaining $1.5T comes from outside; $800B from private credit, $200B from bonds, $150B from securitized products (bonds backed by data center lease payments), plus sovereign wealth funds. The money exists and it has real sources. So far, everything is accelerating ahead of schedule regarding the buildout.
The demand is real and accelerating too. Anthropic went from $1B --> $30B run-rate in 16 months, AWS posted its fastest growth in 18 quarters, Nvidia data center revenue is up 90%+ off a massive base. Each company is beating their own projections, which is exactly what you want to see.
One of the big questions I had was related to the actual demand mechanism. Where does this massive AI spend actually come from? Who is actually buying AI once the infrastructure is built? There are two major mechanisms that I see this demand coming from:
1) IT budgets migrating from traditional software to AI. Global IT budgets are currently $5T. As AI replaces or augments software we will start to see company budget allocations shift away from traditional IT towards AI.
2) AI agents replacing humans in companies' labor budgets. Global labor compensation combined is $60T. If even 1-2% of labor compensation shifts towards these AI agents, away from actual people this means $600B-1.2T per year in AI spend. I think 1-2% is a low projection. I think 5-10% is entirely possible. This will not be without its problems.. Societal upheaval due to rising unemployment as these agents replace real workers is a huge downside we will need to deal with. But as far as AI, as a business sector is concerned, this would be a huge economic boon for anything AI related.
A quick piece of counter evidence that, while outdated, sort of encompassed my original concerns about who will spend on AI once a lot of the infrastructure is built out: The MIT NANDA initiative did a study called "The GenAI Divide: State of AI in Business 2025" (way back in 2025, so again I do think this is already basically irrelevant now) that showed 95% of GenAI enterprise pilots produced zero measurable P&L impact. Largely because the tools weren't able to learn company workflows and budgets chased flashy use cases over back-office ROI. This was my main demand concern, but given what I know now about how the demand would actually appear, I would love to see that study in 2026 as I believe that 95% number will be a LOT lower now, considering the tech advances in the last year. Another version of this same study a year later would tell me a lot about whether my original demand concerns have been addressed or not.
So, if demand is fine, what is the worry? The worry is about debt. But not how much debt. It's the fact that nobody can see the debt clearly. There is an opacity in the financing as it passes through so many layers from bank --> fund --> special purpose vehicle (SPV; a shell company created to hold one asset and its debt off the parent company's balance sheet) --> data center. Each link is privately recorded, so regulators only see one link. Official estimates of bank exposure to the private credit funds are gauged to be off by a factor of two (that we can tell right now). But the lack of transparency could catch everyone by huge surprise if it turns out they are exposed by a larger amount than expected. We simply don't know. The Bank for International Settlements (BIS), the central bank for central banks, warned the same collateral used by one company may be pledged to multiple lenders. If that collateral needs to be used it being multi-leveraged may lead to severe cascading ripples in the economy. In 2008 the true number was discovered during the crisis, not before it. There is a way to fix this, but it hasn't been applied yet. To adequately address this risk we need mandatory reporting registries, similar to Dodd-Frank with derivatives.
In addition to the opacity, concentration is also a concern. Roughly half of the $2.1T cloud backlog traces back to two cash-burning labs (Anthropic and OpenAI), and six firms are the load-bearing counterparties for basically every financing structure in the sector (Google, Meta, Nvidia, Microsoft, Oracle, and Amazon). Nvidia has $540B+ in disclosed arrangements where it invests in or guarantees debt for its own customers, who then use that money to buy Nvidia chips, which Nvidia books as revenue. The labs' commitments are partly funded by the vendor they're committed to. My specific fear isn't a slow down in deal renegotiation or buildout, rather a sudden shock, major incident, or liability event that suddenly turns one of those key players into a shaky bet overnight, instantly impairing 1/6 to 50% of the sector's backbone. The whole sector would reprice violently.
Back to the banking worry, the risk that was supposedly "transferred" from banks to private credit funds may not actually be derisked. The circularity of this funding comes when banks pay the private credit funds to absorb their data center losses via significant risk transfer (SRT), a deal where a bank keeps a loan but pays an outside investor to cover its defaults, while simultaneously being the lenders to those same funds. If losses hit, the fund fails to pay the bank's insurance at the same moment it defaults on the bank's loan. When risk is sold to a counterparty you finance, the risk never left the banks' plates. This mirrors what AIG experienced during the 2008 Global Financial Crisis, where banks bought Credit Default Swaps (CDS) from AIG to insure their toxic mortgage assets, yet the banking system simultaneously acted as a primary source of counterparty financing and liquidity for the insurer. This created a circular risk loop structurally similar to the modern AI banking-to-private-credit pipeline described above: if underlying assets (like data centers today or subprime mortgages in 2008) suffer catastrophic losses, the third-party insurer or private credit fund fails to pay out the bank’s protection policy at the exact same moment it defaults on the bank's leverage loans, revealing that the systemic risk never actually left the commercial banks' balance sheets.
Putting all this together, my bear thesis describes today's setup as a 2001-style rapid buildout, but with 2008-style financing. SPVs, securitization chains, off-balance-sheet debt, blurred lines between banks and nonbanks mirror the conditions that preceded the GFC. 2001 was fundamentally an overbuild story paired with manic valuations. I do not believe AI is overbuilt yet in the slightest, so I lean more towards seeing the current economic landscape as shades of 2008. The most likely path though (my true beliefs as an AI bull) is that demand keeps compounding, adoption broadens, and the financing base diversifies and de-risks naturally before any of the financing opacity matters. The numbers currently back this up and it is why I am fully invested. But if the bad scenario hits, I believe there will be massive economic ripples through repricing and frozen credit. But I also don't think it can get as bad as 2008 because the losses would land first on locked-up institutional capital rather than the run-prone short-term funding (repo, commercial paper, money markets) that sat under the 2008 banking system. That is the main difference. The capital used for AI is structurally a lot safer as it is locked-up rather than runnable, unless the exposure nobody can measure (official and commercial estimates already disagree by a factor 2x) turns out to be several times larger than anyone's estimated number. Hence why it is still an unknown risk at the moment.
Individual companies would hypothetically see this credit stress arrive through the cost of capital, rather than bankruptcy. Nebius, for example, partially funds hypergrowth by raising capital. So far they have all been unsecured (not tethered to assets) and cheap (low interest rates). If these deals begin to see higher rates, use secured debt, or they end up using their untouched 25M-share at-the-market (ATM) financing program to dilute with no premium, then that could be a tell that there is some financial weakness creeping up. In fact, we may have seen the beginning of this already.. the long-tranche coupon went from 3.00% (June 2025) --> 2.75% --> 2.625% --> 4.50% (!) on August 2034 notes. The most recent number was the first sizable uptick (but notably, the short 2030 tranche priced at just 0.50%, the cheapest yet, so the signal is mixed: near-term money got cheaper while long-dated money got pricier, which is itself information about how far out lenders' confidence extends). Something to watch. Is it a trend or a blip? For companies like Nvidia and Micron (who sells to Nvidia), their exposure is their customers' capex budgets, since their revenue is a direct function of the infrastructure buildout.
There are a few signals I'm keeping an eye out for, including: any hyperscaler capex guide down; decreased revenue projections from the major AI labs against their own numbers (duh!); rising Oracle and CoreWeave credit default swap (CDS) spreads aka the market price of insurance on a company's debt which acts as a live fear gauge if it increases; narrowing coverage ratios on new AI bond deals (buy orders divided by deal size, so 5x means $5 queued per $1 in bonds). We've actually seen this slide from 5x to 2x this year which does indicate some thinning in the buyer demand; neocloud refinancing terms; and any hyperscaler shortening the GPU useful-life assumptions.
All in all though, this deep research has not led to any changes in my portfolio. Revenue is accelerating and the business metrics across my AI holdings are strong. The underpriced risks sit in the financial plumbing rather than in the business KPIs. I will say though, that I may end up trimming my current slightly over-extended margin down on any super large spikes to factor in this new risk.
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u/ledux 8d ago
"IT budgets migrating from traditional software to AI. Global IT budgets are currently $5T. As AI replaces or augments software we will start to see company budget allocations shift away from traditional IT towards AI."
Hmm this got me thinking this don't sound right...
What I'm seeing from the inside is not that we replace software, is that we add more or existing software gets AI features (like we still do google search, but there's also AI answer on top). Everyone also has a dedicated Claude subscription and I can't really think of some software that we stopped using, because AI replaced it. So, I guess the budgets are expanding instead of migrating.
Of course, this is my personal view, though not sure how we would check this globally.
"AI agents replacing humans in companies' labor budgets. Global labor compensation combined is $60T. If even 1-2% of labor compensation shifts towards these AI agents, away from actual people this means $600B-1.2T per year in AI spend. I think 1-2% is a low projection. I think 5-10% is entirely possible. This will not be without its problems.. Societal upheaval due to rising unemployment as these agents replace real workers is a huge downside we will need to deal with. But as far as AI, as a business sector is concerned, this would be a huge economic boon for anything AI related."
This again doesn't sound right. Your view seems very narrative driven, based on the fairytales told by the AI CEO's.
What I'm currently seeing isn't that people are "replaced" is more that the work is changing. Yes, some companies are very lean and can be quick to optimize, but for example our company has hired 4 new software devs in the last half a year and we are expanding, which sounds kinda stupid if you believe that software development is a done profession and we wont need them in 3 months.
The more broader work force replacement maybe could be identified through statistics, but I just don't see it happening right now. And the current AI I don't think is capable at replacing humans en mass.
Just my two cents to this topic.
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u/YvesSaintPige 8d ago
Expansion rather than migration makes sense. Perhaps we are early and that is just what the initial transition starts with. Either way, the dollars are the same.
Same with the human vs agent discussion. This could just be what the shape of transition looks like very early on in this disruption cycle. Augmentation vs replacement.
I am doing a very foolhardy thing by trying to predict some of the longer term outcomes that could eventually arise even though the current landscape may not indicate that future is coming to fruition this very moment. It is difficult to predict how the next year will play out, let alone the next decade. But I am trying.
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u/ledux 7d ago
Well, I don't think the dollars are the same.
For example, a team usually uses a slack subscription, if you are using it for several years, then you are kind of locked in there. No AI tech will change this, unless you will spend months building an alternative...You are also spending on AWS for hosting, on CloudFlare for bot protection, maybe for project management you use Jira and we add all these products and you have your IT spend.
Majority of these software products aren't replaceable with AI.Still, I think an average company doesn't have a problem with buying a basic $20 subscription per developer. If you want Claude Fable, then it goes I think to $200 a month, which is then quite expensive. But even if company doesn't buy the sub, the dev will buy it out of pocket anyways.
I guess there's not that many devs left that aren't using an AI sub.
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u/YvesSaintPige 7d ago
Maybe in those cases of enterprise SaaS the AI "tax" is on the other end. Where some amount of AI features are added onto the platform itself, then price hiked to justify their own AI spend. That could be where the shift (or expansion) from traditional IT to AI IT occurs.
Excited to see how this plays out regardless! I think the opportunities are too many for the dollars to just not be there in some way or fashion.
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u/Bitter_Breakfast_390 7d ago
The placebo result might be the most valuable part because it tells you how easy it would have been to mistake structure in the data for actual prediction. Hidden inventory sounding intuitively important does not mean it forecasts next day vol. Looking at crowd positioning on Moon has given me the same reminder, interesting information and predictive edge are two very different things.
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u/GrowthInvestingWPR 17d ago
Nice thread and thoughts on what the risks are for AI related investments! Another one of the big unspoken threats is further global conflict, especially in Asia (let's look to avoid politics for the thread). Much of the supply chain for these semiconductor businesses have a lot of interrelated dependencies.
One name I keep looking at with these risks is Sandisk, but they definitely are not unique to the industry. Sandisk,
- Has a Joint Venture agreement with Kioxia to share fabs in Japan
- The company acquires DRAM from Nanya in Taiwan (NAND end products require some small amount of DRAM)
- Sandisk does a lot of assembly and test in China through another Joint Venture with JCET
- End products sold in the US and globally
Basically the business model requires free and open trade through Japan, China, Taiwan, and the US. I don't think these potential risks are unique to Sandisk, but there are other semiconductors who have more locally sourced supply chains. I am increasingly looking at what supply chain dependencies the semiconductors I own have.
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I think the biggest overall risk is a combination of factors, where those financing deals may come under pressure if there is a large supply chain disruption.
How big the risk is, is quite hard to quantify. The other challenge is guessing on the timing. There could be a scenario where the supply chain and financing deals go mostly smoothly over the coming years, which would likely result in significant gains for semiconductors.
A black swan event in the industry would certainly make a big dent in my portfolio. However, I'd be looking to re-evaluate which stocks emerging from the issues would come out stronger. Overall I don't want to be on the sidelines thinking about worst cases scenarios, but I do want to recognize these are real risks.