Epoch 2 - Agentic Era

Epoch 3 - Physical and Robotic Era

Epoch 1 - The LLM Era - NOW

I don’t need to explain the LLM era by now. You are living in it.

Epoch 2 - The Agentic Era - Early 2026 to the indefinite future
Perhaps you have some awareness of the agentic era of AI. In this era, AI functions like an employee(s) that you give a set of directives and permissions. It checks in periodically while it repeats various tasks. People have been doing this in earnest since at least January of 2026 when OpenClaw hit tech nerds like a ton of bricks. Since then there have been numerous agentic platforms springing up. The agentic era has yet to be made “Apple simple” for the common man, so this epoch has both been inaugurated but is still largely sequestered to maybe 1-3% early adopters. There are many who build not just agents but teams of agents and sometimes whole departments of agents (marketing department, legal department, graphic design… etc.).

The power users of agentic AI in my life get between 1-4 weeks of work done per day. Most of them are solopreneurs or work in smaller organizations. Agentic AI is a lot easier to deploy the smaller your company or organization is because you have far less rules for compliance, security, and red tape. Those things being said, I have observed three things, to one degree or another, about the power users of agentic AI in my life:

1. SLEEP DISRUPTION - They lose some sleep periodically to get up in the middle of the night to check in in on their agents and give them new permissions or tasks so they don’t waste down time while they aren’t working.

2. BASELINE ANXIETY - I have observed a slight uptick in baseline anxiety in these people. Whether or not they are cognizant of that is another question.

3. DOPAMINE ADDICTION - There is a level of dopamine addiction that comes from insane productivity and the reward cycle of building things so rapidly.

I don’t think these dynamics mean, “don’t do this.” These are just observations that I have had from early adopting power users of agentic AI. Personally, I haven’t played around with it for three reasons - time, learning curve, and I believe I lack the maturity to handle putting the ring of power on my finger. So, what do I know, and you probably better not take my word for it.

Epoch 3 - Physical and Robotic Era - Late 2027 to the indefinite future

The thing hardly anybody is talking about is the era when you see 30 cybertaxis on your commute to work (if you still have one) and people start having Sheldon Cooper’s in their home scrubbing their toilets and folding their laundry.

Do I want something approaching AGI in my home with toilet brush in hand?

Do I want something that smart walking around my home? Do I want it talking to my kids? Does this thing have good enough cybersecurity on it to keep it from the terrorist hacker that wants to hack it and give it the skill find the sharpest kitchen knife to attack me with in my sleep? I’d kinda like to know because I bought the Chinese model that was 70% less than that newfangled Tesla bot.

I’d like my tombstone to read something besides, “Mr. Cooper killed Mike with the kitchen knife in the master bedroom.”

BAZINGA!

In all seriousness, I say all this stuff to say that we aren’t remotely prepared for thinking about the ethics, parenting, or cybersecurity around epoch 3.

Consider this my personal plea for more than just John Dyer to be thinking about epoch 3.

Other stuff to worth reading or listening to:

Even Claude Is in the Dark About Dario Amodei’s Wife—and Her Influence at Anthropic - WSJ

AI and Emotion: Can Machines Feel? (Christopher Watkin interviews MIT’s Rosalind Picard)

I shouldn’t have to say this but if you are new I will remind you, there is no AI writing in here and I don’t hardly read these things over - so you will frequently find typos, poorly written sentences, and other very human artifacts. I both apologize for that (sorry to all my editor friends) but I also treat this more as a feature than a bug in our era of AI slop.

I am also writing up this econ heavy piece because I don’t think you can have a grasp on what is happening in AI apart from having a working knowledge of the increasingly complex and fragile fiscal ecosystem. The invisible hand of the market is perhaps the single most powerful force shaping the worldwide future of AI. While the cone of uncertainty remains very wide on the future of AI, a failure to have a working economic understanding here could be problematic from a predictive standpoint on any number of disciplines including not just macroeconomics, investing, or business decisions but also implications that spill into epistemology, ethics, theology, education, policy, and sociocultural considerations.

A Ton of Caveats
I have hesitated to write this piece for several reasons:

1. I hope that I am wrong and would prefer to be wrong

2. The cone of uncertainty on what happens next in AI is really wide

3. Because of #2, I would characterize what follows as being speculative and highly subjective

4. I don’t like to be speculative and highly subjective in my writing

5. I don’t normally write with a bent towards economics

6. I am not 100% certain that I have all the data right because there are some approximations that I have tried to note

7. I won’t be the least bit bothered if you disagree with any part or the entire thing here. There are plenty of good and empirical reasons to be bullish on AI infrastructure. I would love to hone my own thinking here so I invite dialogue of all sorts.

There are Questions Surrounding Worldwide Daily AI Token Revenue
I have been watching the Silicon Data LLM Token Expenditure Index decline for several week now. This index is a “daily benchmark for large language model inference token prices across different providers.”

This graph is essentially answering the question, “how expensive is it, on average, to run 1 million LLM tokens today?” When you combine this daily price data with data on inference expenditure x estimated inference volume, there are real questions regarding revenue. Admittedly, there isn’t great data on estimated token volume and the best approximations come from tokensperday.com and Epoch AI / Exponential view). If we put these data set together you can make a very crude estimate on daily AI revenues. To be clear, I think there are substantive methodological problems with combining these data sets as I am not confident in the comprehensiveness nor veracity of them. That being said, I am unaware of any better available data:

If (and that is a big “if”) the data from both Silicon Data LLM Token Expenditure Index and Tokens Per Day are correct, there are real questions about deceleration or even decrease in total daily AI revenue. If you think this data and methodology is garbage, I am totally fine with that, just try to read the rest of this essay as a lot of stands without any of these guesstimates. Related to this are publication dates of several open source Chinese models:

• Qwen 3.6 plus - April 8

• Kimi K2.6 - April 21

• DeepSeek V4 Preview - April 24

• MimiMax M3 - June 1

• GLM 5.2 - Mid-June

• Kimi K3 - July 16

• Qwen 3.8 Max - July 19

• DeepSeek V4 - late July/Early August
  
  There are two variables to consider here:

1. How many tokens are used per day

2. How much a token costs

It is possible that the agentic AI era causes token usage to explode faster than the unit cost of a token. If so, then revenues will accelerate again. However, if token prices drop faster than token usage rises, then total revenues will continue to decline.

There will be over a trillion dollars in AI infrastructure capital expenditures (capex) in 2026. As best we understand it OpenAI will have about $25-30b in 2026 revenue and Anthropic will have $35-50b in 2026 revenue. Assuming we go with the high end of these projections and impute $80b in 2026 revenue this is a lot of capex with not a lot of revenue behind it. One might quibble here and say we need to add all revenue from all other LLMs, image/video generation… etc. These things are very tricky to model but are probably in the net total of $125-175b range. So, we have maybe $150b in total worldwide AI revenue chasing over a $1t in AI capex and where there does seem to be some evidence (see above) that revenues are actually decelerating.

It is logically possible that the AI infrastructure buildout in the US was forecasted in a way that didn’t anticipate the proliferation of a lot of open source/weight/model AI, especially from China and for pennies on the dollar.

What happens if the revenue projection forecasts were significantly off and we are just now noticing?

Why 1873, 2000, and 2008 Might Rhyme with 2026-2027
The answer can be found in a mixture of history rhyming with 1873, 2000, and 2008.

1873 isn’t one of those years that just comes to the front of mind but it is an imporant year. It is the year there was a massive financial collapse surrounding the rapid expansion of railroad infrastructure in the United States, especially transcontinental rail. Estimates vary, but railroad capex could have been around 6% of GDP and the projected future revenues from rail ended up being much higher than reality, especially after grain prices fell and freight volume didn’t scale up fast enough. The result was a significant amount of defaults and a dramatic reset in the winners and losers in trains in the US.

2000 is the year most people recall when they think of the dot com bubble bursting. The timeline is straightforward with 1998 to March 10, 2000 being the speculative boom. Rightly so, investors understood that the internet was going to change everything, and it did. The problem is that this doesn’t mean that every website was going to be a huge winner and we now know that most of them would eventually go to zero. Panic and the first round of liquidations took place from April to November of 2000. Then waves of massive insolvency and a recession followed in 2001. Hundreds of dot-coms close up shop, the country enters a rececession, and the NASDAQ loses 78% of its value from peak to trough from March 2000 to October 2002.

2008 is definitely seared more into recent memory for anyone who is now millennial or older. The Great Financial crisis began with a housing bubble fueled by subprime mortgages. Those garbage mortgages were packaged into complex financial instruments called “mortgage backed securities” (MBS) and “collateralized debt obligations” (CDOs). The teaser rates on adjustable rate mortgages end up resetting to their higher rates and mortgage defaults rise rapidly. In early 2007 you see some subprime lenders become insolvent, then Bear Stearns goes under, France’s largest bank (BNP Paribas) freezes funds. Come September 2008 Lehman Brothers files for bankruptcy and Fannie Mae and Freddie Mac enter conservatorship and Wall Street collapses in panic. It takes 6 months for the market to bottom, Congress passes the $700b TARP bailout, and the Fed cuts interest rates to near zero.

So, what do 1873, 2000, and 2008 have to do with 2026-2027?

No one situation maps super cleanly, but they all partially relate in a way that makes you feel like history is rhyming.

With 1873, you have a massive piece of crucial infrastructure needed - railroads. Those railroads were needed for both personal and commercial usage. The buildout was a massive 6% of GDP (note this three times the 1.8% of GDP for 2026 US AI infrastructure buildout ($581b/$32t est. 2026 US GDP). The projected future revenues from the railroads exceeded the actual revenues and this caused cascading debt defaults and dramatically changed who the winners and losers in railroad would be in the US. AI infrastructure is similar in that it is critical, has widespread application, and is a significant portion of GDP.

With 2000, you have a truly world changing technology that brings humanity to an inflection point in the internet. There is no doubt that there will be billions made on the internet with huge rewards for whomever can become digital king of the hill. All of this eventually came true but not until speculation was rampant and valuations unhinged from reality. AI infrastructure is similar in that it is a game changing technology that will change and remake the world but we have yet to sort out who the winners and losers are yet in the tech stack.

With 2008, you have well intentioned financing on something important - home ownership. Those good intentions go sideways when we forget that assessing risk in underwriting is crucial to the stability of the market and when you dramatically loosed or even flagrantly ignore all standards, then buyers flood the market and prices blow up in an unsustainable bubble. To make this scheme possible you create complex financial instruments that hide the garbage and then you have ratings agencies certify it as fresh. AI infrastructure is similar in that you have unprofitable borrowers in startups like OpenAI and Anthropic that cannot afford the compute that they need/want so they deploy increasingly creative tactics to acquire the capital needed. At first you can trade equity for compute but then you only have so much equity to give away. Then you can take on debt for compute but then lenders keep raising interest rates commensurate with default risk and then maybe nobody will lend to you anymore. Eventually you end up with complex circular financing deals that map like this (and even this map is a little old):

It is important to note that in 2008 financial institutions were leveraged 30:1 on extremely toxic assets. The current situation does not have nearly the same amount of leverage on nearly as problematic of an asset.

How is AI Infrastructure Being Financed?
There are at least four main ways that AI infrastructure capex is funded and each of those main ways has several sub-genres:

1. Hyperscalers FCF and Augmented Debt (~55% of total 2026 AI capex) - behemoths like Google, Amazon, Microsoft, Meta, and Oracle who take profits/free cash flow (FCF) and use their own money. More recently some of this has also begun to be financed through debt (typically corporate bond issuances).

2. Private Capital (including all private credit, asset/GPU backed financing, and securitization - ~30% of all 2026 AI capex debt) - deals like the $500b MOU between Nvidia and a number of firms like Brookfield, KKR, Blackstone… etc. - these deals happen now because Nvidia knows that the foundational models are struggling to get the cash needed to buy compute but Nvidia needs people to buy their silicon, so they go and try to shore up the liquidity needed to keep it all going. These deals are primarily for “neoclouds” (CoreWeave, Nebius, IREN… etc.) who don’t have the same FCF to subsidize the structures, land, and power required for their data centers. This money comes in wide range of capital between instruments like private credit, infrastructure equity, pension money, asset-backed loans (collateralized GPUs/land… etc.), securitizations (more GPU collateralizing… etc.), structured leases, offtake agreements, and a few other instruments (insurance… etc.). Offtake agreements require a bit more explanation. If you are a Fortune 500 business who doesn’t own your own data center(s) and you want AI integration in your business then you are essentially stuck “renting” AI from one of the hyperscalers who are hosting some of the foundational model. Many businesses want to make their AI workloads budgetable line item so they enter into a multiyear contract with a hyperscaler(s), model maker(s), and/or sometimes bundled pair. Offtake agreements buttress approximately 30-40% of this category of financing. Bookmark this mentally as we will return as to how this could go sideways for both hyperscalers and model makers if Fortune 500 companies decide to “own” their AI instead of “rent” their AI.

3. Financial Engineering (Circular Financing/Vendor Financing) (~10% of total 2026 AI capex with about 5% of that being purely circular and about 5% being JV debt)- imagine you are in the business of making expensive lemonade stands but your friend wants to start one but doesn’t have the cash to start one up. To accelerate your business you lend the $1,000 needed to start your friends high end lemonade stand but the catch is the lemondate stand must use that money to buy your lemonade startup product. Your friend decides they want to start more lemonade stands and a bank agrees to lend them money to do it but to do it the bank requires the actual lemonade stand to be placed as collateral. You get your money today but your friend takes on the debt and has to pay off the new loan over time and as the physical lemonade stand wears out (i.e. depreciates) over time. Sometimes these deals also involve the giving out the lemonade stand infrastructure in return for an equity percentage in the new lemonade stand venture. In this imperfect analogy a chipmaker like Nvidia will lend money OR trade equity to foundational models (like OpenAI or Anthropic) in return for them buying their GPUs. Nvidia wins because they are able to realize revenue immediately OR they can show in their earnings growth in rapidly appreciating valuations of their ownership in AI-startups whose valuations are skyrocketing. When they no longer want to lend to a particular model maker they simply assist in the handshake with a lender and the lender then collateralizes the GPUs. The risk is passed off to the AI model maker and the financial institution. The technical terms for this particular schema are strategic/vendor equity, offtake guarantees, GPU-backed collateralized lending, and off balance sheet leases (JVs - this is like Meta creating a third party vehicle that builds/owns a data center and Meta signs an exclusive 15-20 year lease/usage contract and the third party vehicle can borrow against its lease with Meta - this keeps debt off the books). Put a mental bookmark here as we will explore what happens how this financial engineering can go sideways in just a minute.

4. Sovereign Wealth Funds (globally this is ~5% of all 2026 AI capex debt) - many countries have concluded that AI infrastructure is critical infrastructure analogous to electricity, water, or national defense. We have seen countries like China, Saudi Arabia, UAE, and others pour tens or hundreds of billions into AI infrastructure with the understanding that their future geopolitical positioning will significantly hinge on being on the bleeding edge of AI. Sometimes the sovereign wealth funds end up with a mixture of sovereign wealth mixed alongside private capital as well.

Three years ago financing AI infrastructructure was straightforward - Microsoft earns money from business operations and buys GPUs.

Now things are much more complex - Anthropic/OpenAI signs B2B long-term compute contract and that contract supports a third party company (typically a special purpose vehicle) that in turn buys GPUs and builds a data center who in turn pays rent from its revenue to the lenders who financed the whole deal.

This makes the work a lot harder for silicon companies like Nvidia because they cannot just make GPUs anymore, they have to invest in the tenant/operators, guarantee compute capacity, and help financially engineer the lending schemas required for adequate liquidity.

What Could Go Wrong?
There are a number of ways that these four financial methods can go sideways and I want to sketch out a few weaknesses with each method before addressing systemic risks:

1. Hyperscaler FCF/Debt (~50% of capex) - prima facia this method of financing is the least problematic because it is simply pre-existing extremely profitable companies plowing their profits into something they think will have high ROI cash flow. The corporate bond issuance is a bit of a nuissance to shareholders and they might ding the stock a bit. But here is the problem, if the return on invested capital (ROIC) isn’t as high as projected then it hurts their future earnings. When those earnings come in lower than expected and/or lower than forward guidance, then the stock price will fall. One might say, “who cares if these behemoth tech companies stock prices come down a bit?” Well remember what the dominant financial advice for regular folks (retail investors) has been for a decade - “just buy the index (“VOO and chill”… etc.). The problem here is that some 40-45% of the indexes are primarily tied into AI infrastructure. Exposure roughly breaks down like this - S&P 500/SPY (30% primary), NASDAQ Composite (40% primary, and QQQ (48% primary). Those numbers go even higher if you account for companies who secondarily relate to AI infrastructure. So, if major companies like Google, Amazon, Microsoft, Meta, and Oracle start to have disappointing earnings or cancel data center contracts then retail and institutional balances are going to take a major hit. This dynamic could be further exacerbated by record amounts of leverage (trading on margin, leveraged ETFs, leveraged individual stocks). One can observe the recent lessons learned in South Korea when a few headlines caused the unwinding of leverage in their index that is over 50% reliant on two memory companies (SK Hynix and Samsung). When highly leveraged assets lose value quickly it can trigger cascading margin calls that spread fiscal contagion. Say a company like Oracle has taken on way too much off the books debt (see image below) and you begin to see the default swaps on the company get more and more expensive due to over-reliance on agreements with OpenAI. Say their stock begins tanking hard and fast but you have leveraged bets on the company and you get margin called, you then have sell other assets to come up with the cash to pay for your margin call. Maybe you sold something “boring and safe” like United Healthcare or Chlorox or an index ETF like SPY, QQQ, or VOO… but now those stocks and indexes are infected with Oracle’s contagion due to elevated leverage:

   

   (Leases not yet commenced only appear in footnotes on corporate financials and sometimes can obscure the overall debt picture and this may be part of the reason why credit default swaps on Oracle have hit an 18 year high)
   

   

   

   So, the big risk here is that the markets are heavily correlated with AI infrastructure and any problems in the narrative OR in actual earnings could cause a painful and potentially violent unwinding. As it stands I will gladly concede that the earnings have largely been there but those earnings are largely downstream from an era where financing was more straightforward and growth in foundational model ARR was growing rapidly. In my view, the TTM and NTM earnings multiples on memory and storage companies justify their current valuations even if they begin to level off in the 2028-2030 time frame.

2. Private Capital (~30% of capex) - the biggest problem that I see here is Fortune 500 companies decided to “own” their AI instead of “renting” it. Take for example Eli Lilly who have decided to treat AI like a proprietary scientific instrument instead of software to outsource. Instead of expensive long-term offtake agreements they just build their own data center (LillyPod). They make sure that all their IP, clinical trial histories, chemical structures, and other sensitive corporate data remain exclusively on their cloud. They can use whatever open weight model they want or build their own custom one and run infinite tokens with the only expense being electricity and the overhead of the GPUs and data center. Initially Nvidia doesn’t care if they do this because LillyPod is full of their GPUs but the neoclouds, hyperscalers, and OpenAI/Anthropic all lose because Eli Lilly owns LillyPod instead of renting cloud compute from them and purchasing tokens from OpenAI or Anthropic. The proliferation or free or pennies on the dollar “good enough” open weight/model/source AI is only going to continue and these models will only get better. Further, the lag time between when OpenAI or Anthropic publish a new “game changing” model and when those model weights have been distilled by open source models will likely continue to compress. The problem is that while initially Nvidia might not care if Eli Lilly owns their AI instead of rents it but it does hurt them because the ecosystem is highly correlated where if these B2B AI ownership trends become writ large then a neocloud bankruptcy, Oracle financial woes, or deceleration in revenue growth at Anthropic/OpenAI could have cascading implications. More on that highly correlated ecosystem in our concluding macro risk assessment in a minute.

3. Financial Engineering (Circular/Vendor Financing + JV Debt) (~10% of capex) - the entire circular financing structure depends on continued revenue growth and ideally growth that is accelerating from the foundational models like OpenAI and Anthropic. When they grow their annual recurring revenue they can buy more compute which keeps grease on all the fiscal gears. If the revenue growth decelerates - or worse the revenue itself declines - then they can’t purchase as much silicon and less JV, SPV, neocloud, and hyperscaler deals and data centers get done. The primary concerns here are margin compression and price/token primarily driven by less expensive AI options.

4. Sovereign Wealth Funds (~5% of capex) - this is probably the least of the financing concerns being that it is the smallest. I think any existing or aspiring world superpower will need to have significant spend on AI infrastructure to be taken seriously both economically and from a power/security standpoint. All sorts of potential military and socio-political and economic game theory scenarios will need to be played out and whomever has more powerful AI will have asymmetric advantages. However, this area is not without some tail risk. Let’s say that things get really bad and the AI infrastructure story falls apart in some significant way and both OpenAI and Anthropic are in serious financial danger and maybe even talk of bankruptcy. In such an event it is entirely plausible that a current or future administration would make an argument that these models are a key part of our tech, national security, and economic infrastructure and develop an expensive plan to salvage, save, prop up, invest, or maybe even nationalize those model makers. In that scenario there would have already been all sorts of pain on Wall Street that would have brought us to this expensive policy decision. However, in order to finance a tech bailout (that would probably also include bailouts to all the financiers as well) you would need to of course print more money and expand the national debt. Doing so in turn increases yields on treasury debt which in turn causes more money to flow out of stocks. At this point there is a good chance we are in a recession too. I am not saying this scenario is base case - it is not. This is a logically possible tail risk scenario.

Core Thesis
Alright, so I have deeply buried the lede on my core thesis:

Because the AI infrastructure ecosystem is incredibly highly correlated, any failures in execution could have very significant economic fallout and then be followed by cultural, policy, and geopolitical implications.

Here are a few systemic threats or execution failures that could dramatically damage the ecosystem:

1. Anthropic and/or OpenAI revenues underperform projections - In this scenario the entire ecosystem is deprived of water needed for everything in the entire system. Less GPUs. Less data centers. Less memory. Less power. Less revenue. Less profit/earnings. Stocks go down, cost of new debt goes up, and risks of defaults become elevated.

2. GPUs depreciate faster than expected - let’s say new generations of AI chips drive down cost/token faster than what had been previously modeled. In such a scenario older GPUs were supposed to have more “life” left in them than what was expected. Normally, you would say, “great, these new chips are amazing, fast, and efficient.” The problem is all those older Nvidia chips are now depreciating faster than expected but they were collateralized AND securitized to have certain lifetime revenue expectations that they can no longer meet. This could have some SPVs default on their debts and private capital might take a big haircut on all those asset backed securities. This likely hurts the neoclouds like CoreWeave, Nebius, IREN, and others and the might not be able to derive the revenue to meet the debt service coverage ratios. If I am in private capital, what do I do with GPUs that I just repossessed? So, maybe a neocloud or two goes belly up or has to dramatically restructure their debt. Or maybe Mark Cuban is right that some of the data centers become pickleball courts… (I don’t actually think this but it was humorous on Cuban’s part).

3. Private Capital Tightens Lending - What happens if lenders decide to only lend 50% loan-to-value (LTV) instead of 80% LTV? What happens if lenders increase rates from 6 or 7% to 10% on AI debt? What if they decide to only do 6 year amortizations instead of 10, 15, or 20 years? What happens if credit for contracted revenue goes from 100% down to 70%? All of these scenarios end up tightening liquidity and that will negatively impact every single player in the entire AI ecosystem.

4. Hyperscaler margins get squeezed - we have already seen capex explode for hyperscalers like Google, Amazon, Microsoft, Meta, and Oracle. They have almost all run out of FCF to finance their capex so now they are doing corporate bond issuance. The market has been patient so far with this trend because the Street si forward looking and is still largely believing that there are massive high margin profits to be found in cloud compute. But what if cracks begin to form in that story and in Wall Street sentiment? In that scenario if you are a hyperscaler scenario you have a tough decision to make. You can either A. Be a true believer that the profits will be there and the capex is justified and be willing to take the short-term hit on your stock price and pain inflicted by bad press and upset shareholders B. Cut capex and be rewarded OR maybe not penalized as much as your choice A peers. If we see hyperscalers begin to cut capex then there is likely to be a significant repricing of every company in the entire AI infrastructure trade. I don’t know if a single hyperscaler cutting capex would be enough of a catalyst to trigger a protracted unwinding and selloff but maybe two consecutive quarters of lower capex by a plurality of hyperscalers would be enough of a catalyst here.

5. Electricity and Land Issues - let’s say everything is humming along and just about everything in here is baseless hand wringing. Let’s say we have record compute backlogs and memory is sold out into 2028+. What happens if we cannot get a enough data centers built and enough electricity to those data centers? We have already seen increased friction even in red states to new data centers. Many states now want to see all kind of new guarantees before approving anything. There is an awful lot of growing NIMBYism as well. Further, there are a lot of parts of electrical infrastructure that are quite inelastic. There are specific transformer and other chokepoints that don’t have quick and easy solutions. Even more, it isn’t simple or quick to just build whole new power plants. This scenario is not the worst scenario for AI infrastructure because it means the demand and profit are there but the whole ecosystem might not grow as fast as expected because electrical and land issues are slowing down the buildout. There is an alternative scenario here where electrical and data centers are overbuilt. This doesn’t seem likely in the near-term future given the compute backlogs. But there is a tail risk scenario where token efficiencies grow much faster than expected and AI really solid models can run on your desktop, laptop, or even smartphone. In that scenario AI workloads could increasingly migrate from the cloud to on-device inference for lighter reasoning workloads. This scenario would hurt lenders, REITs, utilities, GPUs, neoclouds, and hyperscalers. This scenario would only be good for companies like Apple and Qualcomm.

6. Sovereign Wealth Issues and/or Nationalization Scenarios - a number of the scenarios listed above could lead to countries salvage, save, prop up, invest, or maybe even nationalize parts of the AI infrastructure. As previously noted, this would lead to more sovereign debt globally which begets rising interest rates which begets outflows from stocks into fixed interest vehicles. It is possible that there could also be geopolitical situations where nation states like Saudi Arabia might pull out of AI infrastructure deals that aren’t in Saudi Arabia. This would draw additional liquidity out of the system that if not replaced would also have a decelerationary effect.

7. Businesses Don’t See Enough Gains - many CEOs have noted that they have yet to see appreciable fiscal, operational, or efficiency gains from increased AI usage and expenditure. The implications here are obvious if they decrease spending - the whole ecosystem loses.

8. China Invades Taiwan - Taiwan Semiconductor supplies 90% of the worlds silicon. It has been rapidly diversifying its fabrications facilities outside of Taiwan. There is one in Austin and there are two more in Arizona that are slated to come online in late 2027 and 2030 respectively. There are other fabs elsewhere in Asia. However, over 90% of Taiwan Semiconductors output currently comes out of Taiwan (with over 95% of their AI relevant wafers coming from Taiwan) and total output is likely still over 80% occuring in Taiwan by 2030. Simply put, there is no AI without Taiwan Semiconductor. They are an even bigger chokepoint than memory and storage. A Chinese invasion of Taiwan would cause immediate shocks to every market worldwide. It would be biblical.

The scenarios I see as being the highest probable are 1 & 3. We will find out a lot more about scenario 1 when we see the SEC filings associated with Anthropic’s IPO scheduled tentatively for late September or October. The Anthropic IPO filing might be the single most scrutinized set of financials in human history because of how much money is riding on the success or failure of the AI infrastructure story. On scenario 3 we are starting to see debt rates rise on some of the new offerings. This is concerning because those deals are typically not publicized but the financial institutions are able to conduct fiscal due diligence that the public is not privy to. The implication is if rates are rising it is because risk is on the rise and the people who have the best look at the hard numbers as saying risk is rising.

A lot of the other scenarios are less plausible or even tail risk but I think they are all reasonable things that could play themselves out.

I worry that history rhymes with 1873, in that we have too much of our GDP riding on the success of one unproven industry.

I worry that history rhymes with 2000, in that we have this amazing new technological frontier but we haven’t yet sorted out who the winners and losers are in the tech stack.

I worry that history rhymes with 2008, in that the financing of a sector gets so complicated and convoluted that it obscures the actual underlying risk and the potential fiscal contagion.

The cone of uncertainty remains wide. AI infrastructure will be built in the US. There is no doubt about that. AI is probably the single most powerful and transformative technology to date in human history. My questions pertain to timeline, winners and losers, margins, funding vehicles, and ultimately total addressable market, revenue, and profit. The history of paradigm shifting technologies with significant up front infrastructure costs is shaky. There are many possible futures here and there are many reasonable scenarios that could usher real pain to the fragile ecosystem. If so, the impact will reverberate well outside of Wall Street with difficult to predict implications for many aspects of culture and society.

Aignosis is defined as the cognitive angst associated with having a high degree of certainty that something was AI-generated but having no definitive ability to prove that your intuition is correct.

Aignosis can lead to both mental frustration and loss of relational or institutional trust. Let’s run through a few scenarios:

Work Scenario
You receive something at work from a co-worker, subcontractor, or consultant. The work bears a lot of the hallmarks of a lot of AI-generation or at least significant AI-assistance. You were clear in your directives that you wanted human generated work. After all, you could have taken 30 seconds of time and made a good prompt to get a similar quality deliverable. In this scenario, you have now been placed in a state of aignosis where you have a high confidence in your intuition that this work has been phoned in. Now you are wrestling with the interpersonal and institutional implications of whether and/or how to address this matter with the party involved.

Knowledge Economy Scenario
You have paid a good sum of money to fly and attend a conference and you are excited to hear a favorite thinker/writer give a plenary session on a subject very relevant to you. You attend the plenary session but the first thing you see is the slides were likely made in 10 seconds in Claude from a fed manuscript. Now, subconsciously, your slop-scanning radar is up. In the first 15 minutes you hear three, “it’s not X, it’s Y” sentence constructions. Now your train of thought is completely lost. Slop-scanning has now passed over from subconscious to conscious. You are no longer able to follow the actual content of the talk and the entire reason you traveled to the conference. The talk finishes and you are left in a state of aignosis. You have a high degree of confidence that you wasted all that time and money to travel to hear someone speak and you feel disrespected that you could have simply gotten 60%+ of the plenary content from a well crafted prompt from your phone. You can’t prove it but your intuition is strong and you are left just sitting in the frustration and angst.

Here are a few developments in AI worth noting from this week:
Demis Hassabis changes roles at Google - Google Press Release

I will be teaching two sessions in this AI cohort here aimed at ministry leaders who want to get up to speed on AI developments

AI and the Image of God: What Does it Mean to be Human? [Silicon Spiritualities - Ep. 3]

The reason I knew why is because Claude has very particular fonts, font sizes, spacing, colors/hex codes, and other formatting giveaways. Claude also likes to do these mini headings in all caps and summary statements at the bottom.

Maybe you have had a similar experience reading something with a ton of em dashes or “it’s not X, but it’s Y” sentence configurations and it messes with your head. I know I am weird and probably an outlier here, and that’s okay, but I can’t be the only one annoyed by this cognitive tax.

The whole thing is frustrating enough that I feel the need to coin a new technical term to efficient describe this new cognitive tax and so I am officially coining the term “slop-scanning.”

Slop-scanning is defined as the ongoing cognitive tax of having to perpetually ask yourself, “is this AI-generated?” The only time you can turn it off is when you have the luxury of reading or interacting with pre-2023 content (three cheers for old books!) and you don’t have to be triggered every time you see an em dash, over-hyphenation (see what I did there), or sentence constructions with three parallel clauses.

Maybe you are saying, “Mike, get over it, who cares if someone used Claude for their slides?

You have a point and on the one hand, I don’t particularly care if the speaker’s slides are made in Claude. On the other hand, as someone who is drowning in AI-generated content, it feels special when you get to interact with something that is entirely analog.

I wouldn’t have minded this particular instance but Claude seemed to have added additional content that wasn’t in the speech. It was small and subtle, mainly in the tiny headings but it also added summary statements that weren’t in the speech.

If we go further down the rabbit hole there are much more problematic cases. I am seeing all sorts of article pitches, email body content, summaries, reports, and other content that are being passed off as if it came from the authors mind… when it clearly didn’t. In my view, we are either running afoul or just in straight violation of the 9th commandment here. There is also a sense where this kind of communication can be insulting — do you think I am too ignorant to notice this AI-content being passed off as yours? Am I not worthy of the time it takes to hear your actual thoughts and wisdom from all of your knowledge and experiences?

I really don’t know how to turn off the slop-scanning. I am not sure I should turn it off even if I could. The prevalence of people taking cognitive shortcuts without attribution is so ubiquitous that prudence demands I keep a permanent and RAM-intensive browser tab in my mind.

I have some hope that we can establish transparency norms around usage and attribution but in the meantime we will all be paying the slop-scanning cognitive tax.

Next week I will coin another technical term, “aignosis” to pinpoint another frustrating new phenomenon in the era of ubiquitous AI-generated content. Make sure you subscribe if you haven’t already.

Hopefully, this is obvious and goes without saying, I never use AI to write any of these posts.


Here are a few developments in AI worth noting from this week (and speaking of old books):
AI Companies Are Buying Tons of Old Books Because They’re Free of AI Slop (404 Media)

Citadel Buys Situational Awareness’s Stock Portfolio After Big Losses in AI (WSJ)

AI and the Image of God: What Does it Mean to be Human? [Silicon Spiritualities - Ep. 3 with Stephen Driscoll]

The AI era is similar to the industrial revolution and the inventions of electricity and the printing press. AI is similar to electricity because of how it can democratize and be a force multiplier wherever it is available. AI is similar to the printing press in that it rapidly increases the velocity of knowledge. AI is similar to the industrial revolution in that it will reorder the human workplace. Here’s the difference, these three inventions largely occurred in three different centuries (15, 18, and 19). The AI era is happening all at once and the new winners and losers in human capital will be swift. Broadly speaking, the invisible hand of the market will force a significant shift in human capital away from specialists towards generalists.

In the post-industrial and systematized farming era, specialists have been almost alone at the apex of the earning pyramid. The one obvious exceptions here are entrepreneurs and business owners — more on that in a minute. These specialists would be things like doctors, lawyers, accountants, engineers, programmers… etc. One has to ask themselves the question:

“How did those people become specialists?”

Those people became specialists because they acquired voluminous, costly, and lengthy education that was domain knowledge specific. Because of the depth, expense, and time involved that knowledge was scarce and therefore it was valuable. The problem is that the AI era has dramatically shortened the amount of time between not-knowing something and knowing something. The cost of acquiring that knowledge is effectively zero. The ability to receive nuance or depth in that knowledge domain is only limited by rudimentary prompt engineering or markdown skills. This leaves us with another important question:

“When knowledge has depreciated to near zero, what is valuable?”

In my last article, I said the answer is wisdom — and that remains true. However, we need to make this more concrete. Applied wisdom in the workplace means multi-domain expertise. The most valuable human capital in the future will be generalists who possess a strong working knowledge of 4-6 different domains instead of singular expertise of a single domain.

If I am correct here, then this has profound implications for the future of education and the reskilling of existing human capital. I would argue that the humanities, general education, logic, business administration, and systems thinking become more important than what they have been over the last decade or two. Those already in the workforce will need to be expanding their skills into broader domain knowledge. Even better will be those who can then apply systems thinking across multiple domains. This will be increasingly important in the agentic era of AI.

Here are a few developments in AI worth noting from this week:
OpenAI and Hugging Face partner to address security incident during model evaluation (OpenAI Press Release)

AI and Formation: How Does AI Shape Our Loves? [Silicon Spiritualities - Ep. 2 with Michael Keller]

If you know me at all you know I prefer an economy of words whenever possible. This piece will be one of those ruthlessly efficient ones.

Human (marketplace) value propositions have and are being dramatically repriced in real-time. I am not speaking of intrinsic human value here. I am speaking primarily to labor markets.

If your field deals with words, numbers, images, video, or systems/processes then your field is in the midst of some kind of paradigm shift. For many domain experts, their moat was based on having scarcity of knowledge. The problem is that in the LLM era that moat is shrinking and in many fields has shrunk rapidly.

The time between not-knowing and knowing continues to shrink. The internet sped up that time frame, then Google sped it up further, and now the LLMs have shrunk that time even more.

Is it always right? No.
Is it getting better? Yes.
Is it free of bias? No.
Does it have all the same weaknesses as humans? Yes.
Do we still need education and knowledge? Yes.
Do we need humans in the loop? Yes.

Here is the big shift:
Knowledge is a depreciating asset and wisdom is an appreciating asset.

The human (marketplace) value proposition moving forward has shifted from mere knowledge to wisdom.

What needs to stay the same in this corporation/organization/institution?
What needs to be modular and swappable in this corporation/organization/institution?
What new systems do we need in this corporation/organization/institution?
What new processes do we need in this corporation/organization/institution?
How does human capital need to shift in this corporation/organization/institution?

The wisdom literature has a lot to say about this subject of wisdom. The most common refrain is:
”The fear of the Lord is the beginning of wisdom.”

As we enter an entirely new era for humanity, we cannot neglect the fear of the Lord as our first and primary source of wisdom.


Next week we will look at what these dynamics mean for a shift from specialists to generalists.


Here are a few developments in AI worth noting:
AI models’ values are very different from most people’s (The Economist)
The Mom Who Runs a Household With a Staff of AI Agents (New York Magazine)
Palantir CEO Alex Karp on the future of AI workloads:

More AI stuff I am working on:
Silicon Spiritualities Podcast

Get Ready for the Age of AI: A Personal and Practical Guide to Our New World

As a sidebar, I have been working on some other AI-related projects at The Keller Center (where I work). Two in particular you might want to check out are:

A live interactive cohort from sharp theologians on navigating this new AI era called:
”Get Ready for the Age of AI: A Personal and Practical Guide to Our New World”

The Silicon Spiritualities Podcast Mini-series with the first episode that publishes on Monday, July 13 (Apple Podcast and Spotify):

Some of you will be like, that’s John Frame…

You would be right. If you are unfamiliar you can read his primer on triperpsectivalism here.

All that to say, there are three different kind of prompts in AI - thinking prompts, feeling prompts, and doing prompts.

• Thinking prompts are ones that help you cognitively connect dots, ideate, organize, prioritize, and things of that nature

• Feeling prompts are ones that deal with relationships, emotions, and wisdom

• Doing prompts are one that are task oriented

I don’t use AI ever for feeling prompts. I want to use prayer, the Bible, books, or human advice for anything involving wisdom, relationships, or emotions.

I use AI for thinking prompts when I am at the end of my cognitive rope and I need to push the frontier out a bit but I don’t utilize it until I have put in the intellecutal workout first. I think cognitive atrophy will be a real world dynamic in a few years, especially among power users. Also, I will have a forthcoming post on some observations on what agentic AI is doing to power users.

I use AI for doing prompts mainly in building spreadsheets and dashboards on stuff that is toil-centric.

Hopefully this little heuristic gives you some quick categories before you use AI to think a little bit meta about your prompt before you use the tool.


THINGS WORTH READING AT THE INTERSECTION OF RELIGION AND AI THIS WEEK:

1. “Policy on the AI Exponential“ - Dario Amodei

2. Peter Thiel moving to Argentina - New York Times

3. “Do Not Resign from Life” - L.M. Sacasas

4. “Designing AI for Human Flourishing: A Multidimensional Framework for Responsible AI Governance“ - Christos Makridis, Tyler VanderWeele, and Byron Johnson
   

• Toil is a work that is a direct result of the Fall.

• Labor is work that is a direct result of the creation/cultural mandate.

I like the usage of “toil” as a translation of the Hebrew word, Amal (עָמָל) used frequently in Ecclesiastes. It connotes some of the mundane, meaningless, burdensome, thorny, and broken aspects of work. AI is often pretty good at this type of work and requires less wisdom in using in these situations.

Labor is best understood as the type of work outlined in Genesis 1:26-28:
26 Then God said, “Let us make man in our image, after our likeness. And let them have dominion over the fish of the sea and over the birds of the heavens and over the livestock and over all the earth and over every creeping thing that creeps on the earth.” 27 So God created man in his own image, in the image of God he created him; male and female he created them. 28 And God blessed them. And God said to them, “Be fruitful and multiply and fill the earth and subdue it, and have dominion over the fish of the sea and over the birds of the heavens and over every living thing that moves on the earth.”

I am broadly inclined to use AI for toil and I am broadly reluctant to use generative AI for labor. The reasons why are in point 2.

NOTE - this distinction is my adaptation from things that Andy Crouch has written about. So, if you like this distinction, credit Andy for his brilliance. These are rough sketches here and not meant to be ultra fleshed out exegetically.

2. Knowledge vs. Wisdom
I will often use AI for knowledge but never use it for wisdom - let me explain.

AI can be quite good at knowledge based questions, especially when you apply good prompt engineering skills. It is especially good in the upper left quadrant here of things that tend to be analytical and high consensus like coding, data extraction, and financial auditing. It is not as strong on things in the bottom right quadrant that are more subjective and are lower consensus like poetry, aestethics, or ethics:

When I am using AI in a ministry setting for knowledge based questions you will need to add guardrails to your prompts like:

• “And make your answer consistent with The Gospel Coalition’s Foundation Documents and the New City Catechism”

I am fine and would encourage using AI for things that help you get back into human generated primary source content. So, I am good with prompts like this one:

• “I am preaching on Ecclesiastes 5 and want some exegetical words studies on Amal (עָמָל) and Hevel (הֶבֶל) from a reformed Protestant perspective, please suggest several commentaries or resources I should look at.”

The same principle applies here outside of ministry or church contexts. These tools can be incredible on the knowledge side of things, especially on matters in the green quadrant.

I don’t ever use AI for things that require wisdom. I will write at more length on this in future weeks, but right now this is merely a sketch. In my view, wisdom requires embodiment, experiences, and image bearing. We acquire wisdom from things like prayer, the reading of Scripture, the reading of books authored by humans, and conversations with other humans.

AI might be able to point me to some books, parts of Scripture, or people who possess wisdom but I am not looking to generative AI to give me wisdom directly. I am also not saying that generative AI cannot say things that accord with wisdom. What I am saying is that any wisdom that comes from generative AI is because it has approximated wise things from embodied or sacred sources and we are much better served by just going directly to those embodied or sacred sources and skip the middleman when it comes to wisdom.

It is easy for knowledge based work to slip into wisdom work. You have to be careful there.

3. Tool vs. Prosthetic
Generative AI is best used as a tool and less as a prosthetic.

AI as a tool - You are operating a machine to complete a task. You create the intent and verify the output.

AI as a prosthetic - AI is functioning like a part of your person and replacing some type of function.

I am inclined to broadly use AI as a tool and less inclined to use it as a prosthetic. The only use cases where I am using AI to do things that I would normally do myself are things that incline themselves to automation that are toil-centric oriented. This would be things that only require knowledge but not wisdom such as the creation of custom dashboards or niche CRMs.

NOTE - This distinction is from John Dyer at Dallas Theological Seminary.


Hopefully these three distinctions help your personal decision tree on when to use AI and when not to.

WHAT HAPPENED IN RELIGION AND AI THIS WEEK:

1. The White House signed a new executive order meant to balance innovation and security (NYT writeup here)

2. Yuval Levin has what is probably the best single response to the papal encyclical in the New Atlantic

3. NY Times piece on some of Silicon Valley’s reaction to the papal encyclical

4. New multi-faith consortium effort from Notre Dame, Baylor, BYU, and Yeshiva (CEFE-AI) launches an all religious faith benchmark to examine religious bias in LLMs (GitHub data repository, Axios write up)

5. Venture Capitalist Bill Gurley reflects on Anthropic and religion

6. Paolo Benanti reflects on the papal encyclical in WSJ

• The busy pastor, ministry leader, or educator who is trying to keep up on AI but doesn’t have time

• The person who works in tech who is trying to get a vignette into what religious people are thinking (especially Protestants/evangelicals)

• The person who is in the knowledge economy who is interested in the intersection of AI and religion

Alright, there was a lot to like about the papal encyclical on AI. Please bear in mind that I have only read it once and I would prefer to have had more time to think about it before responding.

WHAT WAS STRONG
The theological anthropology - see paragraphs 50-53

Emphasis on common grace in science and technology:

• “Scientific discoveries are talents entrusted to humanity so that they may bear fruit (cf. Mt 25:14-30). Technology has the power to heal, connect, educate and protect our common home...” (paragraph 9)

• “Technology should not be considered, in itself, as a force antagonistic to humanity. On the contrary, it has formed part of our history since the beginning as ‘a profoundly human reality, linked to the autonomy and freedom of man.’” (paragraph 4)

• “The Church regards all who sincerely seek ‘truth, goodness and beauty’ as companions on the journey, and considers them as ‘precious allies’ in defending the dignity of every person and in caring for creation.” (paragraph 23)
  

Christ’s restoration of true humanity:

• “By becoming man, the Son of God enters our history and takes on human flesh, bringing with him the love that unites him to the Father and the Holy Spirit. In him, ‘the mystery of humanity truly becomes clear’ because his humanity is completely free, open to others, capable of building healthy and beautiful relationships and committed to the total gift of self. Those who believe in him are engaged in the great work of renewal... and they cooperate in building up the Kingdom of God...” (paragraph 49)

Critiques of transhumanism - see paragraphs 12, 115-117, 120, 122, 131, 172, and 232

Emphasis on subsidiarity:

• “The principle of subsidiarity stems from the very same understanding of the human person that has guided our reflection on dignity and the common good. If every woman and man is called to take ownership of his or her own life and to contribute to the formation of society, then social institutions must also respect and support this responsibility. The Social Doctrine of the Church refers to subsidiarity as the principle according to which the role of individuals, families, local communities and intermediary organizations should not be supplanted by higher-level authorities. Moreover, higher-level institutions must recognize, protect and promote the freedom and creativity of lower-level entities, coordinating their contributions so that they can cooperate effectively for the common good.” (paragraph 68)

PLACES OF DISAGREEMENT

• I am Protestant so assume those places of disagreement on semi-pelagianism, atonement… etc. - I won’t relitigate those here

• While I am not opposed to the use of natural law, the encyclical is pretty heavy on natural law

• While I find value in interfaith dialogue, I also want to be careful to avoid syncretism and I would draw some the the lines around the church a little more sharply

• I am more reluctant to draw parallels between chattel slavery and problematic exploitative labor practices used in data scrubbing. I agree that it is a significant problem but I prefer to keep chattel slavery in an extremely high category of evil

• I found myself agreeing more on diagnoses than prescriptions. In particular there are problems with mutant or exploitative capitalism and Just War Theory has new dynamics to consider. However, I am more inclined to think optimistically about capitalism and Just War Theory than the encyclical.

Overall, I think the encyclical is worth reading and makes a lot of really good points. It is strongest on its diagnosis of the challenges of the next several years of shifts for humanity. It was a touch more hopeful than what I had anticipated and I appreciated the spirit of that. I think Protestants and Roman Catholics have a lot more agreement than disagreement, especially on diagnoses. While I am acutely aware that it is the nature of encyclicals to… well… pontificate… I am more concrete than I am abstract so I struggled most with it not being as concrete of a document.



AI DEVELOPMENTS AND ARTICLES
The biggest developments from Google’s I/O 2026 - The Verge

Google is killing search as you know it - TechCrunch

Work will always matter - Commonplace

Main Takeaways From Pope Leo’s Encyclical on A.I. - New York Times