Oliver L. Sims III

#TechExec #Broadcom #BoardMember #REInvestor #Father #Husband #Friend #DiversityChampion #Student #Advisor #Cybersecurity

[Oliver L. Sims III @OliverSims]

@DallasAptGP Congratulations. As a software industry veteran the best software is driven by vertical knowledge as in this case.

[Oliver L. Sims III @OliverSims]

@fortworthchris Always a no.....

[StockMarket.News @_Investinq]

Nicolai Tangen, CEO of Norges Bank Investment Management pressed IBM CEO Arvind Krishna directly on whether AI is a bubble (Save this).

And Krishna responded with what has become known inside financial circles as the $8 trillion math problem.

A single gigawatt of AI data center capacity filled with accelerators, liquid cooling, and power infrastructure costs roughly $60 to $80 billion to build and populate.

The industry has committed to more than 100 gigawatts of buildout globally.

That is $6 to $8 trillion in capital expenditure and because AI grade hardware depreciates on a five-year cycle, that entire sum must be effectively replaced and refreshed every five years.

To service the interest on $8 trillion in capital at a conservative 10% borrowing rate, the AI ecosystem would need to generate approximately $800 billion in annual profit, a number that currently exceeds the combined net income of every large technology company in the world.

Goldman Sachs estimates $7.6 trillion in aggregate AI CapEx between 2026 and 2031 alone, and Reuters Breakingviews has flagged that even if the capital is available, physical bottlenecks power permits, land, cooling infrastructure, and electrical grid connections mean that half of the planned data center projects are being cancelled or delayed before they ever go live.

Krishna also raised a second, structurally distinct concern that markets have largely ignored.

He argued that the largest foundation models, GPT, Gemini, Claude, Llama are converging toward commodity status.

When a product is a commodity, switching costs collapse.

When switching costs collapse, pricing power evaporates and margins compress regardless of how much capital was spent building the capability.

Morningstar's equity research team conducted a review of 132 technology companies in 2026 and found that AI had caused moat rating downgrades across roughly 40 major stocks concentrated in enterprise software, IT services, and SaaS with Adobe, Salesforce, Workday, and ADP among the companies whose competitive moats have materially weakened.

The implication is that the companies spending the most on AI model development may be building an asset that is simultaneously the most expensive to produce and the most difficult to monetize with durable margins.

This bear case is serious but it is also incomplete and that is what makes Krishna's framing so important to understand precisely.

When pressed further, Krishna explicitly said he does not believe there is an AI bubble in the technology itself only in a subset of the infrastructure capital that is being deployed against speculative assumptions rather than proven demand.

He draws the same analogy, the fiber optic overbuild of the late 1990s. Dozens of companies went bankrupt laying cable that nobody was using.

And yet that exact "wasted" infrastructure became the physical backbone of every cloud company, every streaming service, every mobile network, and every modern AI training cluster that followed.

The builders lost, the infrastructure won.

And the companies that were built on top of it, Amazon, Google, Netflix, Salesforce compounded for two decades.

The question, as Krishna framed it, is not whether AI is real.

It is which capital deployment earns a return versus which gets stranded and crucially, whether you own the stranded assets or the companies built on top of them.

On winners, Krishna was direct that distribution is the moat on the consumer side, and enterprise is wide open.

The data supports this, Meta with 3.3 billion daily active users across Facebook, Instagram, and WhatsApp is building AI into a distribution network that no startup can replicate at any cost.

Meanwhile, the productivity evidence arriving in real time is beginning to challenge the bear case's revenue projections.

Jensen Huang just showed on stage at Computex that GitHub commits, the universal measure of global software output nearly tripled in the first months of 2026, effectively converting $3 trillion in developer salaries into $9 trillion in productive output.

That is measurable, real time economic value already flowing through the system and it feeds directly back into token demand in a compounding loop that Krishna's static CapEx math does not fully capture.

[Oliver L. Sims III @OliverSims]

@dotkrueger So so true!

[Fred Krueger @dotkrueger]

California is not for everyone.

It's expensive.

The taxes are absurd.

But then you wake up in Santa Monica, drive up the PCH, ski in the mountains, have lunch outside in January, and watch the sunset over the Pacific.

You can't get that anywhere else.

[Amy Nixon @texasrunnerDFW]

There are currently more than 2000 homes available for sale in Celina, TX

Over half are new construction

Home prices in Celina are also down over 15% since 2022

If you ever needed proof that building more homes makes housing more affordable, Celina is it

[Oliver L. Sims III @OliverSims]

@FCNightingale BIG

[Oliver L. Sims III @OliverSims]

@ARHoopScoop Appreciate you coming, contributing and representing they way you have and will
Continue to.

[Oliver L. Sims III @OliverSims]

@RazorbackMBB @ThomasMeleek Go Leek , Go Hogs!

[Oliver L. Sims III @OliverSims]

@texasrunnerDFW Any idea why the change?

[Chris Powers @fortworthchris]

Today I sat down with @JohnMcQueeneyTX, State Representative for House District 97 in Tarrant County, Texas and member of the State Affairs Committee covering power grid and electric policy.

In the last six weeks alone, John's committee has run three interim data center hearings. He is also drafting the Data Center Responsibility Act for the January 2027 session - the bill that will set the framework for how Texas handles the data center build-out for a generation. Texas has 440 gigawatts of applications in the queue against roughly 110 gigawatts of current peak capacity. Someone has to sort out what's real, what's speculative, and who pays for the grid when it all comes online. John is one of the people doing that work.

I hope you enjoy this episode as much as I did.

Timestamps
(0:00) Intro
(02:27) Why Data Centers Are a "12 Out of 10" for Texas
(08:17) A Day Without a Data Center
(09:34) Inside Stargate: Lancium, Crusoe, Oracle & OpenAI
(14:34) When One Data Center Funds 30% of a City's Budget
(17:03) The Vicious Restudy Cycle & the Batch Zero Fix
(28:55) 440 GW of Applications Chasing 105 GW of Capacity
(35:53) The 75 MW Threshold & Going Behind the Meter
(48:36) Drafting the Data Center Responsibility Act
(54:19) North Texas's Hidden Risk in Batch Zero
(01:08:35) Who Actually Pays for the Grid Buildout?
(01:14:32) Data Centers Are a National Security Issue
(01:18:12) Data Centers in Space & the Long Arc

[MAGA Cult Slayer🦅🇺🇸 @MAGACult2]

“I have never cared about what a president has done before. This will set us back 50 years!”

[Oliver L. Sims III @OliverSims]

@BobKnakal Well said Bob!

[Oliver L. Sims III @OliverSims]

@texasrunnerDFW Makes perfect sense!! Thank You!

[Oliver L. Sims III @OliverSims]

@ClintStoerner @Coach_HNutt @RazorbackFB Agreed

[Moses Kagan @moseskagan]

What every voter and apparently, the NY Times Editorial Board, should know about housing policy:

1. Rents reflect the balance of supply of apartments and demand for those apartments in a given area. That’s it; there’s no magic. If you want lower rents, you can hope for a recession that destroys jobs and, therefore, demand. Or you can add supply.

2. There is no amount of money that any big city government could feasibly spend that would add materially to supply. This is because, depending on the location, new apartments cost $250,000-1,000,000 to develop… building even a few hundred of those starts to stress any city budget, and many big cities need tens or hundreds of thousands.

3. On the other hand, investors (including pension funds and endowments, insurance companies, rich families, etc.) can collectively **easily** provide enough capital to build as much housing as we need **so long as they are confident they can get a reasonable return**.

To get those investors to fund the creation of the housing our society needs, we must do two things:

1. Dramatically reduce the time & complexity associated with securing governmental permission to develop housing. This means reviewing and simplifying the overlapping regulations that constrain housing production: zoning codes, building codes, parking, ADA, etc. But it also means changing the cultures within the relevant governmental agencies from “default no” to “how can we help you?”.

2. Provide certainty around on-going regulation of apartment operations.

The way investors get a return from building rentals is as follows: They hire managers to lease the apartments, collect the rents, pay operating expenses and any mortgage payments, and then send the investors the cashflow that remains.

But governments all over the country have been restricting the manner in which apartment buildings can be operated in all kinds of ways.

For example: Cities have been making it harder to screen tenants, while also making it much harder to evict tenants who don’t pay. You can see why both of those measures are politically popular. After all, who doesn’t want people to get second chances? And who wants anyone to get evicted? But, as a manager, the combination of those two regulations makes it much harder to predict, with any certainty, that the rent will get paid… and that makes it very difficult to get investors to provide capital to create more housing.

Another example: Rent control. Again, I understand why renters love rent control and why politicians want to give it to them. But, if, as has been the case in NY, LA and San Francisco, city governments hold annual rent increases below the rate of growth in the operating expenses of the buildings, the cashflow payable to the investors shrinks… making them much less likely to invest capital in building more apartments.

In conclusion: For ~every other good or service in the economy, we allow the market to function, and the result is that we have a surplus of choice at all price points (think of food or clothes or cars), which is spectacular for the consumer. If we want a surplus of choice at all price points in housing, we need to get comfortable with the idea of allowing the market to provide it.

And that means allowing investors to build rental apartments *and* allowing them to operate those apartments in a manner consistent with making a reasonable profit.

Remember: Every developer of rentals is either a landlord-in-waiting or hoping to sell to one.

[Oliver L. Sims III @OliverSims]

@fortworthchris @JohnMcQueeneyTX What strategies is the state deploying that would prioritize the continuity of consumer power over Data Center power.

[Chris Powers @fortworthchris]

Texas’ current grid capacity is ~85 GW.

Current interconnection requests statewide? Over 400 GW.

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