AI is Not Ready for Prime Time<

Vanity | October 15, 2025 | CIB-173RDABN

[CIB-173RDABN]

AI is Not Ready for Prime Time: The Infrastructure and Human Reality Gap

Artificial Intelligence has captured the imagination of investors, companies, and the public alike, promising unprecedented efficiency, automation, and insight. Headlines trumpet AI’s transformative power, but a closer examination reveals a significant gap between hype and operational reality.

1. The AI Bubble: Investment vs. Value

AI has attracted billions of dollars in investment, reminiscent of the dot-com bubble of the late 1990s. Startups and established companies alike promote “AI-first” futures, claiming humans will be replaced, efficiency will soar, and profits will skyrocket. In practice, however:

• Most AI applications are effective only in standardized, repetitive tasks.
• In complex, real-world environments, AI frequently applies the wrong solution, creating errors that humans must correct.
• Software instability — such as version updates that break existing integrations — further erodes reliability.

The result is a paradox: companies expecting AI to replace humans find themselves more dependent on human oversight than ever, undermining the very efficiency they sought. For investors, this translates into minimal ROI relative to the billions poured into the sector.

2. Humans Remain Essential

The notion of fully automated operations has repeatedly failed. Examples from Tesla, IBM, Klarna, and Duolingo show that AI cannot replicate human adaptability, judgment, or problem-solving in non-standard scenarios. Humans act as shock absorbers, correcting errors, adapting to unforeseen circumstances, and maintaining quality — roles that AI cannot perform reliably.

3. Infrastructure as the Hidden Bottleneck

The most overlooked barrier to AI’s “prime time” readiness is energy infrastructure. AI’s massive computational needs require enormous electricity consumption. Current electrical grids, even in developed countries, are not capable of sustaining AI at projected scale.

The situation is analogous to the railroads in the 1800s: trains could not deliver their promised value until a reliable track network was built. Similarly, AI cannot function effectively without a robust electrical infrastructure to support massive data centers. Without such foundations, billions invested in AI risk being wasted, data centers may become stranded assets, and projects may fail — no matter how advanced the algorithms appear.

4. Perception vs. Reality

Media narratives and investor enthusiasm often exaggerate AI’s capabilities. Many believe that AI can solve any problem or replace humans entirely. In reality:

• AI is narrow, brittle, and context-limited.
• It excels at pattern recognition and repetitive processing but fails in tasks requiring intuition, judgment, and adaptation.
• Overconfidence in AI risks both corporate failure and societal disruption if humans overly rely on tools that cannot perform as promised.

Conclusion

AI is not ready for prime time. Until electrical grids are upgraded to support massive computational loads, and until developers integrate AI with human oversight and operational continuity, it will remain a tool with promise but limited practical impact.

Investment, hype, and public fascination cannot substitute for infrastructure and reality. Like the railroads of the 1800s, AI will only reach its potential when the foundational systems supporting it are in place — otherwise, billions of dollars risk being wasted on a technology that looks impressive but cannot function sustainably at scale.

[RoosterRedux]

BTW, Fink is right about AI being a matter of national security (i.e., "We need massive investment in AI").

I use AI daily and can say unequivocally, if we don't develop it to its maximum potential, China will...and then they will use it to destroy us.

Anyone who says AI is nothing but a hyped bubble doesn't use it.

Use it. Test it. Ask it questions. Push it.

This is not just the next economic revolution. This is the next step in human development.

We must understand it and use it to our advantage. And know full-well, others will use it to our disadvantage.

[grey_whiskers]

🦔 I've been digging into the AI data center economics, and a hedge fund manager just discovered something that confirms my worst fears about this bubble. Harris Kupperman initially thought data centers were financially questionable, but after talking to industry insiders, he…— Hedgie (@HedgieMarkets) October 13, 2025

https://www.zerohedge.com/markets/ai-now-debt-bubble-quietly-surpassing-all-banks-become-largest-sector-market"

https://www.zerohedge.com/markets/bubble-ai-echoes-past-lessons-present

A few links, general reading

[SunkenCiv]

Thanks RR.

[minnesota_bound]

I hope the AMD/OpenAI stock deal works out. It means they believe the stock will be worth $600 a share in a few years.

Today at $238+
UP from about $164+ from friday Oct 3rd to $235+ wednesday Oct 8t.

I use ChatGPT mini and Google AI mode to help with layout of webpages.

[RoosterRedux]

Concerns about a “debt bubble” in AI data center financing overlook how this sector actually operates. These projects follow disciplined project-finance principles, not speculative lending.

Debt is drawn in phases as construction milestones are met and is secured by the underlying assets, cash flows, and long-term leases—typically 10 to 15 years—from hyperscalers such as Microsoft and Google. These investment-grade leases provide stable revenue, giving lenders strong collateral, further reinforced by loan syndication during construction and asset-backed refinancing once facilities are operational.

Oracle’s roughly 4.5x debt-to-equity ratio, partly tied to its $60 billion OpenAI commitment, reflects this structure: its September 2025 $18 billion bond sale was oversubscribed several times, backed by lease commitments and data-center assets rather than unsecured projections. The projected $1.5 trillion financing gap through 2028 will likely be filled by private credit markets, which have about $800 billion in deployable capital for secured transactions offering attractive yields.

Hyperscaler capex is expected to reach $375 billion in 2025, largely self-funded from operating cash flows and contributing about 0.4 percentage points to U.S. GDP growth. The main risk is whether demand keeps pace, but current projections—nearly $7 trillion in global AI infrastructure spending by 2030—support the trajectory. This is not a debt bubble; it’s the rapid, capital-intensive build-out of essential digital infrastructure.

[Political Junkie Too]

When Fink says, "We're at the beginning of the tokenization of all assets," I think he is trying to sound visionary and profound. And he isn't a visionary or profound.

I don't know.

I used to be a data modeler in the 3G and 4G world of IT. Back then, we built hierarchical and relational databases to represent the real-world things our company was doing (i.e., customers, products, business assets, materials, orders, invoices, payables, receivables, etc.).

You can consider these to be rudimentary IT virtualizations of real-world things, that is, the things our business cared about. Of course, we were limited by the capabilities of the IBM 370 mainframe until personal computers, servers, and the client-server framework came out and put a mainframe on everyone's desk.

Object-oriented became a Thing, followed by stateless programming, the internet, and web-based computing. Everyone had a mainframe in their pockets.

Then came the cloud, virtual hardware, and the "internet of things," and everyone had a mainframe on their wrists, in their light bulbs, on their countertops playing music, and so on.

Soon enough, everyone will have a mainframe embedded in their arm or in their brain.

They already track our bank accounts, our credit card transactions, our utility bills, the TV shows we watch, the music we listen to, the things we buy on Amazon, where we drive, who we call, what we eat, etc. All that data is going to go somewhere.

It's going to be tokenized in a massive AI that will finally be capable of building a massive virtual world, true virtualizations of real-world things, only those real things will be us. And the power of AI will make connections between US that WE didn't even know existed.

Welcome to TRON.

All Hail the Master Control Program!

-PJ

[Bullish]

AI is typically GIGO. That has been my experience with it so far. It’s useful but also suspect. Be sure to double check the info you get from it if you have any doubts at all.

[RoosterRedux]

Good points. And I believe you are correct.

I was mostly grousing about Larry Fink (a person I thoroughly dislike). I shouldn't have conflated what he said with my grousing about him as a person.;-)

[SunkenCiv]

Having been 100 percent in cash (IOW, I needed the money for real life, was much younger, and couldn’t invest at the time) I missed the ‘87 crash. Just a few years, maybe months, earlier, the new NYSE head had convinced them to invest in computing power for the exchange sufficient to handle 2 billion shares. Skeptics pooh-poohed the expenditure. Turned out, they’d have been intercoursed if they hadn’t had the capacity, because it couldn’t quite handle the volume.

Now of course, 2 billion shares isn’t much higher than the daily basis. Not quite tip money, but, different times, different times.

A few years later, same employer, different work, there was a bit of a bad week, and we had people showing up wanting to cancel their 401K direct deposits. I couldn’t talk them out of it. The signup for starting it was only quarterly I think, and I tried to explain to them, you’re much better off keeping the contribution, because market prices for investments were cheaper now, and the investment was for the long term.

[ComputerGuy]

Conceptual thinking will be a challenge