The discussion remains controversial depending on the location.  Generally, data centers located in rural isolation do not seem to be too controversial. However, data centers built in proximity to population centers stimulate a great deal of opposition.

Recently New York state banned the building of Data Centers as politicization of the construct has become somewhat of a Right/Left divide.  President Trump notes the importance of data centers:

(VIA TRUTH SOCIAL) – One of the biggest Driving Forces in the Future for Jobs, are Data Centers. They are big, strong, bold, and Money Machines for the State in which they are built. Governor Kathy Hochul, for political reasons, has terminated all Data Centers being built, or to be built, in New York State. These Companies are now being sought in Alabama, Florida, Texas, Arizona, and many other States. Both the Taxes and the Jobs amount to LIQUID GOLD! New York State has made a terrible decision.

All of this Income, and other Benefits, will be going to Red States, and some Blue, where Data Centers are sought as Cash Cows, with Lower Taxes and Record Setting Jobs. They must pay for their own Water and Power, and any leftover goes back to the State and local Community. Data Centers are tremendous WINS for the States and Communities that are lucky enough to get them. New York should change its Policy, IMMEDIATELY. The Radical Left Dumocrats must not be allowed to cause us to lose Data Centers, AI, and all of this incredible new Technology, to China, and other countries!”

~President DONALD J. TRUMP

The hardware side of the Data Center expansion is leading to increased revenue for raw material providers, chip makers, computer systems, fabrication shops, electrical components and construction.

At the same time, the software side LLM builders and AI companies are trying to figure out how to make stable profitability within the sector.

CTH watches the tokenization and subscription fees for various AI model use with the same perspective CTH viewed over a decade of false claims within the financial market that told lies about social media viability and data processing costs.

Now, we watch the seemingly exponential growth of AI capabilities and associated costs with the same pragmatic perspective.

Robotic pool cleaners were introduced two generations ago.  Did the pool cleaner business dry up? No, it expanded.

Robotic vacuums broke into the popular household appliance market five years ago, you probably have one, did it eliminate maid services?  No, still growing.

AI can now write its own code to generate outputs. Are software developers getting fired?  No, demand for software designers and engineers is up.

The mainframe approach, the one AI brain to run all systems, will never work – it is cost prohibitive (see first paragraph – wash, rinse, repeat).  Deny this reality at your own investment risk. If needed, politely absorb the ridicule – for it matters not.

CTH predicts AI will become a localized and optimized sub-set for each sector of the economy, requiring each major organization and corporation to adopt specific cost/benefit data libraries and networks for use and functionality.

At scale, a thousand coders each working on Gemini, ChatGPT, Anthropic, Fabian, Grok, etc. will become 100,000+ software designers working inside individual companies to create personalized, targeted, bespoke AI data systems and networks; each system specifically tailored to the industry or sector of business.  The intranet of internets will happen again.

Creating and selling AI system networks and integration functions that are personally tailored to highly specific company functions, creates an entirely new sector of the technology industry that has not even begun yet. [There’s an investment opportunity there]

Will AI robots replace some repetitive human functions?  Yes, the ice rink Zamboni will likely not have a steering wheel, just an emergency joystick. A reference for a comparative industrial scale Roomba vacuum, or the robotic pool cleaners.

However, at scale the robotic industry is slower than human efficiency in almost all sectors that matter; the cost benefit analysis will limit growth.  The maid service sector will not be impacted any more than the software developers.

It is not an issue to fear some AI task efficiencies will grant more time available that will be filled with alternate task capabilities.  Human productivity will increase in certain sectors of the economy, but humans will not lose work opportunities.  Blue collar jobs will continue to expand as each of the hardware tools developed will need manufacturing, installation, maintenance and monitoring.

The further downstream the worker is from a repetitive function within the [XXXX] industry, the more irreplaceable they become.

As to the bigger picture of fully developed AI and the intersection of information and knowledge; yes, the automation of AI can present an issue.  However, all AI concerns can be mitigated so long as multiple, alternative AI systems exist within the larger information realm.

As a nation we need dozens of different AI models each competing within the industry for the best AI product.

As long as we have multiple AI systems, alternatives to the hive-mind, we do not need to fear the AI network as a source of information.  If we don’t like the AI outputs, we can switch to an alternate AI provider.

If the subscription cost of the AI is too high, then as long as we have a competitive market where a lesser expensive, perhaps bespoke AI option can exist, we should be okay.  Let the free-and-fair market decide.

If AI outputs don’t offer empirical truth or real value to the end user, we should be fine as long as consumers have alternative options available.  AI providers should be information providers in the same concept as cell phone providers.  The key is to have multiple, competing AI systems available for industrial, business, professional and personal use.

On the upside of this information worry dynamic -in the pragmatic and optimistic perspective- we have the cost limiting nature of a massive singular AI information network.

A single AI central brain handling over 360 million users at once, all requiring identical responses that update with every tiny change in a multi-trillion datapoint-per-millisecond data stream, is far beyond the capacity of any computational AI system. The costs tied to such a setup are only now becoming clear, and AI business models are starting to fall apart in real time. This is a hard truth that isn’t going to change.

Within the AI business, those who can carefully write AI input instructions to achieve maximum value in AI output -industry by industry- will become increasingly more valuable.

Those who can train AI to be cost effective -and provide materially beneficial outputs- within their granular sector of business, within each company, will become priceless to the organization.  Wage rates will follow competency.

The one key issue about AI to emphasize is the need for multiple competing models.  If China (hive mind) has their model, and Europe (another hive mind) has their model, and the United States (entrepreneurial competitiveness) has multiple competitive models – we will win the AI race and simultaneously we will retain freedom.

What we don’t want is a singular AI model to win the support of the United States government and then end up with an AI regulatory system where the govt starts defining terms of “safety” to eliminate information adverse to the interests of the government that regulates it.  Both China and Europe will predictably do that.