When we look back at the history of AI... It will become clear. Everything
we've witnessed up until now has been mere prelude. So, forget the launch of
ChatGPT. Forget Nvidia's massive rally. And forget the supposed "AI bubble."



<[link removed]>



Сⅼіϲkhеrе and I'll reveal the shocking details. <[link removed]>



Editor’s Note: Louis Navellier has been a guest at Mar-a-Lago, President
Trump's private residence in Palm Beach Florida. He's also one of America's top
tech investors, managing a $1.1 billion portfolio - including $358 million in
AI stocks. (He recommended Nvidia to his followers before it went up 44,000%.)
In addition, he predicted the dot-com crash. He called Google's rise. And today
he's revealing what he calls the biggest prediction of his 40-year career.
<[link removed]>
According to Louis, Trump's new AI breakthrough borders on the "miraculous."

When we look back at the history of AI...

It will become clear.

Everything we've witnessed up until now has been mere prelude.

So, forget the launch of ChatGPT.

Forget Nvidia's massive rally.

And forget the supposed "AI bubble."

Looking back, we'll see the REAL revolution began...

With a private meeting that just took place at Trump's Mar-a-Lago...

A place I've been a guest at more than 10 times...

For reasons you're about to see...

I believe this meeting will lead to the first true AI "Miracle."

I'm talking about the launch of a new category of AI computer...
<[link removed]>

Being created now at a government lab in the mountains of Tennessee...

A device 1 TRILLION times more powerful than anything we've seen so far.

One project insider calls it "a scientific instrument for the ages."

And while you won't get fair warning from CNBC or The Wall Street Journal...

I can tell you right now, in advance...

America WILL flip the on switch on this device just days from now...

It'll leapfrog ChatGPT... Gemini and even Elon's Grok... instantly.

And it will trigger a $100 trillion reset of the AI markets this year.

I consider this the biggest prediction of my 40-year career.
<[link removed]>

Bigger than calling Nvidia before it went up 44,000%...

Bigger than calling Apple before it went up 36,000%...

Bigger than calling Microsoft before its 60,800% rise...

Bigger than predicting the 2008 crash (as noted by MarketWatch)... the
dot-com bust... and the 2020 Covid Rally.

This event could define my career - and your retirement.

Because it's poised to send certain AI stocks tanking...

While creating a new generation of AI millionaires and billionaires...

Starting with the company I reveal here (down to the ticker) in my new
presentation.
<[link removed]>

I encourage you to check it out now. At least jot down the ticker.

And fair warning: This video contains time-sensitive information.

I will be forced to take it offline very soon.

Regards,

Louis Navellier
Senior Quantitative Investment Analyst, InvestorPlace

P.S. This device is being build right now at a secretive laboratory in the
mountains of Tennessee. When Trump flips the "on" switch, it'll trigger a $100
trillion reset of the AI markets... and the biggest tech shock we've ever seen.
Go here for the details.
<[link removed]>

This ad is sent on behalf of InvestorPlace Media at 1125 N. Charles Street,
Baltimore, Maryland 21201. If you're not interested in this opportunity, please
click here
<[link removed]>
.


 

 

From 2015 to 2024, Every Doubling of Data Centre Capacity Was Followed by a
3.5% Fall in Average Retail Electricity Prices — About 6% at the State Level.
Data Centres Were Making Power Cheaper. What Changes That Is Not the Buildout
Itself but Whether the AI Demand Behind It Shows Up.


A working paper from the Electric Power Research Institute found that until at
least 2024, data centre operations did the opposite of what households feared.
Using Federal Energy Regulatory Commission data and EIA retail revenue figures
from 2015 to 2024, researchers identified a causal relationship running the
other way:for every doubling of data centre capacity, average retail
electricity prices fell 3.5%, and roughly 6% at the statewide level.


The mechanism is specific to how electricity is priced. Unlike soybeans or
gasoline, where the price reflects the cost of production,electricity prices
are based on cost recovery — how much is consumed. Fixed costs are divided
across kilowatt hours, so more kilowatt hours mean a larger denominator and
lower unit prices, an effect compounded as additional and more efficient
generators come online to serve the load. That arithmetic holds only while the
consumption arrives. EPRI researcher Asa Watten put the failure case directly:
if the grid builds capacity expecting substantial data centre demand and that
demand does not materialise, the fixed costs get spread across fewer people,
which is the opposite of the dynamic that produced the savings. The evidence
that the trend may already be turning is accumulating —PJM has attributed a
projected $6.3 billion increase in consumer electricity costs over three years
mostly to data centre demand, Virginia's residential prices have risen more
than 13% in the past year, and Goldman Sachs has projected the buildout adding
6% to electricity costs between 2026 and 2027 with a further 3% by 2028.
McKinsey puts total data centre construction spending on track for $7 trillion
by 2030.


For the investor, this reframes the AI capex debate in a way the usual bubble
framing misses. The question has generally been whether hyperscalers earn a
return on their infrastructure; the EPRI finding showsthe same uncertainty
determines whether households see lower bills or higher ones, through the
identical variable — realised demand against built capacity. Mark Cuban's
warning that many data centres will end up as pickleball courts rests on a
related argument, that AI will become cheaper to run through efficiency gains
and much of the capacity will prove unnecessary. What makes the electricity
channel worth watching is that it is measured continuously and publicly, in
utilisation and retail rates, rather than quarterly in earnings. If the
historical relationship inverts in the data, that is an early read on the
demand question —visible in a utility bill before it is visible in a balance
sheet.


 

Sources — Fortune, July 26, 2026 · Electric Power Research Institute working
paper, 2026 · McKinsey, 2026


 



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