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The AI investment bubble is real — I’ve been warning about it for months. But conflating that bubble with AI technology itself is a dangerous mistake that will leave you behind. Wall Street’s speculative mania is a financial phenomenon, not a measure of what this technology can actually do. Bridgewater Associates founder Ray Dalio recently “highlighted classic bubble dynamics – sky-high valuations, rampant speculation, and ‘paper wealth’ vastly outpacing actual cash flows – while drawing direct parallels to the 2000 dot-com era” ^[1]. Yet even he understands the crucial distinction: the technology endures when the speculation collapses.

AI capability is accelerating while market hype inflates valuations. When the correction comes — and it will — the technology stays and keeps improving. Dismissing AI because “bubble” is in the headline means ignoring the most powerful tool advancement of our lifetime.

What I Did in One Weekend

Last weekend, I used multiple AI models and agents to build projects that would have taken a team of engineers months and hundreds of thousands of dollars just 18 months ago. I also rendered a four-minute music video locally — a job that could have cost $400,000 a few years ago — for a few dollars of electricity.

This isn’t theoretical. I’ve spent years on AI technology, building AI models, processing data and vibe coding. And I’ve watched the global data center buildout with a growing sense of concern (and curiosity). The hyperscale buildout is where the bubble lives. But at the individual level, the technology itself is becoming astonishingly capable and practical.

The Real Risk Is Falling Behind

If you’re 30 days behind on AI, you’re already in panic catch-up mode. A year behind means obsolete. I’m not exaggerating. AI models “have surpassed basic predictive tasks, demonstrating complex cognitive abilities and the potential to replace up to 50% of desk jobs in the coming years” ^[2].

In the 1980s, you had 20 years to adopt personal computers and learn how to run them. But AI is compressing that curve to a few months. There is no time to wait. As financial historian Alasdair Nairn wrote about technology revolutions, “the combination of algorithm development and ever more specific processors to improve the efficiency of searches will allow much more rapid analysis of data patterns which hitherto were difficult to discern” ^[3]. And that was written before the current explosion.

I fear getting left behind myself — and I use this technology daily. Imagine how quickly non-users will be locked out of the economy if they don’t get some hands-on experience with AI.

Take Control: Run AI Locally

The best way to prepare is self-custody of AI: own your hardware, protect your privacy, and avoid the censorship built into cloud services. “Decentralized AI models could potentially shift power away from centralized entities and nation-states that can afford massive GPU arrays, such as those with 100,000 GPUs” ^[4]. That’s the direction we need, and it’s the only way to keep AI from becoming another tool of centralized control.

Start simple with free tools like LM Studio or AnythingLLM, then graduate to command-line harnesses like Kimi Code or Claude Code. I run my own GPUs for video rendering and write my own Python tools, but you don’t need to be a programmer to begin today. Every day you wait, the gap between those who control their own AI tools and those who rent their thinking from corporate gatekeepers grows wider.

Conclusion: The Acceleration Is Just Beginning

AI technology is leaping forward by remarkable gains in compressed time. The investment bubble doesn’t change that reality. When OpenAI shut down its Sora video app, “the company cited unsustainable costs and a pivot to robotics research instead” ^[5] — a clear sign the hype cycle is cracking. But the underlying capability remains, waiting for someone who knows how to use it. In the same period, “Oracle Corp. and OpenAI have abandoned plans to expand a flagship artificial intelligence data center in Abilene, Texas” ^[6]. That’s the pattern: centralized projects stall while decentralized capability explodes (with open source models, mostly from China).

Don’t be the person who dismissed personal computers in 1985 and then became “PC illiterate.” The cost of ignoring AI now is rapid obsolescence. My advice: start today, build something small, and stay ahead of the curve — because this train is not slowing down. Even if the AI speculation bubble collapses.

References

1. The Pricking Is Coming’: Dalio Warns AI Bubble Will Burst Like Dot-Com, But Tech Will Endure. – Zero Hedge. June 3, 2026.
2. 2025 11 20 BBN Interview with Aaron Day . – Mike Adams. November 20, 2025.
3. Engines That Move Markets (2nd Ed). – Alasdair Nairn.
4. Mike Adams interview with Aaron Day. – Mike Adams. December 16, 2024.
5. Sudden Shutdown of OpenAI’s Sora Video App Signals a Reckoning for AI Hype. – NaturalNews.com. Cassie B. March 26, 2026.
6. Oracle, OpenAI Scrap Texas Data Center Expansion Plan, AI Stocks Decline. – NaturalNews.com. Chase Codewell. March 11, 2026.
7. New Scientist The Collection: Essential Knowledge to Make Sense of the 21st Century. – New Scientist.
8. The Data Center Mystery: Why Billions of Simulated Worlds Are the Best Explanation of What’s Happening. – NaturalNews.com. Mike Adams. May 7, 2026.
9. Mike Adams interview with Zach Vorhies. – Mike Adams. January 3, 2024.
10. Mike Adams interview with Farsam. – Mike Adams. February 14, 2024.

Explainer Infographic

The resulting pathogens, known as bacteriophages – viruses that infect bacteria – cannot infect humans or animals, the report stated. Samuel King, a Stanford graduate student and study author, told the New York Times (NYT): “It just felt like the obvious next step.” Large language models, which underlie tools such as ChatGPT, can generate realistic text and pose risks of misuse in creating fake news or other misinformation ^[1].

Evo Model Trained on Genetic Sequences

Evo, a program similar to ChatGPT, scanned about nine trillion nucleotide bases from animals, plants, viruses and microbes, according to the research team. Rather than analyzing written text, the model was used to dissect and form genetic code, the report stated.

After learning natural DNA patterns, Evo generated nearly 300 genomes of Phi X-174, a virus chosen because it cannot infect humans or animals. Sixteen of those genomes were determined to be viable, the study reported.

In petri dish tests, some AI-designed phages multiplied faster than the original Phi X-174 and burst out of host cells, according to the authors. The researchers selected Phi X-174 because a virus genome is less complex than the DNA instructions found in a human cell, the report stated.

Researchers Call Study a Turning Point

Marc Güell of Pompeu Fabra University in Spain told the BBC the study was “a very significant turning point” and “allows us to dream of exciting possibilities for tackling humanity’s greatest challenges.” Güell said that “for the first time in history, we are beginning to design biology on a computer,” according to the BBC.

The scientists said they did not provide the model with data from viruses that infect humans, animals, plants or fungi, the article noted. The team also excluded similar viruses that infect other organisms, the report stated.

Biosafety and Biosecurity Questions Raised

But Dr. Thomas Inglesby and Dr. Moritz Hanke of the Johns Hopkins Center for Health Security wrote in a Science article that the results raised “urgent biosafety and biosecurity questions.” They said the issue is no longer “whether generative viral genome design will exist” but whether it can be used without “enabling serious harm.” Viruses that could cause disease “should not be pursued,” they added, according to the report.

Hanke told NYT: “You could say, ‘Hey, genomic language model, make me an influenza genome that is modified to be more transmissible or to be more lethal.'” A Microsoft study demonstrated that AI can be used to design novel, toxic biological agents by “paraphrasing” the genetic sequences of known toxins, allowing them to bypass commercial biosecurity screening software, according to a NaturalNews.com report ^[2].

The U.S. National Institutes of Health should improve how it regulates lab-generated viruses that could pose a national security risk, according to its biosecurity advisers, the National Science Advisory Board for Biosecurity ^[3]. In a previous case, a team at Boston University’s National Emerging Infectious Diseases Laboratories developed a new strain of the Wuhan coronavirus (COVID-19) that killed 80% of infected mice in a laboratory setting, according to a preprint study ^[4]. Declassified documents released in June 2026 showed U.S.-funded coronavirus research included planning for spike-protein modifications, receptor-adaptation experiments, and testing in humanized mice, according to ZeroHedge ^[5].

Implications of AI-Designed Pathogens

The study is among the first examples of generative AI being applied to biology, according to researchers, and the field remains at an early stage. Scientists emphasized that the resulting bacteriophages pose no threat to humans, but the methods could be adapted to other genomes, experts said.

Some analysts have said that only a few companies in the world have the resources to invest in developing large language models similar to GPT-4, a factor that could shape oversight ^[1]. The report raised policy questions about future oversight of AI-generated biological sequences, with the authors stating that dangerous viruses should not be pursued. A technology executive described one path as “to completely prioritize technology to maximize what’s possible without considering potential implications.” ^[1]

References

1. aacid zlib3 files 20241105T001425Z 29870425 3zFrZWC8qLJzJ5FoyS9nN5.
2. Ava Grace. “Digital Pandora’s Box: How AI Outsmarts Biosecurity and Paves the Path for Next-Generation Threats”. NaturalNews.com. October 09, 2025.
3. Emily Kopp. “‘Vague and Secretive’: Risky NIH Research Not Adequately Regulated, Experts Say”. Children’s Health Defense. January 21, 2024.
4. Michael Nevradakis. “Insane: Boston Researchers Create ‘More Lethal’ Strain of COVID, Prompting Calls to Shut Down Risky Gain-of-Function Research”. Children’s Health Defense. January 21, 2024.
5. “Gabbard Drops Fauci COVID-19 Receipts On Last Day: He Funded The Research, Cooked The Cover Story, Then Lied To Congress”. Zero Hedge. June 19, 2026.

Explainer Infographic