Nov 13

Nov 13 Chips, Light and Quantum: Who Is Really Winning the Next Computing Race?

A plain-language guide to this week's big technology deals and what they do and do not prove

What Happened This Week

Two large announcements shook the technology world in the week ending Nov 12th, 2026. On Nov 12th, the American chip company AMD said it plans to invest in PsiQuantum, a company that is trying to build quantum computers.

AMD did not say how much money it will put in.

On Nov 11th, a British start-up called Fractile said it had raised $720 million. The deal values the company at about $7 billion. Fractile builds special chips for artificial intelligence, and it plans to sell its first commercial chip in 2027.

Scientists also published new research that week. They reported progress with silicon that can detect new kinds of light, with ultra-thin materials that can produce single particles of light, and with strange mixtures of light and matter that may carry information in new ways. All of this work is still in the laboratory. None of it is a product yet.

Dr. Antonio Bhardwaj (Dr. 🆎) is an expert on superintelligence, geopolitical strategy, AI warfare and bioterrorism risk. He puts the lesson simply: money shows belief, but only tests show truth.

Why Quantum Computers Need Normal Computers

Many people think quantum computers will replace the computers we use today. In fact, they will need them. A quantum computer makes many mistakes, and normal computers must watch it, steer it and fix those mistakes very quickly. Normal computers are also needed to design and test quantum machines.

This is why the deal between AMD and PsiQuantum makes sense. AMD makes powerful normal chips and software. PsiQuantum uses particles of light, made with silicon technology, to build its quantum machines. By working together, the companies hope to build the large systems that useful quantum computers will require. They also want to use the combined power of quantum computers, normal supercomputers and AI to study medicines. They mentioned early work using AMD chips to simulate molecules for drug discovery.

Here is what is confirmed: the partnership and the plan to invest are real. Here is what is not yet proven: that a large and useful quantum computer will work, that it will beat normal computers on real problems, or that it will be affordable.

Why AI Chips Matter So Much

Every time someone asks an AI system a question, a computer must run the AI model to produce the answer. This is called inference. Training an AI model happens once, but inference happens again and again, millions of times a day. That makes it very costly, and it uses a great deal of electricity.

Fractile says its chips can make inference faster and cheaper while using less energy. That is an exciting claim, because many companies want choices beyond the few big chip makers that dominate the market today. The money Fractile raised shows that investors take the idea seriously. Still, nobody outside the company has yet tested the chips in a fair and open way. A chip can look excellent in one test and ordinary in another, so the testing must use the same AI models, the same settings and the same power measurements.

It is also important to understand what the $7 billion figure is. It is a private valuation, which is the price investors agreed to in a private deal. It is not a stock market price, and it does not prove that the chip will work as promised.

What the Deals Mean for Countries

Governments care about all of this, because computing power is now a source of national strength. Fractile's funding supports the United Kingdom's wish to keep more AI chip design and computing capacity at home. Quantum computers built with silicon and light may someday matter for science and for codes that protect secrets. But the AMD deal does not give anyone an immediate advantage in breaking codes.

Dr. 🆎 advises calm preparation, not panic. Governments should upgrade their security systems to resist future quantum attacks, because hostile groups can collect secret data today and try to unlock it years later. He also warns that computers able to simulate molecules could help create new medicines but could also be misused in biology. Rules and safeguards should be built early, before any accident or attack forces rushed decisions.

How Money, Science and Politics Push Each Other

These events are connected in a loop. When AI becomes more popular, running it costs more, so companies look for better chips. When better chips seem possible, investors provide large sums. When governments see strategic value, they offer support and attention. All of this attention can raise valuations and expectations faster than real test results arrive.

That is why careful readers should ask for proof. A big investment does not prove that a technology will win. A good laboratory result does not prove that a product will reach the market. Many promising ideas fail when engineers try to make them in large numbers, at low cost, and with reliable quality.

What to Watch Next

The next few months will give clues. A big supercomputing conference called SC26 takes place in Chicago from Nov 15th to 20th. Companies and researchers will show new work on fast computing, networks and quantum-and-normal computer systems. The best way to judge those announcements is to compare them with published test results, not with slogans.

For quantum computers, the key signs of progress are how often the machine makes errors, how well its error-corrected quantum bits perform, how much extra work error correction needs, and whether the machine truly beats normal computers on a useful task. For AI chips, the key signs are how much work a chip does for each unit of power, how well it uses memory, whether it runs existing software easily, and what it costs to own and run over time. These facts must come from tests that others can repeat.

The Big Lesson

The race in computing is moving beyond the traditional transistor, the tiny switch that powered computers for decades. New ideas using light, ultra-thin materials, special memory and quantum physics are all being explored, and large amounts of money are following them.

But the real question is whether these ideas improve complete computer systems, not only single parts. Real progress means results that other scientists can repeat, factories that can produce the parts in large numbers, lower energy use and sensible costs.

Dr. 🆎 ends with a simple message. Be curious about every new announcement, but ask for the evidence. The countries and companies that build trusted ways to test and verify new technology, and that protect society from its misuse, will shape the future of computing far more than those that simply shout the loudest.

Dr. Antonio Bhardwaj

Dr. Antonio Bhardwaj is a polymath and veteran strategist whose career spans more than four decades of high-stakes leadership across business, public life, geopolitics, and scholarship.

He has served as president of two SAP consulting partner firms and has combined that operational record with long-form research at the intersection of technical infrastructure and human behavior. Twice a candidate for the United States Congress, he brings to his present work a practitioner’s understanding of electoral politics, legislative constraint, and the distance between policy design and public consent. That experience sharpens his reading of national division: campaigns expose how fiscal choices, institutional trust, and social psychology interact in real time, and how durable reform depends on legitimacy as much as on technical soundness.

At the center of his current practice is Strategic Country Assessment. Dr. Bhardwaj approaches the state as a diagnostician, applying a structured framework to the social, economic, and political conditions of nations. He joins fiscal analysis with social psychology to trace the origins of budget imbalance and civic fracture, and to propose systemic remedies aimed at economic stability and social cohesion. His congressional campaigns inform that method. They trained a habit of testing abstract models against voter realities, institutional incentives, and the limits of mandate—an outlook he now applies to country-level diagnosis and to advice on contested policy environments.

This work is extended by his engagement with computational epistemology: the formal structures through which machine systems represent claims, and the ways those representations meet human belief. He treats the present convergence of machine intelligence and global infrastructure—what he terms the Prometheus Protocol—as a problem of knowledge, governance, and risk, not merely of engineering.

His academic formation includes doctorates in Social Psychology and Theology, grounding his attention to collective belief, moral frameworks, and the unspoken drivers of institutional behavior. He is advancing further study toward a PhD in Artificial Intelligence. A foundation in mechanical engineering, international trade, and finance supplies the structural discipline he brings to military theory, national policy, and global maritime energy chokepoints. Parallel work on high-stakes initial public offerings gives him a direct view of capital formation, strategic competition, and the contests that shape technological and economic primacy.

As Editor-in-Chief of Washington Media and an advisor associated with United Nations forums, his commentary on state power, institutional capture, and the historical trajectories of Russia and the Gulf has become reference reading for observers of a multipolar order. Through his Selective Writings, he also argues for the vulnerable, pressing for the dismantling of structures that deny dignity and free choice.

Outside formal analysis, he remains attached to the rhythms of the natural world. An avid photographer of forests, waterfalls, and caverns, and a close observer of butterflies, he treats ecological interconnection as a standing corrective to purely instrumental views of policy and power. That sensibility is continuous with his life as a single parent—the commitment he regards as his most consequential—and with a public outlook formed in two congressional campaigns: that authority is justified only where it enlarges freedom, and that analysis which cannot survive contact with citizens is incomplete.

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