Nine years ago I first asked whether the battle for quantum supremacy had already been lost. Last summer I updated that analysis: we had entered the quantum threat window, and the only real question was how far in we were.

A year later, I have an answer of sorts — not from a lab announcement or a leaked intelligence assessment, but from watching what the U.S. government just did.

tl;dr — America is finally competing in quantum by becoming more like China. And if you want to know how close Q-day really is, don’t listen to what governments say. Watch how fast they move their own deadlines.

New Thought #1: America Is Finally Competing — By Becoming China

In 2017, my core complaint was arithmetic: China was outspending us 50-to-1, and the National Quantum Initiative was a rounding error against Beijing’s commitment. For nine years, the American answer to that asymmetry was to write somewhat bigger checks and hope the private sector would carry the rest.

That era just ended.

On June 22, 2026, President Trump signed two executive orders — with the presidents of Google and IBM standing in the room. The first, Ushering in the Next Frontier of Quantum Innovation, launches QC-ADDS: a national effort to build a scientifically useful quantum computer and deliver it to a Department of Energy facility, with the White House signaling 2028. National workforce institutes. Federally coordinated sensor and network programs. The second order pulls the government’s post-quantum cryptography deadlines dramatically forward (more on that below).

And a month before the ink dried, Commerce put roughly $2 billion of CHIPS Act money into nine quantum companies — taking equity stakes in exchange.

Read that list again: state equity positions in strategic technology firms. A government-directed national computer build at a national lab. Mandated private-sector-shaping deadlines. Workforce planning by decree.

That is not the American model I was defending in 2017. That is military-civil fusion with American characteristics.

So here’s the uncomfortable question for this year’s update: is this a belated vindication or a quiet concession? On one hand, everything I argued for in 2017 — treat quantum as a national strategic program, not a research grant portfolio — is now official policy. On the other, the fact that we had to abandon our own model to compete suggests that in the system competition, Beijing already won a round. We didn’t beat their playbook. We photocopied it.

One more wrinkle: my original “Quantum Coventry” argument — that the most important capabilities are the ones being deliberately kept hidden — was always pointed at Beijing. With a state-directed program of this scale now running through DOE and the IC, that logic cuts both ways for the first time. Neither side’s public scoreboard should be trusted. Which, depending on your priors, is either terrifying or the first genuinely good news in this series.

The Technical Scoreboard: A Year of Compression

The last twelve months weren’t about one splashy chip. They were about compression — of error rates, of overhead ratios, and above all of resource estimates.

• Error correction went from milestone to routine. Google’s Willow demonstrated below-threshold error correction, then posted a benchmark result 13,000x faster than the best classical supercomputer in October 2025. Quantinuum demonstrated a 2:1 physical-to-logical qubit ratio — against the hundreds-to-one everyone assumed a few years ago.

• The referees showed up. DARPA’s Quantum Benchmarking Initiative advanced 11 companies into Stage B of its effort to verify — independently, adversarially — whether anyone can hit utility-scale by 2033. In a field drowning in press releases, an institution whose job is separating hype from reality is arguably the most important development of the year.

• China kept pace, and structured for the decade. Zuchongzhi 3.x is competitive with Willow, and the 15th Five-Year Plan names quantum first among seven strategic “future industries” — with fault-tolerant general-purpose machines as an explicit national target through 2030. Meanwhile, the evidence keeps mounting that U.S. export controls are accelerating China’s self-sufficiency rather than slowing it — we are building their domestic supply chain for them, one restriction at a time.

• The number that matters most collapsed. In 2019, breaking RSA-2048 was estimated to require ~20 million physical qubits. By 2025, under one million. In early 2026, a series of papers pushed plausible estimates toward 100,000 qubits under newer architectures. That’s a 200x reduction in seven years — from public, academic work alone. Ask yourself what the classified curve looks like.

New Thought #2: Deadlines Are Intelligence Signals

Last year I wrote that if RSA or ECDSA breaks at scale, we won’t be told. The first exploitations will be covert, and the public will learn about it years later, if ever. I stand by that. But this year taught me there is a signal we can read — and it’s hiding in plain sight.

Watch the deadlines.

In 2024–2025, the NSA’s guidance pointed to 2035 for the government-wide post-quantum transition. In June 2026, the second executive order ordered agencies to move high-impact systems and high-value assets to post-quantum key establishment by December 31, 2030, and post-quantum digital signatures by December 31, 2031.

Governments do not voluntarily compress a decade-long, brutally expensive migration by five years for fun. Bureaucracies move deadlines out, not in. When the most heavily briefed institution on Earth suddenly decides its most sensitive systems need quantum-resistant encryption five years earlier than previously stated, that is revealed preference. It is the closest thing to a declassified threat assessment we are going to get.

This gives us something the 2025 piece lacked: a testable, trackable proxy. Forget the qubit-count press releases. Track deadline compression. Every time a major government or standards body moves a PQC date forward, that’s the classified consensus leaking into public policy. Three data points now line up — the resource-estimate collapse, the deadline compression, and the sheer velocity of state investment — and they all point the same direction: we are deeper into the window than the public technical literature implies.

New Thought #3: It’s No Longer One Race. It’s Two Clocks.

Here’s the reframe I’d offer anyone still asking “who’s winning the quantum race” — the question itself is stale. The unit of account has changed.

For nine years, this series has scored the race the way everyone does: qubits vs. qubits, dollars vs. dollars, China vs. America. But the contest that will actually determine winners and losers is between two clocks:

Clock one: decryption capability. Accelerating. 20 million qubits to maybe 100,000 in seven years. Below-threshold error correction achieved. State-scale money flooding in on both sides of the Pacific.

Clock two: cryptographic migration. Grinding. History says major cryptographic transitions take 5–10 years under good conditions — and the global installed base of RSA and ECDSA is the largest cryptographic footprint ever deployed. Most enterprises haven’t even completed the inventory step.

Whoever wins the race between those clocks matters more than who builds the first cryptographically relevant machine. A world where the U.S. builds the first CRQC but hasn’t migrated its financial system is still a catastrophe. A world where migration outruns capability is survivable regardless of whose flag is on the computer.

And nothing illustrates the two-clock problem better than crypto. A Federal Reserve working paper flagged what makes Bitcoin uniquely exposed to harvest-now-decrypt-later: the entire ledger is public, permanent, and cryptographically frozen. There is no quiet re-encryption of history. Google’s own researchers estimate the elliptic curve cryptography underpinning Bitcoin and Ethereum falls to fewer than 500,000 physical qubits, in minutes. Bitcoin cannot move a deadline forward by executive order. It has to achieve consensus among millions of anonymous stakeholders to migrate — the slowest possible version of clock two, racing the fastest version of clock one. If you want to know which asset class is most structurally exposed to Q-day, it’s the one that advertises its immutability.

What To Actually Do

The federal 2030/2031 deadlines are now the de facto private-sector benchmark. If you run a bank, an exchange, a healthcare system, or critical infrastructure, assume regulators and counterparties will hold you to the government’s clock, not NIST’s original one. That means: complete your cryptographic inventory this year, demand PQC roadmaps from every vendor now, and treat any data with a shelf life beyond 2030 as already harvested.

Conclusion: The Question Has Changed

In 2017 I asked whether the battle for quantum supremacy had already been lost. In 2025 I argued we were inside the threat window and wouldn’t be told when it closed.

In 2026, the question has changed again. It’s no longer whether America can outspend China, or even who builds the machine first. It’s whether the defensive clock can beat the offensive one — and whether we’re willing to admit that the U.S. only got serious about winning by adopting the very model it spent a decade warning against.

The battle for quantum supremacy hasn’t been lost. But it’s no longer the battle that matters. The migration is. And on that clock, almost everyone is behind.

Coda: A Word for the GPU Maximalists

One last thought, aimed at the datacenter maximalists currently underwriting the largest infrastructure buildout in human history. The hyperscalers will spend north of $600 billion on capex in 2026 alone — roughly $450 billion of it AI infrastructure, much of it financed with debt against assets with 3–5 year useful lives for the silicon and multi-decade lives for the shells and power.

That buildout is now facing a squeeze from two directions at once.

From below: as I argued in April in You Don’t Need the GPUs They’re Selling You, memory bandwidth — not compute — is the real bottleneck in AI inference, and the open-source efficiency stack (llama.cpp, vLLM, MLX, and friends) has been quietly proving that much of the “required” GPU footprint was compensating for inefficient software. Since then, the market has started confirming the thesis: Apple’s WWDC announcements put a rebuilt, foundation-model-powered Siri and a new Core AI framework for running developers’ own models on device — private, local, zero server dependencies, zero token costs — at the center of its platform strategy. When the world’s largest consumer hardware company bets its assistant on unified-memory silicon in your pocket instead of a GPU in someone’s datacenter, that is a proof point that inference demand — the supposedly infinite tailwind justifying the capex — leaks out of the datacenter at the margin.

From above: overlay the timelines from this piece. QC-ADDS targets a scientifically useful machine at DOE by ~2028. DARPA’s QBI is stress-testing utility-scale — computational value exceeding cost — by 2033. Both dates sit inside the depreciation and financing horizon of datacenters breaking ground today. Quantum computers will not train LLMs, and GPUs are not going away. But a meaningful slice of the workloads justifying this capex — simulation, optimization, materials, chemistry, drug discovery, risk modeling — is exactly the slice quantum eats first. If even 10–15% of projected HPC-class demand migrates to quantum-cloud offerings in the early 2030s, the terminal-value assumptions underneath a trillion dollars of GPU-centric buildout get interesting, fast.

Efficiency eats the inference floor; quantum eats the HPC ceiling. What’s left in the middle is training and high-volume serving — real, but a much narrower base than the one currently being financed. And note who’s hedged: Google, IBM, Microsoft, and Amazon all run serious quantum programs, and Apple just showed everyone the on-device playbook. The hyperscalers are insured against their own buildout. The REITs, the neoclouds, the debt holders, and the utilities signing 20-year power agreements are not.

The lesson of this entire series is that the market consistently prices technology transitions as later than the people closest to them behave like they are. Watch the deadlines — and maybe re-read your depreciation schedules.

— Sultan

tl;dr offensive quantum systems that break bitcoin might already been out there.

The Quantum Arms Race: No Longer Theoretical

Back then, the debate was whether quantum computers could ever break encryption or deliver practical advantage. Today, the question is how soon—and who gets there first. China’s massive $138 billion government-backed quantum fund, announced in 2025, is just the latest escalation in a global investment surge. U.S. quantum funding has exploded in response, with over $1.25 billion invested in Q1 2025 alone. The market is projected to hit $5.3 billion by 2029, but the real story is in the speed and scale of technical breakthroughs.

Technical Milestones: From Hype to Reality

• Google’s Willow chip smashed previous records, using error correction to make quantum computing more reliable and completing calculations in minutes that would take classical supercomputers eons.

• Harvard’s logical quantum processor encoded 48 logical qubits, a leap toward practical, fault-tolerant quantum machines.

• IBM’s roadmap: 4,000 qubits by 2025, with fault-tolerant systems running 100 million gates by 2029.

Quantum attacks on encryption are no longer science fiction. Chinese researchers have already demonstrated quantum attacks on 50-bit RSA keys. While this is far from breaking modern encryption, it’s a proof-of-concept that the “harvest now, decrypt later” threat is real—and growing.

The Quantum Threat Window: The Encryption Ticking Clock

Here’s the uncomfortable reality: we have officially entered the window where quantum technologies—both those publicly acknowledged and those still classified—are in a position to break widely used encryption standards like RSA and ECDSA (the cryptographic backbone of Bitcoin and much of the world’s secure communications).

• Quantum computers have reached a threshold where, in theory, they can factor large integers and compute discrete logarithms—core operations underpinning RSA and ECDSA.

• Recent breakthroughs in quantum error correction and scaling have accelerated timelines far beyond what most experts predicted even a few years ago. The demonstration of quantum attacks on smaller-scale RSA keys is no longer academic; it’s a warning shot.

• “Harvest now, decrypt later” is no longer a hypothetical threat. State actors and sophisticated cybercriminals are actively collecting encrypted data with the expectation that it will be decrypted once quantum capabilities are sufficient.

Known vs. Unknown Capabilities

• Public quantum milestones—such as Google’s Willow chip and China’s quantum communication satellites—are only part of the story. The most advanced quantum technologies are likely classified, particularly those developed for military and intelligence use.

• It’s plausible that nation-states already possess quantum systems capable of breaking cryptographic standards, but are keeping these capabilities secret for strategic advantage.

How Far Into the Window Are We?

• No one can say with certainty how deep we are into this quantum threat window. The only consensus is that we are in it now.

• Indicators that the window is open:

  • Successful quantum attacks on smaller encryption keys have been demonstrated in academic and government labs.

  • Major governments have set aggressive deadlines (e.g., NSA’s 2035 mandate) for transitioning to quantum-resistant cryptography, signaling that they believe the threat is imminent.

  • The pace of investment and secrecy around quantum programs has sharply increased, especially in China and the U.S.

Will We Know If It Happens?

If a quantum attack breaks RSA or ECDSA at scale, we may not know immediately. The first successful exploitations are likely to be covert, targeting high-value intelligence or financial systems. The public may only learn of these breaches long after the fact, if at all. The prudent move is to assume the window is open and act accordingly: accelerate the transition to quantum-resistant cryptography, monitor for signs of compromise, and prepare for a world where cryptographic trust can no longer be taken for granted.

Geopolitics: The Quantum Cold War

China’s Quantum Ambitions

China isn’t just competing—it’s integrating quantum into every lever of state power:

• Military-Civil Fusion: Quantum research is embedded in China’s military modernization. The PLA is developing quantum sensors for submarine detection, quantum navigation for GPS-denied environments, and quantum-secure communications.

• Quantum Communications Leadership: China’s national QKD network and quantum satellites make it the world leader in secure communications, with over 2,000 kilometers of quantum-encrypted fiber and the world’s first quantum satellite.

• Indigenous Innovation: Facing Western export controls, China is building a self-reliant quantum ecosystem, from hardware to software, reducing dependency on foreign suppliers.

U.S. Risks: The Quantum Threat Matrix

• Encryption at Risk: Quantum computers threaten to render today’s encryption obsolete. The U.S. faces the risk that adversaries are already stockpiling encrypted data, waiting to decrypt it once quantum is ready.

• Military Edge Eroding: Quantum sensing could make U.S. stealth technology vulnerable. Quantum navigation could enable Chinese forces to operate undetected in GPS-denied environments.

• Supply Chain and Talent Shortages: The U.S. struggles to secure quantum supply chains and fill talent gaps, risking both innovation and security.

• Global Technology Divide: The race risks a world where a handful of nations—especially China and the U.S.—control the most powerful computation and communication tools, deepening global inequality and increasing the risk of technological surprise in future conflicts.

U.S. Response: Scrambling to Catch Up

• Export Controls: The U.S. has imposed strict controls on quantum hardware and software, and banned investments in Chinese quantum firms.

• Post-Quantum Cryptography: The NSA has set a 2035 deadline for government-wide adoption of quantum-resistant encryption.

• Allied Coordination: The U.S. is working with allies to keep quantum advances out of adversarial hands, while investing in domestic research and workforce development.

Commercialization: Quantum Goes Mainstream

Quantum computing is no longer confined to research labs. Commercial sales hit $854 million in 2024, up 70% from the previous year. Real-world applications are emerging:

• Financial Services: Quantum optimization for trading and risk modeling.

• Drug Discovery: Quantum simulation for pharmaceutical breakthroughs.

• Materials Science: Designing new materials for batteries and energy storage.

• Cybersecurity: Quantum key distribution for “unbreakable” communications.

The Talent Crunch

A new bottleneck has emerged: talent. There’s only one qualified candidate for every three quantum jobs. The skills gap threatens to slow progress, regardless of funding. The good news? More than half of industry jobs don’t require a PhD—upskilling from adjacent fields is working.

The Ethical and Strategic Dilemma

Quantum’s rise brings new ethical and strategic risks:

• Resource Inequality: Quantum capabilities are concentrating among a few nations and corporations.

• Transparency: Quantum algorithms are often black boxes, complicating accountability.

• Privacy: The ability to break encryption threatens fundamental privacy rights.

• Job Displacement: Automation potential could disrupt entire industries.

The Path Forward: Collaboration or Confrontation?

Despite tensions, international collaboration continues—Europe’s Quantum Internet Alliance, U.S.-Singapore partnerships, and more. But the trend is toward militarization and restricted cooperation, especially as quantum becomes a pillar of national security.

Table: Key Geopolitical Threats and U.S. Risks

(sorry for table format, substack doesn’t do my kinds of tables well)

Conclusion: The Battle Isn’t Over—But the Window Is Open

The “battle for quantum supremacy” hasn’t been won or lost. Instead, it’s evolved into a more complex, urgent, and consequential contest. China leads in government investment and quantum communications; the U.S. leads in private innovation and hardware (cavear - we think). The next five years will decide who turns quantum promise into quantum power.

But here’s the new reality: we are now living in the quantum threat window. The only question is how far in we are—and whether we’ll recognize the moment when the world’s encryption is truly broken. The future of national security, economic competitiveness, and even global privacy hangs in the balance. The only certainty? The battle is far from over—and the stakes have never been higher.

— Sultan

One of the most common questions I get—right after “Is AI going to take my job?” and “Can AI write my emails for me?”—is: How do you keep up with everything happening in AI? The truth is, even for someone who lives and breathes this stuff, it’s overwhelming. There are more headlines, breakthroughs, and think pieces than there are “AI will change everything” conference panels.

So, what’s my secret? I did what any self-respecting AI nerd would do: I had an AI platform build a news site for me (I used our native AI platform Limni from Frontier Foundry, which runs securely on my laptop without an internet connection). Not just any news site—a bespoke aggregator that surfaces the most-discussed AI stories from tier 1 news outlets, then cross-checks what’s echoing across social media. It’s like having a Bloomberg terminal (or OpenBB, but for AI, and with fewer existential crises.

Introducing: AI Trend Tracker

This little experiment is now my go-to dashboard for:

• Top AI headlines from sources that actually check their facts

• Stories trending on social media (so I know what everyone’s arguing about)

• A clean, distraction-free UI (no pop-ups, no “One Weird Trick” ads)

And yes, it updates itself. No more doomscrolling through a dozen tabs or chasing social rabbit holes.

EDIT: Here it is!

What Does It Look Like?

Below are a few screenshots from my AI Trend Tracker test app:

Confession: This Is What I Do on Conference Calls

Let’s be honest: we all multitask during conference calls. Some people doodle. Some people check their fantasy football scores. Me? I’m refreshing my AI news dashboard, pretending to nod sagely while actually reading about the latest LLM drama or regulatory kerfuffle. If you see me looking extra thoughtful on Zoom, now you know.

Pro tip: If you’re ever asked a question mid-call, just say, “I’m sorry you broke up there, can you ask me that again?” Works every time.

Why Build My Own?

Sure, there are plenty of news aggregators out there—Feedly, Google News, Flipboard, News360, you name it. But none of them quite nailed the combination of:

• Tier 1 news only (no clickbait)

• Social signal boosting (what’s actually being discussed)

• AI-powered curation (not just RSS feeds)

• Minimal distractions (my attention span is precious)

• Slightly humorous summaries (see my previous bullet lol)

So, I did what any builder does: I built (well, had AI build) my own. It’s not perfect, but it’s already saving me hours a week—and a few brain cells.

This also allows me to continue to iterate on it (note the 2.0 in the screenshot above)… there are some fun new features coming (ranking/rating posts relative to my interests, automate putting drafts into social media, push notifications for certain things to my phone directly or run it locally on my phone as a private app as a few examples).

This entire app build and deployment took me less than 3 hours, half of which I did while on a beach while getting a tan. I’ve built over 10 apps like this that run in the background of my life keeping me sync’d and moving forward while carving out time to be a good husband, father and (occasionally) even relax.

Like What You See?

Should I open source this and put it on Github? Curious about your thoughts dear reader

Coming Up Next

This is the first in a series of real-world examples of how modern AI isn’t just hype—it’s delivering real value, right now. Next up: how I use AI to automate the world’s most boring compliance tasks (and why my legal team both loves and fears me).

Stay tuned. And if you see me on a conference call, know that somewhere, an AI is making sure I’m up to date—even if I’m not.

— Sultan

EDIT: Here’s the full yahoo link

Click here if you’re interested in watching my appearance on Yahoo Finance discussing this.

Notable Investors and Absent Players 

The fundraising attracted marquee investors such as Nvidia and Microsoft, yet Apple’s absence from the round was conspicuous. Nvidia's involvement stands out, given the hardware giant’s integral role in AI development through its GPU technology. However, the heavy involvement of both Nvidia and Microsoft also stirs subtle antitrust concerns, especially as these two titans hold significant influence over the broader AI ecosystem. Microsoft’s involvement seems less problematic than Nvidia’s, but their continued investments in AI startups do fuel conversations about potential antitrust behavior in AI infrastructure and services. 

Valuation and Revenue Expectations 

The implied valuation of OpenAI is now estimated at over $150 billion, a staggering figure that raises significant questions. With previous losses of $5 billion reported, industry watchers might expect OpenAI’s current revenue to reflect a more substantial number, somewhere closer to $7 billion, based on conventional venture norms. Instead, OpenAI’s most recent figures suggest revenue around $3.7 billion, leaving a notable gap between the expected financial performance and the actuals. This discrepancy highlights the challenges of aligning market expectations with the realities of current AI revenue generation. 

A Skewed Market: The Impact on AI Startups 

OpenAI’s fundraising underscores a broader issue in the AI venture capital market—there’s an imbalance in funding distribution. If we remove the top five AI deals of the year, the amount of capital remaining for the rest of the AI market is meager. Early-stage AI startups are simply not generating the revenue levels investors are expecting, creating the impression that the market is overvalued. As we look toward 2025, it seems increasingly unlikely that we will see many more mega-deals at the scale of OpenAI's. 

Furthermore, while there are ten startups led by former OpenAI employees that have raised over $25 billion collectively, these companies are struggling to translate that capital into revenue. Combined, these ventures report less than $100 million in revenue—a stark reminder that high valuations are not necessarily a reflection of current financial success. 

Racing to Justify Valuations 

In response to the immense capital raised and the lofty valuation attached to it, OpenAI is under pressure to rapidly build revenue and justify its standing in the AI sector. Part of this effort involves aggressive hiring, particularly targeting senior AI talent to compensate for significant attrition in recent years. Additionally, OpenAI is following in the footsteps of companies like Scale AI by hiring teams to perform manual data work, signaling a shift in its operational strategy. 

Beyond hiring, OpenAI is also investing heavily in lobbying efforts aimed at shaping AI regulation. As AI becomes increasingly integrated into sectors like healthcare, finance, and national security, the regulatory landscape will play a crucial role in determining the success of these technologies. OpenAI’s efforts to influence this space are critical to ensuring they remain a dominant player in the AI field. 

The Next Generation of AI Startups 

Interestingly, the newest wave of AI startups looks markedly different from OpenAI. Rather than relying on centralized clouds packed with Nvidia GPUs, these emerging firms are focused on specific industries and use cases. This approach mirrors the early days of cloud adoption by enterprises, where companies slowly embraced the technology over a 10 to 15-year period. There’s a parallel here with the move away from mainframes to open systems in the 1990’s as well. The reality is that it will take more than a few years for AI companies' revenues to catch up with the hype. Customers are still grappling with understanding how best to leverage AI, and the tangible value these technologies offer is still materializing. 

In contrast to OpenAI, many of these new AI companies are leaner, more focused, and on a faster path to profitability. They aren’t chasing general-purpose AI but rather targeting niche applications with clear and immediate business value. This positions them to scale and generate revenue faster, potentially disrupting the broader AI ecosystem in ways that OpenAI’s slower, more capital-intensive approach has yet to do. 

Conclusion: Navigating an Overheated Market 

OpenAI’s record-breaking fundraising highlights both the incredible potential and the growing challenges within the AI sector. While this influx of capital signals continued optimism in AI’s transformative power, the realities of revenue generation, competition, and market expectations are casting a shadow over these valuations. As we move toward 2025, the AI sector may see fewer large-scale deals, and success will hinge more on practical revenue generation rather than inflated market expectations. The next generation of AI startups will likely define the future of this industry, driven by a focus on specialization and profitability, in stark contrast to the generalist approach of OpenAI. 

For those who've been following my commentary over the last two years, you’ve heard me argue that the demand for Nvidia’s kind of chips—especially those powering the development of foundational AI models—is fundamentally transient. Sure, there’s still plenty of money to be made right now, but let’s not kid ourselves: the writing is on the wall. Even the notion of ‘foundational model’ is open for debate (even if you can define it).

Take a look around. Our own company has been running multi-billion parameter language models on nothing more than off-the-shelf laptops. This isn’t some hypothetical future—it’s happening now (and has been for quite a while!). The idea that you need sprawling data centers and massive cloud infrastructure to support most of today’s AI applications is increasingly a relic of the past. This shift isn’t just about the tech nerds like us tinkering in our labs. Even big players like Apple are moving towards on-device processing, a strategy we’ve championed since last year at Frontier Foundry. Our first LLM was called “Limni” - “Lake” in greek in case you didn’t know.

What does this all mean? It means that companies spending eight or nine figures on hardware may soon find themselves questioning those decisions. Software is eating the world, as they say, and nowhere is this truer than in AI. Companies will increasingly pivot from those heavy, capex-heavy hardware investments towards more agile and cost-effective software solutions, the kind that deliver almost the same power without the need for the massive physical footprint or energy consumption.

How many times do we need to learn this lesson? Software always beats hardware. We’re just watching it play out on a new stage.

==========

[^1]: Nvidia's revenue and earnings data sourced from their Q2 2024 financial report, showing a remarkable increase in data center revenue and a total earnings beat, but with slowing growth compared to previous quarters.

[^2]: [Investopedia Live Coverage](https://www.investopedia.com) detailing Nvidia's shift in market dynamics and investor concerns about long-term demand trends.

[^3]: Commentary on Apple's shift to on-device AI processing, a movement away from heavy reliance on centralized cloud infrastructures

I wrote this op-ed in The Wall Street Journal as a corrective. I want people to understand that AI will have a tangible, beneficial impact on their lives. This op-ed spoke to how AI will lower food prices, where Americans are feeling the effects of inflation most acutely. But AI will benefit real people in countless ways, including:

• Transportation: Nothing aggravates people as much as sitting in traffic, and that traffic has enormous economic consequences, too. What if AI determines our traffic patterns, instead, shaving countless cumulative hours off of people’s commutes every year? And let’s not forget the enormous potential for self-driving cars, which are only possible because of AI, to save lives by limiting traffic accidents.

• Healthcare: Diagnosing patients can be extremely difficult, particularly if they have symptoms that could point to any number of underlying causes. But AI can detect patterns a doctor would never think to look for to identify the cause of someone’s medical issue. We are not far from AI detecting cancer or heart disease in patients long before it shows up on any tests, and, consequently, saving their lives.

• Climate: The climate crisis is here, and it is real. The only way we can reverse this worrying trend is to cut carbon emissions immediately and substantially. AI can help companies, governments, and even private households identify quick, relatively easy ways they may not have considered to lower their emissions footprint.

• Financial Services: Racial discrimination, both subconscious and very overt, has plagued the financial services industry for as long as there have been financial services. In recent decades, this has been most acute in lending and credit decisions. AI can identify where people have been refused credit for good reasons, and when discrimination is the only feasible answer. Once we know when and where discrimination is happening, we can far more effectively root it out.

• Education: As any parent who has ever worked with a school district to get their child an individualized education plan (IEP) can attest, getting educational plans tailored for a student can be painful. And that’s with school districts only on the hook for IEPs for students with special needs. With AI, though, every student in America could get an individualized education plan, tailored to their strengths and areas for improvement, and even, thanks to burgeoning technologies like those found in ChatGPT, get the materials that will help them follow through on their IEPs.

This is just the tip of the iceberg. There is so much else AI could do for people in ways they can see and feel. It’s up to us to make sure they know.

I’m not referring to the upskilling of our current workforce on AI topics nor the continued education of top AI professionals at our universities, though both are extremely important. I’m talking about AI’s impact on our workforce. Perhaps owing to the rapid release of large language models (LLMs) this year, I’ve heard some rumblings about how LLMs and other forms of AI will impact workers across industries. Will GPT4 replace coders? Will AI eliminate the need for customer service jobs? What is really going on here?!?!? Rest easy, friends. Read on. 

1. Administrative Burdens: Every job has aspects that no one likes. Mostly it’s just simple paperwork, maybe records management, maybe (no, definitely) it’s travel vouchers. Whatever it is, we all encounter this kind of sludge in our work. We accept this as reality, but every time we are pulled off a task for something like this, we are pulled away from being experts in our field. The first place we will see this play out is in the AUGMENTATION of the workforce by removing administrative burdens. This will improve efficiency and value creation across organizations by letting talent flourish and remove the sludge.  

2. Better Decision Making: I know, I know, you are an eminent expert, and you know your field better than anyone else. But hey, we could all use a hand, right? Another opportunity for augmentation is in the improvement of data used by humans to make decisions. Notice I did not say the AI is making the decisions. AI can process mountains of data and provide a simple visualization of critical points that human decision makers can use to inform their decisions. Again, more efficiency, more value creation, allowing the experts to be experts and not wasting their time on administrative functions, managing spreadsheets.  

3. Time Reduction: Finally, AI, particularly generative AI, will reduce the time it takes to create products by doing the initial heavy lifting to be followed by a human who refines the output. We should all check to verify the ChatGPT output we get for anything we ask it for. You might ask it to generate an opening paragraph to get you started on a product or you might ask it to create some code for you. Even if the output isn’t perfect, it is a start and it will reduce the time you and your people spend on creating products. 

As AI flies off the shelves and tech podcasters struggle to keep up, remember...augmentation & automation of the stupid. The workforce conversation is likely to continue but if it is allowed to be controlled by those who claim AI is coming for our jobs, that will define the industry for years to come. That doesn’t have to be the case. Let’s start talking augmentation when we talk about workforce and talk about opportunities to let our human experts be experts and not caught in sludge.  

Executive Summary & Takeaways

Quantum technologies are poised to radically alter the technical, political and commercial landscape of the US-Chinese dynamic. As it stands, the US is in a greatly disadvantaged position and current investments do not close the gap in a meaningful timeline. From the US national security, to American competitiveness, a Chinese lead in Quantum could well define the rest of the 21st century.

It is entirely possible this battle has already been lost. Can we be sure that this brief has not already been read by a PRC quantum analyst put in place to keep the full scope of their advances hidden? Call it a Quantum Coventry strategy (after the WW2 attack that killed over 600 people, linked to keeping the cracking of ENIGMA secret).

1. The Chinese have invested, at a minimum 10 times as much as the US in quantum technologies, unless there are secret investments north of $50bn.

2. In 2016 they began publicly announcing quantum radar and communications systems in real world environments

3. Offensive cyber and other espionage applications, coupled with quantum tools, allows the Chinese government to advance global commercial interests and accelerate their advances.

4. Using the industrialization of genetics as a model, based on interviews with leaders in the field, it is possible that by the end of the year (if not already), quantum computers capable of breaking current encryption standards will be available.

5. The passage of the US National Quantum Initiative Act, a 10 year new set of programs across a variety of federal agencies, around $1.25bn in investment and some other activities is a good step but a woefully tiny drop in the bucket.

[NOTE: This was originally published in a slightly different form on 8 Aug, this version has been edited for public consumption]

Introduction

Quantum computing represents a category of technologies that shift computational technologies out of the digital for the first time. Digital — the 1’s and 0’s that are ubiquitous today — are replaced by a near infinite set of numbers, with a corresponding massive increase in computational power. Instead of working on one calculation, a similarly sized computer could calculate an entire category of calculations, picking the best and moving on. Coupled with AI, it will soon change the face of technology in a myriad of ways.

For the last 20 years, the Chinese government has invested orders of magnitude more into these technologies, and while much of it is shrouded in secrecy, what is known now is generations ahead of what is available anywhere else. From communications systems, to radar, to encryption and advanced analytics, they are generations ahead of the United States. Coupled with the offensive cyber capabilities the Chinese make use of on a daily basis, it presents a clear and present threat to the US, US corporations and US global interest.

A few places where Quantum already has an impact

Quantum Communication: Quantum communications systems are proofed against eavesdropping, as any attempt to eavesdrop would change the entangled quantum state, alerting the users. In 2016, the Chinese launched a quantum communications satellite — Micius. Micius is the first step in China building a nationwide quantum network for military communications and financial transactions.

Quantum Radar: Quantum radar is a photo-based radar system that, unlike electromagnetic platforms in use today, can bypass stealth technologies, and is immune to most if not all interference technologies. The same month as the launch of Micius in 2016, the Chinese announced a quantum radar platform in a real-world environment.

Quantum Technologies Breaking Encryption: While we might be a few years away from a pure quantum computer — one that could replace the computers used for in general use environments today — specially designed quantum computers with a focus on breaking encryption are much closer, if not already here. Nearly every encryption platform in general use, from communications to financial transactions, to healthcare records is vulnerable to this kind of attack. Lattice cryptography is one example of an emerging cryptographic technology that may prove less vulnerable to quantum attacks.

A brief note: one cannot ignore the timing of these announcements in communications and radar vis-à-vis the US presidential election and within the context of the ‘special relationship’ between China and Russia. The timing raises more questions than answers.

Comparison of Current US and Chinese Investment

While the full scope of Chinese investment is not known, what is publicly known, puts their investments at a different order of magnitude. This year they announced a 4 million square foot industrial research facility, spending over $10 billion. Best estimates on Chinese investments in Quantum start on the low end at $50 billion and go up from there. They have been spending more for longer. As far back as the 1980’s it has been known these technologies are the next step on the innovation curve — hints of Chinese research and investment started then.

In June, the National Quantum Initiative Act was proposed, with broad congressional support. The plan proposes a 5 year, $1.275bn set of activities at DoE, NSF and NIST focused on primary research in a variety of quantum areas, as well as directing the federal research community to build a 10-year strategic plan. On an annualized basis, the $200m or so per year earmarked currently (implementation looks to start in 2019 given the current state of the legislation) is less that 1/50th of what the Chinese appear to be spending (ignoring secret/classified spend on both sides). On that front, even if the US had a secret investment of which the general public is unaware, it would have to have totaled well over $50bn over the last 15 years to put the US program in the same ballpark as the Chinese.

History of Chinese Technical Attacks

Without rehashing the history of offensive Chinese cyber activities, a brief mention of the hacks of the JSF, B-2, drone and other programs that have led to the development of the J-20, J-31, H-20, CH-5 and other weapons systems are worth highlighting. Also, worth mentioning are the backdoors into telecommunications infrastructure, compromised supply chains of networking hardware vendors and the use of smartphone-based ultrasonic, and their discoveries near US IC facilities.

The point here is that it is an error to consider the Chinese public and private sectors as separate entities — they exist together. Anything learned through cyber means is disseminated to all interested/needed parties. As such, advances in any one specific area (i.e. quantum) are available across the entire Chinese ecosystem.

What we have seen to date in terms of Chinese offensive capabilities is simply a foundation, a road map if you will, to our public and private sectors and that the Chinese will leverage everything they can get both politically and commercially.

Chinese Competitiveness & Industrialization

The Chinese are rather unimaginative when it comes to building large scale programs around innovative technologies, as such you can use other areas for modeling their activities. The key for this dialogue is when the inflection curve crosses the industrialization line. Clinical genetics is a close recent example — BGI. While BGI was started over 20 years ago, in 2010 they received an investment of $1.5 billion which, within 12 months, had scaled their industrial capabilities to dominate the market. At mid-point in that year only 2,700 human genomes had been sequenced. By the end of the year it was over 30,000. The rest of the Chinese ecosystem (like Wuxi) emerged quickly to drive applied use of it — today Wuxi has the largest biologic library and manufacturing plant in the world and is a leading force in drug development. Within 3 years, the Chinese had industrial scale animal cloning in use.

It is difficult to imagine the Chinese driving the kind of industrial scale into quantum without significant advances outside of the public eye. When the Chinese pull the industrialization lever, global impact is not far off.

Conclusion

While most look to D-wave Systems, Google, IBM and other western companies as the harbingers of innovation, in the case of Quantum, all eyes should be on the Chinese. Review the takeaways at the top, but it is clear they are far ahead, and investing appropriately to dominate this market, with wide ranging impacts for the US today, and in the future.

Follow-up Reading:

· https://www.bloomberg.com/news/articles/2018-04-08/forget-the-trade-war-china-wants-to-win-the-computing-arms-race

· https://foreignpolicy.com/2017/11/28/the-quantum-gap-with-china/

· https://www.idquantique.com/chinas-growing-investment-in-quantum-computing/

· https://www.scmp.com/news/china/economy/article/2140860/china-winning-race-us-develop-quantum-computers

· https://www.mcclatchydc.com/news/nation-world/national/national-security/article179971861.html

· https://blogs.wsj.com/cio/2017/10/24/u-s-should-invest-more-in-quantum-computing-research-business-and-government-officials-say/

· https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2576262/

· https://www.zdnet.com/article/ibm-warns-of-instant-breaking-of-encryption-by-quantum-computers-move-your-data-today/

· https://www.aip.org/fyi/2018/quantum-legislation-leaps-forward

· https://foreignpolicy.com/2017/11/28/the-quantum-gap-with-china/

· https://en.wikipedia.org/wiki/Ilyas_Khan

· https://thediplomat.com/2016/12/behind-china-and-russias-special-relationship/

https://gizmodo.com/house-passes-bill-to-advance-quantum-science-in-the-u-s-1829060167