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From RSA to Lattices: The Quantum Safe Crypto Shift
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The race to transition online security protocols to ones that can’t be cracked by a quantum computer is already on. The algorithms that are commonly used today to protect data online—RSA and elliptic curve cryptography—are uncrackable by supercomputers, but a large enough quantum computer would make quick work of them. There are algorithms secure enough to be out of reach for both classical and future quantum machines, called post-quantum cryptography, but transitioning to these is a work in progress.
Late last month, the team at Google Quantum AI published a whitepaper that added significant urgency to this race. In it, the team showed that the size of a quantum computer that would pose a cryptographic threat is approximately 20 times smaller than previously thought. This is still far from accessible to the quantum computers that exist today: The largest machines currently consist of approximately 1,000 quantum bits, or qubits, and the whitepaper estimated that about 500 times as much is needed. Nonetheless, this shortens the timeline to switch over to post-quantum algorithms.
The news had a surprising beneficiary: Obscure cryptocurrency Algorand jumped 44% in price in response. The whitepaper called out Algorand specifically for implementing post-quantum cryptography on their blockchain. We caught up with Algorand’s chief scientific officer and professor of computer science and engineering at the University of Michigan, Chris Peikert, to understand how this announcement is impacting cryptography, why cryptocurrencies are feeling the effects, and what the future might hold. Peikert’s early work on a particular type of algorithm known as lattice cryptography underlies most post-quantum security today.
IEEE Spectrum: What is the significance of this Google Quantum AI whitepaper?
Peikert: The upshot of this paper is that it shows that a quantum computer would be able to break some of the cryptography that is most widely used, especially in blockchains and cryptocurrencies, with much, much fewer resources than had previously been established. Those resources include the time that it would take to do so and the number of qubits (or quantum bits) that it would have to use.
This cryptography is very central to not just cryptocurrencies, but more broadly to cryptography on the internet. It is also used for secure web connections between web browsers and web servers. Versions of elliptic curve cryptography are used in national security systems and military encryption. It’s very prevalent and pervasive in all modern networks and protocols.
And not only was this paper improving the algorithms, but there was also a concurrent paper showing that the hardware itself was substantially improved. The claim here was that the number of physical qubits needed to achieve a certain kind of logical qubit was also greatly reduced. These two kinds of improvements are compounding upon each other. It’s a kind of a win-win situation from the quantum computing perspective, but a lose-lose situation for cryptography.
IEEE Spectrum: What do Google AI’s findings mean for cryptocurrencies and the broader cybersecurity ecosystem?
Peikert: There’s always been this looming threat in the distance of quantum computers breaking a large fraction of the cryptography that’s used throughout the cryptocurrency ecosystem. And I think what this paper did was really the loudest alarm yet that these kinds of quantum attacks might not be as far off as some have suspected, or hoped, in recent years. It’s caused a reevaluation across the industry, and a moving up of the timeline for when quantum computers might be capable of breaking this cryptography.
When we think about the timelines and when it’s important to have completed these transitions [to post-quantum cryptography], we also need to factor in the unknown improvements that we should expect to see in the coming years. The science of quantum computing will not stay static, and there will be these further breakthroughs. We can’t say exactly what they will be or when they will come, but you can bet that they will be coming.
IEEE Spectrum: What is your guess on if or when quantum computers will be able to break cryptography in the real world?
Peikert: Instead of thinking about a specific date when we expect them to come, we have to think about the probabilities and the risks as time goes on. There have been huge breakthrough developments, including not only this paper, but also some last year. But even with these, I think that the chance of a cryptographic attack by quantum computers being successful in the next three years is extremely low, maybe less than a percent. But then, as you get out to several years, like five, six, or 10 years, one has to seriously consider a probability, maybe 5 percent or 10 percent or more. So it’s still rather small, but significant enough that we have to worry about the risk, because the value that is protected by this kind of cryptography is really enormous.
The U.S. government has put 2035 as its target for migrating all of the national security systems to post-quantum cryptography. That seems like a prudent date, given the timelines that it takes to upgrade cryptography. It’s a slow process. It has to be done very deliberately and carefully to make sure that you’re not introducing new vulnerabilities, that you’re not making mistakes, that everything still works properly. So, you know, given the outlook for quantum computers on the horizon, it’s really important that we prepare now, or ideally, yesterday, or a few years ago, for that kind of transition.
IEEE Spectrum: Are there significant roadblocks you see to industrial adoption of post-quantum cryptography going forward?
Peikert: Cryptography is very hard to change. We’ve only had one or maybe two major transitions in cryptography since the early 1980s or late 1970s, when the field first was invented. We don’t really have a systematic way of transitioning cryptography.
An additional challenge is that the performance trade-offs are very different in post-quantum cryptography than they are in the legacy systems. Keys and cipher texts and digital signatures are all significantly larger in post-quantum cryptography, but the computations are actually faster, typically. People have optimized cryptography for speed in the past, and we have very good fast speeds now for post-quantum cryptography, but the sizes of the keys are a challenge.
Especially in blockchain applications, like cryptocurrencies, space on the blockchain is at a premium. So it calls for a reevaluation in many applications of how we integrate the cryptography into the system, and that work is ongoing. And, the blockchain ecosystem uses a lot of advanced cryptography, exotic things like zero-knowledge proofs. In many cases, we have rudimentary constructions of these fancy cryptography tools from post-quantum-type mathematics, but they’re not nearly as mature and industry-ready as the legacy systems that have been deployed. It continues to be an important technical challenge to develop post-quantum versions of these very fancy cryptographic schemes that are used in cutting-edge applications.
IEEE Spectrum: As an academic cryptography researcher, what attracted you to work with a cryptocurrency, and Algorand in particular?
Peikert: My former Ph.D. advisor is Silvio Micali, the inventor of Algorand. The system is very elegant. It is a very high-performing blockchain system, and it uses very little energy, has fast transaction finalization, and a number of other great features. And Silvio appreciated that this quantum threat was real and was coming, and the team approached me about helping to improve the Algorand protocol at the basic levels to become more post-quantum secure in 2021. That was a very exciting opportunity, because it was a difficult engineering and scientific challenge to integrate post-quantum cryptography into all the different technical and cryptographic mechanisms that were underlying the protocol.
IEEE Spectrum: What is the current status of post-quantum cryptography in Algorand, and blockchains in general?
Peikert: We’ve identified some of the most pressing issues and worked our way through some of them, but it’s a many-faceted problem overall. We started with the integrity of the chain itself, which is the transaction history that everybody has to agree upon.
Our first major project was developing a system that would add post-quantum security to the history of the chain. We developed a system called state proofs for that, which is a mixture of ordinary post-quantum cryptography and also some more fancy cryptography: It’s a way of taking a large number of signatures and digesting them down into a much smaller number of signatures, while still being confident that these large number of signatures actually exist and are properly formed. We also followed it with other papers and projects that are about adding post-quantum cryptography and security to other aspects of the blockchain in the Algorand ecosystem.
It’s not a complete project yet. We don’t claim to be fully post-quantum secure. That’s a very challenging target to hit, and there are aspects that we will continue to work on into the near future.
IEEE Spectrum: In your view, will we adopt post-quantum cryptography before the risks actually catch up with us?
Peikert: I tend to be an optimist about these things. I think that it’s a very good thing that more people in decision-making roles are recognizing that this is an important topic, and that these kinds of migrations have to be done. I think that we can’t be complacent about it, and we can’t kick the can down the road much longer. But I do see that the focus is being put on this important problem, so I’m optimistic that most important systems will eventually have good either mitigations or full migrations in place.
But it’s also a point on the horizon that we don’t know exactly when it will come. So, there is the possibility that there is a huge breakthrough, and we have many fewer years than we might have hoped for, and that we don’t get all the systems upgraded that we would like to have fixed by the time quantum computers arrive.
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Nvidia closes in on Hugging Face acquisition
Nvidia has agreed to buy Hugging Face for $12.9 billion, The Information reported Wednesday night, citing a source familiar with the matter. Business Insider, which first reported over the weekend that Hugging Face was fielding takeover interest, reported Wednesday night that the talks — which would value the company at more than $13 billion — had not yet produced a signed agreement and could still atomize.
TechCrunch reached out earlier to both Nvidia and Hugging Face for comment, and neither has yet responded. (Nvidia’s silence is particularly noteworthy here as the company has moved quickly in the past to address reports it considers inaccurate.)
Maybe it was destined from the start. Hugging Face, founded in 2016, is one of the most popular hubs where developers share and download open-source AI models. Buying it would give Nvidia a strong foothold in the world of open-source AI, right as open-source developers are doing their level best to catch up to closed AI systems from companies like Anthropic and OpenAI.
Why would Nvidia want that? Most obviously, it comes down to protecting its dominance in AI chips, which, from the outside at least, appears increasingly at risk, even with Nvidia’s aggressive chip-release schedule. Pretty much all of the biggest closed-source AI labs (OpenAI, Google, Amazon, and Anthropic) are now in the process of building their own AI chips to lessen their reliance on Nvidia. A thriving ecosystem of open-source AI models gives customers more alternatives to those closed labs, which in turn keeps more of the market dependent on Nvidia’s hardware. That’s also why Nvidia has already poured tens of billions of dollars into building its own open-source AI models.
Should we be surprised that Hugging Face’s days as an independent outfit appear numbered? Not really. Hugging Face CEO Clem Delangue has spent much of this year publicly aligned with Nvidia’s open-source push, amid a debate that has been building for months, as Washington officials reportedly weighed restrictions on open-weight models. (After Chinese labs like Moonshot AI released systems like its Kimi K3 model that matched leading U.S. models on benchmarks while costing a lot less to run, talk of competitive and national-security concerns appeared to grow in Washington, with some critics of closed labs — like White House advisor David Sacks — suggesting the fears were being fanned by the “duopoly” of Anthropic and OpenAI.)
In an appearance on CBS’s “Face the Nation” earlier this month, for example, Delangue said Hugging Face used an Nvidia-modified version of a Chinese open-source model to defend itself after a cyberattack and pointed to a recent letter — signed by Nvidia CEO Jensen Huang and 24 other companies, including Hugging Face — urging the U.S. government to support open models rather than restrict them. In a separate CNBC interview in late July, Delangue made similar points, citing that same letter while warning that China is “clearly dominating” open-source AI.
The deal would also mark something of a comeback for Nvidia in cloud computing. Nvidia reportedly scaled back its own cloud business, called DGX Cloud, about a year ago. But according to The Information, owning Hugging Face — which already helps developers run their AI models using rented computing power — could give Nvidia a way back into that market without starting from scratch.
There’s also a financial safety net at play. Nvidia has promised to help cover the cost of tens of billions of dollars in cloud computing deals for its customers. If those customers end up not using all the computing power they signed up for, Nvidia could get stuck with it. Owning Hugging Face would give Nvidia the ability to sell that unused capacity to Hugging Face’s customers.
The price marks a huge jump from Hugging Face’s last known value. The company raised $235 million in 2023 in a funding round that valued it at $4.5 billion. That round was led by Salesforce Ventures, with money also coming from Alphabet’s GV, IBM Ventures, and Nvidia itself, among others.
This wouldn’t be Hugging Face’s first brush with an Nvidia offer, either. Hugging Face turned down a $500 million investment offer from Nvidia late last year that would have valued it at $7 billion, the Financial Times previously reported. Hugging Face said at the time it didn’t want a dominant investor that could sway its decisions.
As for why it would say yes now, one could argue that a buyout is different from taking on one giant backer — a scenario that often means ceding control while being pressured to continue growing.
Hugging Face is also still a comparatively small business by revenue in the world of AI. The Information reported it was recently generating about $150 million a year in revenue, up from roughly $100 million just two months earlier.
That growth has enabled the company to get “close to profitability,” as Delangue told TechCrunch last month. Still, a price near $13 billion would be a massive multiple for a company this size and hard to resist.
Not last, the deal would give Hugging Face access to Nvidia’s much deeper pockets just as other, AI infrastructure competitors start to get pulled into other outfits, as suggested by Stripe’s recent deal to acquire OpenRouter, a startup founded in early 2023 that helps customers select different AI models to perform different tasks depending on their needs and budget.
OpenRouter was valued at just $1.3 billion back in May during its Series B round. Stripe reportedly paid more than $7 billion to make it its own earlier this month.
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Tech
OpenAI Restores 5-Hour Codex Limit for ChatGPT Plus
ChatGPT Plus users got a taste of fewer restrictions, but OpenAI has now put the clock back on Codex and ChatGPT Work.
OpenAI restored a usage allowance that resets every five hours for ChatGPT Plus subscribers using Codex and ChatGPT Work on Aug. 25, ending a temporary period when only the weekly quota applied.
Thibault “Tibo” Sottiaux, an OpenAI engineering lead working on Codex and ChatGPT, announced the change on X after the company temporarily removed the five-hour restriction in July. The move gave Plus users more freedom to use the tools during individual sessions, but that period has now ended.
Sottiaux said the five-hour window helps OpenAI spread computing demand more evenly while preserving a relatively generous weekly allowance.
He also said some newer and more casual Plus subscribers were unintentionally consuming their entire weekly allowance in a single stretch, leaving them confused when they could no longer use the tools.
What happens when users hit the limit
The five-hour restriction works alongside the weekly quota. Once a Plus subscriber exhausts either allowance, the user must wait for the relevant reset or purchase additional credits to continue using Codex.
OpenAI had temporarily removed the five-hour window in July, while also resetting weekly allowances early on some occasions as Codex and ChatGPT Work reached usage milestones. That gave developers and other heavy users a short period of greater flexibility before the restriction returned.
For now, Sottiaux said the five-hour restriction will remain disabled “for the upcoming months” for users on the plans he identified as the $100 and $200 tiers. Enterprise and Edu accounts use a separate credit-based system and are not covered by the Plus-plan change.
More must-read AI coverage
A less predictable experience for developers
For frequent Codex users, the restored five-hour window reduces flexibility. A developer working through a demanding project could exhaust that window’s allowance even when weekly usage remains available.
But there is a practical reason for the restriction. AI coding tools can consume significant computing resources, and allowing users to concentrate large amounts of usage into short periods can make demand harder to manage. Spreading that usage across five-hour windows gives OpenAI more control over its infrastructure while preserving a larger weekly pool.
Plus subscribers working on demanding projects now need to monitor both their five-hour and weekly allowances. Before beginning a long coding session, users should check their remaining capacity and plan for a reset or additional credit purchase if either allowance is running low. The change gives OpenAI more control over computing demand, but it also makes usage less predictable for developers who rely on Codex throughout the workday.
Read more: OpenAI’s Codex Windows app brings its AI coding workspace to more developers, with tools for managing multiple coding tasks from a dedicated desktop interface.
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Viral AI startup Instinct has raised $350 million at a $2.5 billion valuation
Instinct, a startup founded only last year and helmed by a 23-year-old, has managed to ride the wave of AI enthusiasm toward a gargantuan valuation over the course of the summer.
The company, which offers an AI assistant that has inspired enthusiasm among its early users, told the Wall Street Journal on Wednesday that it had raised $250 million in a recent Series B funding round. That new round brings the company’s total funding to $350 million and gives the startup a valuation of $2.5 billion.
That new funding round was co-led by Index Ventures and Benchmark, the Journal reported.
Instinct, which is offered by the company Spear Street Technology and led by founder Noah Shinn, is an agent that the company says can efficiently organize your life. Users connect it to their apps and devices and can communicate with it via texts and calls.
“I’m thrilled with everything our early users are doing with Instinct,” Shinn wrote in a tweet on Wednesday. “They’ve told us they’ve planned cross-country road trips, bought weekly groceries and concert tickets, and cancelled hundreds of dollars of subscriptions. Someone’s even planning their wedding with Instinct.”
Instinct, which rocks a decidedly lo-fi website, is currently in private beta, but it has already inspired a certain amount of controversy due to privacy concerns. Online, users have worried about the overly generous permissions that the app requires as well as its terms of use that has disturbed some users because of their invasive potential.
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