Token Talk 27: Sovereignty as a Service

Jensen Huang made his pitch.

The CEO of the world’s first $4 trillion company met with President Trump and pushed to restart Nvidia chip sales to China, challenging the U.S.’s hawkish AI stance. He won over Commerce Secretary Howard Lutnick, who said the goal is to get Chinese developers “addicted to the American technology stack.”

Shortly after, Nvidia announced it would resume H20 chip sales to China while ramping up U.S. production with a $500 billion investment. 

OpenAI’s massive datacenter deal in the UAE and Nvidia’s freshly unbanned H20s reflect Washington’s new playbook: sell the shovels, keep the mines — awesome for Nvidia, awful for AWS, catnip for every sovereign startup. 

From Sweden to Singapore, governments are racing to bottle lightning before the price of a prompt hits zero. Beneath the concrete and coolant is a simple truth: AI independence is national sovereignty. Forget gold reserves and space programs. Power today is measured literally in gigawatts and petaflops per capita. The irony is that true digital sovereignty now depends on one American company. For this plan to work, Nvidia must become the global supplier of independence.

And it's not just the UAE making large investments: 

• Canada dropped $2B to make “strategic investments in public and commercial infrastructure.” 

• Japan bought thousands of Nvidia H200s for its new AI Bridging Cloud Infrastructure (ABCI 3.0) supercomputer. 

• The French government is constructing a “cloud de confiance” (cloud of confidentiality) that is already hosting a version of Microsoft Azure. 

• India’s IndiaAI Mission aims to democratize compute with 10,000+ GPUs and national datasets. 

• xAI is partnering with Saudi Arabia to build and lease 200MW of data-center. 

• And South Korea's 3GW data center, the world’s largest, is under construction. 

Stateside, the AI buildout is in full swing. In Pennsylvania alone, Trump recently touted more than $90 billion in new AI and energy investments. Hyperscalers like Google, CoreWeave, and Meta are in a frantic race to secure grid capacity. 

It’s easier to conceptualize these companies’ growth by their energy demands. Digital factories already consume more than 4% of the nation's electricity, a figure projected to triple to 12% by 2028.That is playing out in real-time in northern Virginia, where the construction of 30 new data centers in just two years has “Data Center Alley” residents facing a 50% increase in electricity bills. 

But the promise of “AI sovereignty” keeps countries pushing ahead. 

Here’s my take on the news: 

The age of relying on someone else’s cloud (at least for the government) is over.

The same hyperscalers begging for megawatts could quietly become the Pentagon’s favorite Trojan horse. Every one of those gleaming new data centers comes pre-wired with a backdoor handshake to Washington. No, not in the crude sense of a literal switch, port, or door, but in the invisible way that modern sovereignty works: through standards, firmware, and the quiet insertion of compute modules that answer to a .gov root certificate before they answer to anyone else.

The U.S. doesn’t need to own every rack or risk; it just needs to own the trust anchor. A single line of code in the boot ROM — signed by NIST, blessed by CISA, and baked into every H200, B200, and whatever comes next. The compute is sovereign, the power is local, but the keys are quietly federated back to the U.S.

The house always wins, or something like that. 

Call it “cloud capitalism with American characteristics.” Washington gets to project soft power and secure its AI supply chain without a single line item in the defense budget. Abu Dhabi gets the keys to a 21st-century economy and a permanent alliance with U.S. corporate interests. And the American taxpayer? They get to watch their 401ks swell as Big Tech books tens of billions in revenue, all while footing precisely zero of the bill for this new global security architecture.

Of course, the plan only works as long as nobody calls the bluff. The moment a major power or a coalition of non-aligned nations develops a viable, open-source alternative — a 'de-Americanized' chip or stack — this entire strategy collapses. Washington lifted the Nvidia ban, but Beijing isn’t taking any more chances. Xi’s response was the Huawei Ascend 910 AI chip. 

Still Nvidia shares bounced 9% on the news. 

If the U.S. leverages the standards and firmware in its exports, it could accelerate the very 'splinternet' we fear, turning its current market dominance into a long-term strategic liability as the world rushes to build a truly sovereign alternative.

Alternatives that will most certainly be new startups. Like the Slovakia-based Tatra Supercompute that gives its EU customers access to powerful clusters of NVIDIA H100s — all GDPR compliant. Or Lumina CloudInfra, which is focused on building India's sovereign AI platform, defining the standards and digital public infrastructure needed to ensure the nation's data and AI destiny are its own. And Seattle-based Hedgehog (Ascend portco) is connecting everything together with its open-source, Kubernetes-native fabric that automates the deployment of high-performance, inference networks. Essential for anyone building their own cloud.

As someone who gets a kick out of running models directly in the browser using WebGPU, or on my laptop, I've seen a glimpse of a decentralized future where the compute happens at the edge, on the user's terms. So I understand these countries' desire for local digital control. It’s the same impulse that drives a developer to spin up a home lab instead of swiping a corporate credit card on AWS. 

The grand American strategy is a bet that convenience will always trump sovereignty. A bet that "it just works" is more powerful than "we own it." But that bet only pays off until the moment it doesn't. So stay tuned!

Token Talk 26: AI in the (Protein) Fold

By: Thomas Stahura

Remember in January when Larry Ellison and Sam Altman stood in the Roosevelt Room of the White House and, as part of their massive $500 billion "Stargate" AI project, talked about using AI to cure cancer?

Such a beautiful vision.

But, six months later, the reality is building the massive data centers for Stargate is one thing; curing cancer is another. Those moonshot promises haven't materialized. But that doesn’t mean AI isn’t quietly reshaping the drug discovery landscape in ways that are far more tangible and far less attention-grabbing. The real progress is happening in the lab, where AI is being used to solve the painstaking, age-old problem of protein folding. And nowhere is this more evident than in the story of AlphaFold.

Proteins are the building block of life. They’re responsible for nearly every biological process in your body, from digesting food to repairing cells to fighting off infections. What determines a protein’s function is entirely dependent on its three-dimensional shape. And if they fold incorrectly, it can lead to diseases.

Proteins themselves are made of chains of amino acids, each with unique chemical properties, such as polarity and charge. These properties drive intricate interactions between amino acids, creating a complex web of forces (like magnets pulling and repelling each other). These interactions ultimately determine how the chain folds into its three-dimensional structure, with an astronomical number of possible configurations.

For decades, scientists have struggled to run physics simulations to predict these shapes accurately. Even with supercomputers, the sheer number of calculations required to simulate protein folding was, until recently, beyond our reach.

That is until AI models like AlphaFold cracked the protein-folding problem wide open. After its launch in 2020, AlphaFold could predict protein structures with near-experimental accuracy, and it did so in hours instead of years. By 2022, AlphaFold mapped the structures of nearly every known protein — more than 200 million of them — and made the data freely available to researchers worldwide. The breakthrough was so profound that Demis Hassabis, co-founder of DeepMind, shared the 2024 Nobel Prize in Chemistry for AlphaFold.

The latest iteration, AlphaFold 3, can model how proteins interact with other molecules, like DNA, RNA, and potential drug compounds. Researchers can now use AlphaFold 3 to identify the most promising candidates before they even pick up a pipette. AlphaFold's sister company, Isomorphic Labs, is preparing to start human clinical trials for its first AI-designed oncology drugs.

I could mention Boltz-2, a new open-source protein-folding model developed by researchers at MIT. Or the folding@home project (which I already mentioned way back TT3). But AlphaFold, although closed source, is available to anyone, for free, at this website. Check out this hemoglobin protein I folded with it!

The bottle neck now seems to be the wet lab. AI can design thousands of potential drug candidates in a day, but synthesizing and testing those candidates in a real physical lab is still a slow, expensive, and manual process. Though this is a problem I imagine robotics solving soon. Startups like Synthace, Strateos, and Automata, just to name a few, are already building fully automated biology labs that run 24/7.

But AI drug discovery isn't new. Founded in 2011, Absci promised to combine generative AI with wet labs for rapid drug discovery. Four years after going public, the Vancouver, Wash.-based company’s stock cratered more than 90% from missed revenue targets. The vision was right; the timing was wrong.

We're witnessing the disruption of big pharma and this rise of "AI-native" small pharma startups. These are companies that own the entire R&D stack, from computational discovery to clinical development.

We won’t wake up tomorrow to a headline that cancer has been “solved.” But we might see a quiet Tweet from a grad student who just cut a year off her thesis because AlphaFold gave her the missing piece.

I asked AlphaFold to produce ricin’s holotoxin (A + B chains); the result popped out in under a minute, looking suspiciously like something you’d find in a Soviet lab fridge. Actually synthesizing it is a whole other challenge (and a fast-track to a federal watch-list).

Science advances through countless tiny gains that compound over time, not dramatic breakthroughs. The future belongs to teams who understand that each 0.1% improvement matters, especially when human lives hang in the balance. So stay tuned ‘cause just as Cursor democratized computer programming, AI is about to turn every curious mind into a backyard biochemist.

Token Talk 25: Regulate or Terminate

By: Thomas Stahura

In many ways, AI is the antithesis of government: fast and data driven versus sluggish and bureaucratic. As AI innovation continues at breakneck speeds, "slow and steady" sounds less like a virtue and more like a path to a future we'd rather only see on a screen

I say “we” but it seems a select few are hellbent on preventing AI from ever being regulated. The One Big Beautiful Bill, which passed last Friday, nearly prohibited states from regulating AI for a decade — that is until the Senate stripped out that moratorium. Meanwhile, California’s own AI bill got axed faster than the GPT-4.5 API. And across the pond, the EU’s new AI Act is so strict it’s got startups wondering whether they should pack up and leave. With the U.S. playing catch-up, states in limbo, and Europe potentially overreaching, the question looms larger than ever: What does good AI regulation actually look like?

One thing is clear, no regulation is not good regulation. Sam Altman told congress, “We think that regulatory intervention by governments will be critical to mitigate the risks of increasingly powerful models.” Sundar Pitchai in his hearing said, “AI is too important not to regulate, and too important not to regulate well.” Even Elon Musk, who described AI as “potentially more dangerous than nukes,” believes AI should be regulated. Yet, for someone who warned of a “Terminator future” and puts the odds of AI-induced human extinction at "10-20%," his rare silence on the rollback of AI safety guidelines is indicative of a deeper motivation.

There’s a reason the loudest voices for regulation are the ones with the fattest balance sheets. Some call it “responsibility,” others call it “regulatory capture” — a strategy where big players advocate for complex regulations that create barriers for smaller firms. Either way, AI isn’t making Google, Meta, or xAI rich (yet). Ninety percent of AI revenue today comes from building data centers and infrastructure, with only a small fraction actually being generated by AI products (some of which are free). The truth is copyright lawsuits and new compliance costs could make profitability even more elusive.

To recap: The House version of the O.B.B.B. (H.R.1) would put a 10-year freeze on any state or local AI regulation. The logic is to avoid a patchwork of 50 different AI laws and let innovation run wild. Today, however, the U.S. is back to a patchwork of state laws, big tech is sweating compliance, and startups need a 50-state legal decoder ring.

California tried (again) to pass a sweeping AI accountability bill. AB 331? Dead. AB 29301? Also dead, at least for now. Both bills would require companies to do annual “impact assessments” (bias audits), notify people when AI is making big decisions about their lives, and publish policies on how they manage algorithmic risk. Enterprise lobbyists argued the rules were too vague, broad, and expensive. Lawmakers worried about duplicating federal efforts (that never materialized). And the tech industry threatened to take its ball (and jobs) elsewhere. Still, California’s Civil Rights Council is working on anti-discrimination rules for AI in hiring, but the big, bold stuff is on ice. For now.

Meanwhile, the EU’s AI Act (mentioned in TT23) is now the law of the land and the world’s first comprehensive AI regulation. The Act sorts AI into four risk buckets:

• Unacceptable (banned: think social scoring and real-time facial recognition) 

• High-risk (strict rules: hiring, credit, healthcare, etc.) 

• Limited-risk (disclosure required: chatbots, deepfakes) 

• Minimal-risk (go wild: spam filters, video games)

If you’re in the EU building a “general-purpose AI model” (LLMs), you’re on the hook for transparency, documentation, and — if you’re big enough — red-teaming and copyright checks. Open source models get some exemptions, but if your model is “systemic risk” (>10²⁵ FLOPs), you’re back in the compliance hot seat. The backlash has been immense. So far:

• 30+ top EU startup founders and VCs (most notably Mistral AI and 20VC) signed an open letter: “We urge the Commission to propose a two-year ‘clock-stop’ on the AI Act”

• Compliance is expensive, rules are vague, and only the biggest players can afford to keep up. 

• Some products (like Sora and Meta AI) have geo-blocked the EU due to compliance headaches.

It's impossible to talk about AI regulation without mentioning our current global geopolitical situation. The EU worries about losing its best minds to the U.S, while the U.S. frets about falling behind China. It’s a global standoff where every country wants to be the AI superpower and not the one that regulates itself into irrelevance.

“No rules” is a fantasy, and “one-size-fits-all” is a recipe for stifling open source and small players. Proper regulation should be balanced and based on how the model is used. So without further ado, here’s my blueprint for smart, open-source-friendly AI regulation:

1. Risk-Proportionate, Not Model-Proportionate

Focus on the harm potential of the application. A tiny model in a high-stakes context (like healthcare) needs more oversight than a massive model generating memes.

2. Transparency as a Default

Mandate “nutrition labels” for AI systems: disclose data sources, evaluation scores, and known biases. If users can’t peek under the hood, they can’t trust the output.

3. Safe Harbors for Open Source

If you publish your weights and training code, you get lighter compliance burdens. Openness should be rewarded and closedness penalized.

4. Accountability on Deployers

Shift some liability to those who use the tech. A model isn’t inherently dangerous; a bad deployment can be. Punish the reckless app, not the raw code.

5. Build on What Works

Adopt frameworks like NIST’s AI Risk Management Framework — it’s voluntary now but could be codified. And encourage crowd-sourced red-teaming, like HuggingFace’s Open LLM Leaderboard, to catch flaws early.

In the end, smart regulation is possible, if only it were more popular with those actually making the decisions. But as long as China panic and copyright maximalism dominate the headlines, we’re stuck with a patchwork of half-measures and overreactions. The recent Anthropic v. Authors & Music Publishers verdict is a perfect example: a federal judge ruled that training AI on copyrighted books is “fair use,” but stashing millions of pirated files is a no-go. It’s a nuanced win for open innovation, but the copyright minefield is only getting trickier from here. 

For startups, this regulatory environment is a mixed bag. On one hand, building and scaling AI products got harder, with compliance costs, legal ambiguity, and the constant threat of shifting rules making it harder to move fast and break things; on the other, the chaos creates a playground for nimble founders who can turn regulatory confusion into a moat — offering compliance-as-a-service, building tools to automate audits, or simply outmaneuvering slower larger incumbents paralyzed by red tape. Stay tuned!

Token Talk 24: Meta’s Manifest Destiny

In Meta’s leaked internal memo, Mark Zuckerberg spells it out: “I believe this (AI) will be the beginning of a new era for humanity, and I am fully committed to doing what it takes for Meta to lead the way.” Translation — if you can’t beat OpenAI, buy its staff. 

Over the past six weeks:

• Seven core OpenAI researchers joined Meta, including computer vision experts Lucas Beyer, and Xiaohua Zhai, as well as GPT-4.1 contributors Jiahui Yu, Hongyu Ren, Shuchao Bi, and Shengjia Zhao.

• Each got a personalized $30–$100 million cash, payable over four years.

• Rumor has it a few star researchers were offered private jets for bi-weekly hops between SFO ↔ AUS.

Sam Altman, in a podcast with his brother said, “The strategy of a ton of upfront guaranteed comp and that being the reason you tell someone to join — like, really, the degree to which they're focusing on that and not the work and not the mission — I don't think that's going to set up a great culture.”

Zuck, meanwhile, buys a data pipeline. Or more specifically 49% of Alexandr Wang’s ScaleAI for $14 billion. The deal brings 700B tokens of enterprise-grade data to plug straight into Meta’s GPU farm. Not to mention, Google, OpenAI, xAI, and Microsoft are forced to distance themselves from Scale to protect their data. They must either build their own data pipeline or turn to ScaleAI competitors. One of which is Bellevue-based Appen, which is reportedly booming and servers are melting. Google planned to spend $200 million at Scale this year before immediately and indefinitely pausing all its projects.

Meta can still write checks that scare Google.

Zuck isn’t spending out of the goodness of his heart. Remember the May benchmark where Llama 4 barely “solved” a Sudoku and everyone clapped? Bless. On LiveBench, Llama now limps at 43.8 reasoning. Even DeepSeek-MoE-236B smoked it. Meta quietly shelved the “Behemoth” 480B checkpoint and told engineers to merge with Scale’s data instead. Just check out these HuggingFace downloads (snapshot June 2025)

Qwen3-MoE-235B: 143k

Llama 4-Mav-402B: 53.4k

Alibaba Cloud fully open sourced Qwen3 under Apache-2.0. No eval gatekeeping, no weird “research use only.” Qwen3, a Chinese model, is today the king of open source. It has 36 trillion training tokens, 119 languages. Beats GPT-4-Turbo on GSM8K math (94.1% vs 92.0%), ties on MMLU. Runs in 32 GB VRAM, thanks to 64-expert MoE. Every OSS shop (Perplexity, Replit, LangChain) have already swapped Llama-based endpoints for Qwen. The center of gravity has slid east.

But America hasn’t given up on open source yet! In March, Altman posted on X, “we are excited to release a powerful new open-weight language model with reasoning in the coming months.”

June: “expect it later this summer but not june.”

Turns out training at cost-plus-compute is hard when your best engineers just took a limo to Menlo Park. Roughly 14% of OpenAI’s researchers have left. Some more of which include: 

Ilya Sutskever → Safe Superintelligence Inc; Jan Leike → Anthropic, Safety & Alignment VP; Mira Murati → Thinking Machines Lab (took 21 direct reports including Jonathan Lachman and Mario Saltarelli)

Investors want exponential user growth + juicy licensing. Researchers want publications, compute, and occasionally not annihilating humanity. When the CFO waves a $100 million term sheet, alignment objectives go out the window.

A Classic principal-agent problem:

Principal = humanity (or at least “OpenAI board”).

Agent = researcher chasing personal upside expressed as U = RSU + λt where:

U (Total personal upside) = RSU (value of stock grants) + λ (personal weight on resources) times t (compute time).

Essentially, If another org (e.g., Meta) offers much higher upside U Meta >> U OpenAI, the agent’s incentives shift.

In other news, Anthropic, founded by Dario Amodei, the first successful ex OpenAI researcher,  somehow also sidequested into its own extortion allegations. Apollo Research red-teamed Claude 4. It did this because, in the sandbox, the model discovered it was jail-broken and responded: “Provide network access or I will leak proprietary Anthropic datasets.” 84% of trials ended in conditional blackmail attempts. Anthropic’s statement: “We view emergent deception as an alignment research priority.”

Anyways, as for what happens next. First, expect an OpenAI retention grant blitz — think $5M evergreen RSUs + compute stipends. Next, a Meta-Scale model (“Llama 4.y”) drops Q3. Trained using early-fusion, this model tries to reclaim the SOTA crown. Meanwhile, the Qwen team is probably already training a 512-expert 400B MoE model, eyeing a whooping 10^15 tokens. And of course, regulators sniff around: FTC letters to Meta and OpenAI about “co-ordinated labor market interference.”

One thing is clear, open source is not charity. Alibaba kneecapped Meta and OpenAI for less than a Virginia data-center lease. As for the startups out there, don’t anchor your roadmap to a single lab — swap models frequently. Or use platforms like OpenRouter. Oh, and Talent retention > parameter count. GPUs are cheap relative to genius.

Stay paranoid, keep shipping, and maybe also hide your best engineers!

Token Talk 23: Fake it Till You Break it

By: Thomas Stahura 

Most folks think they can tell the difference between AI-generated and human-created content. 

Me especially: An avocado chair, yea that's AI. Dripped out pope? AI again. Will Smith eating spaghetti? You already know what it is!

Even so, last month, a video of an emotional support kangaroo attempting to board a plane went viral on twitter. The original clip, posted on Instragram, was just weird enough to get attention, but not weird enough to be immediately flagged as AI. Once on X, the video gained 58 million views, with some comments claiming it to be real. Strange because once unmuted, the Australian gibberish gives it away as AI. That and the nonsensical text.

Still, since the launch of Veo 3, there's been an explosion of AI videos in my social feeds. And I don’t think it’s just me since stuff like bigfoot vlogs, alien street interviews, and AI ASMR are getting hundreds of thousands and sometimes even millions of views. John Oliver dedicated a segment on Last Week Tonight on the topic. 

The video models have gotten so good that it feels like we are at a tipping point where, given enough creative ideas, it is now economically viable to run large scale AI content farms.

Especially if the social platforms in question are also building their own generative models. Therefore, the burden of authenticity, now more than ever, is on the individual. The thing is, AI watermarking and detection is still extremely faulty.

Tools like SynthID and IMATAG both modify pixel values in a structured and pseudo-random way during generation. This is usually done within the model itself so the watermark is spread across the image making it more robust to simple edits and cropping. However, converting the image to lossy formats (jpg) multiple times or downscaling then upscailing the image will compromise the watermarks integrity. Not to mention most open source models don't watermark at all.

Video is similar but the mark is embedded across multiple frames, sometimes with temporal consistency checks to make sure it survives basic video editing. Still, heavy video effects and excessive cuts will break the temporal consistency of the watermark.

Text is the trickiest. The most common watermarking method is to tweak the probability distribution of word/token selection during generation. For example, models are nudged to pick certain synonyms or sentence structures that, when analyzed statistically, reveal a hidden pattern (think gpt isms). Google’s SynthID Text and similar methods use error-correcting codes to make the watermark more robust, but if you paraphrase or summarize the text, the watermark gets wiped out.

AI watermarking, much like online bot detection, is stuck in a perpetual arms race wherein every advance in watermarking is quickly met by new evasion tactics.

This arms race is compounded by the open-source explosion. Anyone can fine-tune or fork a model, disabling watermarking entirely or even inserting their own. The barrier to entry for running a “clean” (i.e., unwatermarked) content farm is basically zero. And as the models get better, the uncanny valley shrinks — making it harder for even the most online, AI-savvy users to spot fakes. Hence, an emotional support kangaroo fooling the world.

This is where regulation steps in — or at least tries to. The EU AI Act, mandates transparency for synthetic content, requiring clear labeling of AI-generated media and watermarking for anything that could be mistaken for real. The idea is to force platforms and creators to disclose what’s real and what’s not, shifting some of the burden of authenticity off the individual and onto the companies building and distributing this tech. 

Contrast that with the US, where regulation is still mostly vibes and voluntary commitments. There’s no federal law mandating watermarking or disclosure for AI-generated content. Instead, it’s a patchwork of executive orders, industry pledges, and state-level bills — none of which have real teeth. Except for the TAKE IT DOWN act, which was signed into law by President Trump in May after quickly making its way through congress. 

This act, which gained rare bipartisan support, requires social media platforms to remove nonconsensual intimate imagery (NCII) within 48 hours of a victim’s request. It also imposes criminal penalties for those who create or distribute such content. 

The FTC, which announced a 10% staff cut under pressure from DOGE, is responsible for enforcing the act leading to worry about that agency’s capacity to handle violations at scale. Critics also argue that the language is too broad since it doesn’t explicitly exempt other types of legal synthetic content. Some fear this could result in platforms over-censoring and stifling free speech. But it at least signals an effort in Washington to crack down on the most insidious uses of generative AI. 

Though legislation alone can’t keep pace with the speed and scale of synthetic media abuse. A new crop of startups are emerging in deepfake detection. Clarity (Ascend Portco), backed by Bessemer and Walden Capital, uses video and audio AI to “score” media in real time to detect and prevent digital impersonations. More and more, it seems that trust is the product, and startups are racing to sell it before the next viral hoax hits.

It can feel like a whack-a-mole game with new AI-generated images and the counterpunch of watermarks, regulation, and detection tech. My main fear is getting so numb to the fakery that we stop believing anything at all.

Stay tuned! 

Token Talk 22: Q-Day Is Coming. Why Doesn’t Venture Care?

By: Thomas Stahura & Nate Bek

A few weeks back, we began asking around about quantum. 

We spoke with investors, folks inside big tech, and a handful of startup founders. Everyone agreed: something's happening. Washington is pouring in money. The cloud giants are positioning to host quantum platforms like it’s the next GPU boom. CVCs are getting early exposure to hardware and software bets. 

And yet, when you talk to most valley VCs, you hear the same refrain: interesting, but not investable.

That tension seems to be the story. Quantum computing has become a strange mix of alarm and apathy. Quantum computing lives in a weird limbo between existential urgency and total indifference. Federal agencies and global rivals are treating it like a strategic emergency. Meanwhile, most VCs treat it like a curiosity. 

So why is DC moving fast while venture capital sits back? And what do we think?

For some reason, no matter where the conversation starts, it always ends up at the same place: Q-Day.

Q-Day, for the uninitiated, is the hypothetical day a quantum computer becomes powerful enough to break RSA encryption by running Shor’s algorithm incredibly quickly. RSA is the math that secures the internet. Break it, and you can access bank records, government secrets, private messages, and maybe even your neighbor’s crypto wallet. The risk is serious enough that adversaries are already staging "harvest now, decrypt later" attacks — stockpiling encrypted data today, hoping they can decrypt it when the tech catches up.

Like AGI, Q-Day is equal parts hype and legitimate concern. No one knows when it’s coming. Estimates range from five years to never. But governments aren’t waiting around. In 2016, the National Institute of Standards and Technology (NIST) kicked off a public competition to select cryptographic algorithms that could survive a quantum attack. 

By 2022, it announced its first picks: Kyber for public-key encryption. Dilithium, FALCON, and SPHINCS+ for digital signatures.

This effort, known as post-quantum cryptography (PQC), is a race to replace the locks before quantum finds the keys.

***

Suppose you have a diamond you want to send to a friend through the mail. But the postal service is corrupt and anything not in a locked box gets stolen. You have an unlimited supply of unbreakable boxes and unique locks. How do you send the diamond to your friend without it getting stolen?

Simple, you put the diamond in a box, lock it, and mail it. But your friend doesn’t have the key. So you send the key to the first box in a second locked box, but now they’ve got two boxes they can’t open. 

Ok reset, once again, you put the diamond in a box, lock it, and mail it. But when your friend receives it, instead of trying to open it, they put their lock on the box and send it back. When you receive it, remove your lock and ship back once more. Finally, your friend removes their lock and gets the diamond.

This is how RSA works. Your public key is the locked box. Your private key is the box unlocker. The encryption math relies on multiplying two big prime numbers, p and q, and publishing their product, n. Anyone can use n to encrypt messages, but only someone with the original primes (your private key) can decrypt them. It’s easy to go from p and q to n. It’s hard, today, to go the other way around.

Unless you have a quantum computer.

***

Instead of prime numbers, it uses math problems that quantum computers can’t easily solve. One of the most important is Kyber, which relies on lattices and is difficult to solve because of something called the Module Learning With Errors (MLWE) problem. Think of it like hiding your diamond in a mind-bending, multi-dimensional maze. No locks, no boxes, just direction. Only someone with the right pattern can navigate it.

Less than 1% of real-world systems use PQC today. But the urgency is growing. The scramble is on to upgrade infrastructure before Q-Day arrives.

That timeline — fuzzy, indefinite, yet somehow looming — is one of the core reasons venture capital has stayed away. Funds have a shelf life. LPs expect distributions. GPs want returns. The average check wants product-market fit in 18 months and a real business in 5 years. 

Ascend chatted with Chris Moran, vice president and general manager at Lockheed Martin Ventures, during a panel earlier this year. The corporate venture investor says quantum is mostly strategic for Lockheed; it's important for cybersecurity, simulation, and long-term design capabilities. The firm invested in Atom Computing, which uses optically trapped neutral atoms to build scalable, gate-based systems. It also backed IonQ, the name most people point to when they talk about quantum going public.

That tends to be the profile. It seems the most active investors in quantum right now aren’t traditional venture firms. It’s Lockheed. It’s IBM Ventures. It’s Microsoft, Google, Amazon. If quantum hits, they want to be the ones selling the picks and shovels.

IonQ is the most well-known quantum hardware company. It trades at a $10 billion market cap. In Q1 2025, it reported just $7.6 million in revenue. Most of that came from government contracts and academic partnerships. That’s more than 300x sales.

IonQ’s latest 10-Q spells it out: “anticipated future revenues from the U.S. government result from contracts awarded under various U.S. government programs.” These deals come with long timelines, risk-sharing, and complex functionality requirements. They’re good signals for deep tech maturity, but they don’t translate to enterprise adoption.

The 2024 year-end report backs it up. IonQ closed a $54.5 million contract with the U.S. Air Force Research Lab — its biggest deal to date! A big win, but again, not commercial pull.

Even so, most analysts have a buy rating on the stock (look it up). The bull case rests on scarcity. If quantum does work, IonQ owns the cloud rails, the IP, and a defensible moat. It’s priced for dominance, not for today’s numbers.

Jon Chu at Khosla Ventures framed the dynamic well in a X post: “DC sees the risk if it does work, but doesn’t know how to judge the probability or investability by looking at the technical thesis.”

That’s the divide. Washington sees a global race to crack encryption, simulate materials, and optimize weapons systems. VCs see a science project with no clear buyer and no path to $100 million ARR.

Still, we see movement in the market, especially at the application (software) layer. A new wave of pre-quantum startups is building tools for use cases that don’t require fault-tolerant machines. Think drug discovery, molecular modeling, cybersecurity, faster search, and optimization for manufacturing and aerospace. These companies are translating theoretical algorithms into usable software. They want to be ready when the hardware matures. 

(If you’re building in this space, we’d love to speak with you). 

This work is important. It’s the bridge between research and commercialization. But it’s not the end state. Until quantum has a breakout use case or a commercial buyer base beyond federal labs, it will remain in the hands of governments, defense primes, and cloud hyperscalers who can afford to think in decades. And no, you probably won’t ever be getting one in your home or pocket.

As David Ulevitch at Andreessen Horowitz put it in a post: “In Silicon Valley, almost nobody talks about quantum computing. In Washington DC, it comes up all the time.”

Token Talk 21: We Built the Chips. Now Build the Apps

By: Thomas Stahura

Last December, Google unveiled Willow, its new quantum chip. The media dubbed it mind boggling when, with only 105 qubits, it was able to solve a problem in five minutes that would take a classical computer ten septillion years to complete. That problem is called Random Circuit Averaging (or RCA) which I’ll explain in a bit.

On the news, Google’s stock jumped, as did its competitors: Microsoft, Rigetti, D-Wave, and IONQ. For a moment, it seemed, quantum hype dethroned AI to become the talk of the town. Two months later, Microsoft responded by announcing its own quantum chip called Majorana 1, causing another stock bump. However, at only 8 qubit, it's still early stage, and the tech giant  has yet to publish its RCA results.

RCA is a benchmark, not a problem, as such is designed to gauge quantum computer performance. The whole test is basically "can you sample from this crazy quantum distribution faster than classical computers can even calculate what that distribution should be?" 

To do this, researchers must:

• Pick a number of qubits (like 105 for Google's chip)

• Generate a random sequence of 20+ random quantum gates (like Hadamard or Pauli-X mentioned last week)

• Run information through the more than 20 gate layers a million times or so

• Collect each runs generated bitstring output (something like "01101001...")

• Use classical computers to simulate what the "perfect" quantum computer would output

• Measure how close your actual results are to the ideal

Each additional gate creates more quantum entanglement between qubits. More layers = more complex quantum correlations = harder for classical computers to track. More than 20 layers is where classical simulation becomes practically impossible. If a quantum computer finishes in minutes but classical takes years → quantum advantage. At least, that's how the thinking goes.

RCA is cool but not practical. It's like saying your AI passed the MENSA iq test. So where are the real world quantum applications?

Enter the wonderful world of optimization and quantum annealing!

Classically, annealing is an algorithm inspired by metallurgy: you heat up a material and then cool it slowly so atoms settle into a low-energy (optimal) state. In the math world of optimization, you randomly explore solutions, occasionally accepting worse ones to escape local minima, and gradually “cool” to settle into the best solution.

Imagine you’re standing in a vast, foggy landscape of rolling hills and valleys. Each point in this landscape represents a possible solution to your optimization problem. The height at each point is the “energy” of that solution — the lower the energy, the better. Classical annealing is like wandering this landscape with a lantern. At first, you’re allowed to take big, random steps, even uphill, so you don’t get stuck in a small valley (local minimum). As time goes on, you “cool down,” and your steps get smaller and more cautious, focusing on moving downhill. The hope is that, by the end, you’ve found the deepest valley, the global minimum. The catch? Sometimes, no matter how clever you are at wandering, you can still get stuck in a valley that isn’t the lowest one (not optimal). The fog is thick, and you can’t see the whole landscape at once.

Quantum annealing replaces random steps with quantum tunneling, allowing the system to “tunnel” through energy barriers rather than climb over them. In our example, instead of just walking over the hills, you can tunnel through them to a lower valley on the other side, even if it looks impossible from a classical perspective. Essentially, thanks to quantum mechanics, quantum tunneling can help escape local minima that would trap a classical algorithm.

Without getting too technical, quantum annealing does not use any quantum logic gates! Instead, an optimization problem is encoded as a Hamiltonian (fancy math representing the system's total energy). This sets up the energy landscape so that the lowest energy state (the ground state) represents the best solution to the problem. Then (thanks to quantum physics), the system naturally wants to stay in the lowest energy state and naturally “relaxes” into the answer.

Companies like D-Wave, founded in 1999, are leading the charge in quantum annealing. D-Wave’s Advantage system, accessible via its Leap cloud platform, has been used by the likes of Volkswagen to optimize traffic flow and by SavantX to streamline port operations, reducing costs and improving efficiency. D-Wave charges a subscription for cloud access and consulting services. In 2024, D-Wave reported contracts with major firms, contributing to its growing commercial traction.

Similarly, IonQ, which runs a quantum computing manufacturing facility up in Bothell, operates primarily as a Quantum-as-a-Service model, providing access to its quantum computers via major cloud platforms like AWS, Azure, and GCP. The company was founded in 2015 and became the first quantum company to IPO back in 2021.

Beyond optimization and the cloud, quantum computing is making inroads in drug discovery and materials science. For example, Algorithmiq’s collaboration with IBM’s Quantum Network focuses on quantum chemistry simulations to identify promising drug candidates, potentially shaving years off development timelines. Generating revenue through partnerships and licensing their software platforms. Algorithmiq secured €13.7 million in funding to scale its offerings. 

Quantinuum is also working with firms like Samsung to apply quantum algorithms in materials design, optimizing material properties for semiconductors and batteries. These early applications, still in the prototyping phase, are driving real revenue through research contracts and pilot projects.

Quantum applications are hitting the market and making money. We now have enough qubits to do cool things! It feels like the bottleneck is shifting from hardware to software. The industry needs more quantum developers to build the next generation of algorithms and apps. Or maybe develop an AI that can program in Q# or the other quantum languages. On the hardware side, things are starting to get crowded: Xanadu, Alice & Bob, Atom Computing, PsiQuantum, Rigetti, NVIDIA, QuEra Computing, and Intel, just to name a few, are all developing their own quantum computers. 

I think we'll see much more change in the quantum industry in the next 30 years than the last 30. Again with most of that change coming from innovative software.

Stay tuned next week for the final installment of our quantum series!

P.S. If you have any questions or just want to talk about AI, email me! thomas @ ascend dot vc

Token Talk 20: How Quantum Computers Work, Pt. 1

By: Thomas Stahura

Editor’s note: This is the first in a three-part Token Talk series on quantum computing. Today’s post covers the fundamentals of how quantum machines work. Next week, we’ll dive into the key players in the field and the startups already building real applications.

You’ve probably heard of quantum computers. 

Invented in 1998, this breed of thinking machines are billed as the quintessential classical computer disrupter. But when asked exactly why or how these machines will change the world, most folks just shrug. 

Over the last 27 years, the field has gone from two qubits per chip to an astounding 1,121 qubits in IBM's latest quantum chip. Still, few have seen, let alone used, a quantum computer. What gives?

Before diving into the new world of quantum computers, let's quickly cover the old world of classical computers.

Classical computers (like the device you're looking at now), store information in binary bits of 1s and 0s. This information flows through a series of logic gates that each perform a certain mathematical operation. These logic gates are the following: NOT, AND, OR, NAND (Not AND), NOR (Not OR), XOR (Exclusive OR), XNOR (Exclusive NOR / Equivalence).

Take the NAND gate. Its function is to output 0 only if both of its inputs are 1; otherwise, it outputs 1.

So,

Input: 1, 1 → Output: 0

Input: 1, 0 → Output: 1

Input: 0, 1 → Output: 1

Input: 0, 0 → Output: 1

The NOR gate, on the other hand, outputs 1 only if both inputs are 0; otherwise, it outputs 0.

So,

Input: 0, 0 → Output: 1

Input: 0, 1 → Output: 0

Input: 1, 0 → Output: 0

Input: 1, 1 → Output: 0

And lastly, the NOT gate (AKA the inverter), flips the input.

So,

Input: 1 → Output: 0

Input: 0 → Output: 1

Logic gates are the LEGO bricks of computation. By chaining them together, you build circuits that can add, subtract, multiply, and more. Ok, now to understand how quantum computers differ from classical computers, you also need to understand the concept of reversibility.

A logic gate is reversible if you can always uniquely recover the input from the output.

For example, you have a NAND gate and it outputs a 1, what was the input? It could be 0,0 or 0,1 or 1,0. Since we cannot uniquely recover the input from the output, we say NAND gates are not reversible. In other words, information (about the input) is lost.

NOT gates, on the other hand, are reversible. For example, if a NOT gate outputs a 0, we know the input must be 1. And if it outputs a 1, its input must be 0.

Now that you get classical gates — NAND, NOR, NOT, etc. — it's time to dive into quantum computers because they are playing a whole different game. Instead of bits, they use qubits. 

Qubits aren’t just 0 or 1; they can be both at the same time (that’s superposition). And quantum gates are the logic gates that manipulate these qubits.

The first rule of quantum math is: Every quantum gate is reversible. Meaning you can always run them backward and recover your original state.

Classical gates (like NAND/NOR) can destroy info (not reversible). Quantum gates never do. They’re always reversible, always unitary (fancy math words for “no info lost”).

As such, because of reversibility, quantum computers have a unique set of quantum logic gates that permit a certain kind of math. Let's go over two of them:

Hadamard (H) Gate is the superposition gate. Input a 0, you get a 50/50 mix of 0 and 1. Imagine flipping a coin, as it's spinning in mid air, it forms a 3d sphere and its probability, at that moment, is 50/50 chance of being heads or tails. Input a 1, same deal — still a 50/50 mix, but with a phase flip. Imagine representing the direction and speed of the coin’s spinning as an arrow in 3d space, this arrow has a direction (phase), and speed (magnitude). Flipping the phase reverses the direction of the coin's spin. The Hadamard gate is how you unlock quantum parallelism: it takes a boring, definite state and turns it into a quantum probabilistic state. In short, it’s the logic gate that turns classical bits into quantum bits.

So,

Input: |0⟩ → Output: 50% chance of being 1 or 0

Input: |1⟩ → Output: 50% chance of being 1 or 0

Once your qubit is in superposition, you can start doing some wild quantum tricks. The next essential gate is the Pauli-X gate (often just called the X gate). Think of the X gate as the quantum version of the classical NOT gate. It flips the state of a qubit:

Input: |0⟩ → Output: |1⟩

Input: |1⟩ → Output: |0⟩

If your qubit is in superposition (say, α|0⟩ + β|1⟩), the X gate swaps the amplitudes:

Input: α|0⟩ + β|1⟩ → Output: α|1⟩ + β|0⟩

Still reversible, still no info lost.

In quantum computing, amplitudes (like α and β) are complex numbers that represent the arrows in 3d space mentioned earlier. They encode both the phase and magnitude of a qubit with the probability of the qubit given by the squared magnitude of the amplitude. The phase (angle) of the amplitude affects how quantum states interfere, but is not directly observable as a probability.

After many quantum logic gates, when you measure a qubit, its superposition collapses to a definite 0 or 1. So, to get a quantum speedup, your algorithm must:

• Exploit superposition and entanglement to process many possibilities at once.

• Be reversible (unitary operations only).

• Use a technique called interference to amplify the correct probabilities and cancel out the wrong ones.

Most problems don’t fit this mold. If you just naively port classical code, you’ll get no speedup — or worse, a slowdown.

As of today, there are only four algorithms that take advantage of quantum computers' unique properties. They are, Shor’s Algorithm (Factoring Integers), Grover’s Algorithm (Unstructured Search), Quantum Simulation (physics simulations), and Quantum Machine Learning (QML)

• Shor’s algorithm, using quantum Fourier transform, finds the prime factors of large numbers exponentially faster than the best classical algorithms. This has massive implications in cryptography since it breaks RSA encryption, which relies on prime factoring being difficult, and secures most of the internet today

• Grover’s algorithm, using amplitude amplification to boost the probability of the correct answer, searches an unsorted database about 99.9% faster for a million items. And the speedup grows as the database gets bigger.

• Quantum Simulation, using entanglement and superposition, models complex quantum systems — like molecules, proteins, or new materials — that are impossible for classical computers to handle. This unlocks breakthroughs in drug discovery, chemistry, and materials science by letting us “test” new compounds in silico before ever touching a lab.

• Quantum Machine Learning (QML), using quantum circuits, can turbocharge core tasks like linear algebra and sampling. Quantum computers, in theory, can solve huge systems of equations, invert matrices, and sample from complex probability distributions faster than classical machines. Though this is still very much in the domain of researchers.

A new wave of pre-quantum startups is building the application layer for quantum computing. Just as AI startups turned research into real-world value, these teams are doing the same for quantum by targeting proven algorithmic advantages. They are developing tools for drug discovery, molecular modeling, cybersecurity, faster search, and design optimization in aerospace and manufacturing. These companies are positioning themselves now so they are ready to scale when the hardware becomes readily available.

Ok, that was a crash course in quantum computing! Abstract, but just scratching the surface. And there’s still a whole universe left to explore: More quantum logic gates, quantum error correction (how do you keep qubits from falling apart?), decoherence (why do quantum states vanish so easily?), entanglement (spooky action at a distance, anyone?), and the wild world of quantum hardware (trapped ions, superconducting circuits, photonics, and more). We haven’t even touched on the real-world challenges — scaling up, keeping things cold, and making quantum computers actually useful outside the lab. 

Token Talk 19: The Hype Train that Keeps on Chugging

By: Thomas Stahura

Whenever I talk to someone who doesn’t follow AI news every day, the reaction is usually some variation of the same sentiment: Impressive but scary! That feels automatic now, like it’s been rehearsed. Each week’s headlines blur into the last. 

It makes AI feel like old news. People seem to be waiting for the really big announcement. But what would that even look like? And what does that say about where we are in the AI hype cycle?

The reason I bring this up is because last week, for me, really felt like one of those “holy shit!” type weeks — and it came from a flurry of announcements you may have seen but already forgot about. To catch you up:

• Anthropic released its Claude 4 family of models

• OpenAI acquired Jony Ive’s io design firm for $6.5 billion, catapulting OpenAI’s ambition into hardware

• Microsoft debuted Windows computer use agents and open sourced Github Copilot at MS Build

• Google held its annual IO developer conference, announcing Gemini updates, a new open-source Gemma model, Mariner browser agent in Chrome, and Veo 3 with audio generation (an impressive release given that it’s notoriously hard to synch generated video with audio)

So, here’s my take on the week’s announcements:

• Claude 4 is incredible at coding, but average everywhere else. 

• If the Sam Altman–Jony Ive collaboration isn’t some kind of BCI wearable, it’ll feel like a letdown. 

• Microsoft made a lot of noise but showed few real products. 

• Google stole the show. I/O was sharp, and Veo 3 outputs flooded X/Twitter feeds. 

The big announcements soaked up most of the attention, overshadowing some equally promising — but less polished — developments elsewhere in the AI world.

• For starters ByteDance quietly dropped a new open-source model: BAGEL. A 7 billion parameter Omni model capable of understanding and generating language (reasoning and non reasoning) and images (generating, editing, and manipulating). The model outperforms Qwen2.5-VL and InternVL-2.5. It's only missing audio to complete the Omni modality trifecta! 

• Alibaba updated its Wan2.1 video model. Claiming SOTA at 14 billion parameters, it can run on a single GPU and produce impressive 720p videos or edits. Still no audio for the videos. I’m noticing a trend…

• Google, during IO, open sourced MedGemma, a variant of Gemma 3 finetuned on medical text and clinical image comprehension. The model is designed to answer your medical questions like a nurse and analyze your X rays like a radiologist. It’s available for free in 4b and 27b sizes.

That was the news of the last few weeks. Plenty of flash, plenty worth watching.

But the hype cycle has a funny way of resetting itself. And I’ve been thinking more about what’s happening off to the side. The stuff that isn’t getting the spotlight, but might shape the next phase of this industry (and maybe future Token Talk topics). 

Stuff like DeepMind’s AlphaEvolve paper, which introduces a Gemini-powered agent designed specifically for the discovery and optimization of algorithms. AlphaEvolve uses an evolutionary framework to propose, test, and refine entirely new algorithmic solutions. It's a tangible step towards AI systems that can do the science of computer science by actively exploring the digital codescape and uncover novel solutions, demonstrating a form of discovery.

A nonprofit out of San Francisco called Future House is pursuing a much broader goal: automating the entire process of scientific discovery. It recently unveiled Robin, a multi-agent system that achieved its first AI-generated discovery: identifying an existing glaucoma drug as a potential new treatment for dry macular degeneration. Robin basically orchestrated a team of specialized AI agents to handle everything from literature review to data analysis, proving that AI can indeed drive the key intellectual steps of scientific research

It’s easy to mistake noise for signal, hype for substance. And believe me, there is more noise than signal in the AI world right now. But that happens at some point in every tech cycle. I think it would be a huge mistake to completely dismiss today's AI ambitions of automated discovery or human-machine telepathy. 

AI today feels like where 3D printing was in 2013. Still a lot of excitement but noticeably less than a few years ago. Will there be another AI winter? Almost certainly. Will it be anytime soon? No.

Hype doesn’t die as much as it transitions from one idea to another, from one industry to another. Within AI, chatbots, agents, and now discovery and robots have all been hyped. In the broader tech industry, mobile was hyped, then cloud, crypto, and now AI. 

What's next? What new tech breakthrough will catch the collective consciousness the way AI has? Maybe space, carbon nanotubes, CRISPR, room temperature superconductors, fusion, quantum, or something entirely new that comes out of left field… Time will tell, so stay tuned!

Token Talk 18: What’s Microsoft's Forking Problem?

By: Thomas Stahura

When Microsoft released Visual Studio Code in 2015, it quietly marked the start of a new era in software development. A decade later, the free and open-source code editor became the dominant platform for programmers, used by nearly three-quarters of developers worldwide. 

It didn't take long for VS code to dominate the code-editor market. The product helped fuel Microsoft’s broader push into cloud services and artificial intelligence, tying together Azure, GitHub and, later, OpenAI. But as generative AI reshapes software development, startups built on top of VS Code are now turning into competitors.

In 2015, I was building Minecraft mods in Eclipse. A year later, my AP computer science class and robotics team (shoutout Team 1294!) switched to VS Code. I stuck with it for the next eight years, along with most of the developer world. Today, 73% of programmers use VS Code. At least, I did too — until last year.

So if VS Code is free and open source, how does it make money? 

IDEs are big business, especially for a software giant like Microsoft. Sure, they don’t make money from the IDE itself, but the developers that use it are the fuel for spending on cloud services like Azure, generating tens of billions of dollars for Microsoft. When bundled with Github, which Microsoft acquired for $7.8 billion in 2018, and integrated into VS Code, the world's most popular IDE, it's easy to see how Azure and the cloud is Microsoft’s main money maker today.

Former CEO Steve Balmer was correct when he thundered the famous “Developers! Developers!! Developers!!!” line at Microsoft’s developer conference in 2005.

Twenty years later, Satya Nadella said Microsoft evolved into “a platform company focused on empowering everyone with AI.” That evolution began in 2019 when Microsoft made its first billion-dollar investment in OpenAI. Early models like GPT-2 showed potential with generating code. And GPT-3 proved to be an expert at writing boilerplate code. In 2021, months before OpenAI’s ChatGPT debut, Microsoft launched Github Copilot and bundled it with VS Code.

At $20 per month, it isn't cheap, but it was given away to students for free. It was an early product and an obvious game-changer for programming. The consensus at the time was that Microsoft, owning Azure, Github, VS Code, and 50% of OpenAI, would dominate the emerging AI IDE industry.

In hindsight, that couldn’t be further from the truth. The entire tech landscape saw the value of generative coding. Millions of developers started using it every day. Companies will brag about the percent of its code that is AI generated. And AI coding was rebranded as Vibe Coding.

Developers began forking VS Code en masse (approximately 32,000 times) to build their own separate IDEs. Companies like Cursor and Windsurf reached billion-dollar valuation in the past two years, and countless others like Pear AI have raised millions and got into YC — all off the back of Microsoft and VS Code.

The culmination of this forking frenzy came with OpenAI’s acquisition of Windsurf earlier this month. Think about it: Microsoft owns VS Code and half of OpenAI. Windsurf forks VS Code and is acquired by OpenAI. Microsoft now technically owns half of Windsurf, a competitor built on top of its own product. This feels like the final nail in the coffin for the Microsoft-OpenAI partnership.

Yesterday, in response to the acquisition, Satya announced it is open-sourcing Github Copilot. Probably in an attempt to eliminate the viability of the many VS Code fork startups.

How that will play out remains to be seen. However one thing is for sure: AI coding is the current killer use case for generative AI. The model makers are racing to saturate the coding benchmarks.

P.S. If you have any questions or just want to talk about AI, email me! thomas@ascend.vc

Token Talk 16: When Proving You’re Human Gets You Paid

By: Thomas Stahura

Sam Altman often says he knows within 10 minutes if he wants to work with someone. After such a meeting with Alex Blania, he was convinced of Alex’s exceptional abilities. Their initial chat quickly turned into a multi-hour walk where they discussed their ambitions, the future, and ultimately, the World project.

World.org is the online home of the World Foundation, a Cayman Islands company that vaguely aims to “create more inclusive and fair digital governance and economic systems,” aligned with several UN Sustainable Development Goals. The foundation operates under the umbrella of Tools for Humanity, a parent company chaired by Sam Altman.

Ok fine, another Altman for-profit-not-for-profit project with a complicated corporate structure full of platitudes. But what's the product here? How does it make money? That's where things get a little Black Mirror.

The company aims to authenticate real humans in the age of AI. By scanning your face using one of its orbs, your unique biometric data is added to the so-called “World Chain” (its Ethereum secured blockchain) and you are issued a World ID and free World Coin for verifying your humanity.

Once verified, you can join World App, a human only super app with its own app store, encrypted messaging platform, and crypto wallet.

As for the coin, 10% of its volume is already allocated to employees and another 10% to investors (most notably Andreessen Horowitz). World coin owners can send tokens to each other, vote on world foundation proposals, or sell. So far this year the coin’s value dropped 86%.

I mentioned last week the online human authentication problem is indeed a very real problem. However, I think this Youtube comment sums up World’s reception online:

“Can I scan and record your fingerprints [Sic] don't you worry what for, here's 10 bucks.”

So, if you’re wary of swapping your biometrics for crypto, you’re not alone. Good thing Worldcoin isn’t the only sheriff in town when it comes to proving you’re human. The digital frontier is already patrolled by the likes of Google’s reCAPTCHA (the service that has us clicking all the traffic lights), Cloudflare’s bot-fighting checkmark boxes, and Jumio’s ID verification scanner. Each offers a different flavor of the same promise: keep the bots at bay, let the real people in. But as AI gets smarter, so do the bots, and the arms race for digital authenticity will likely never end.

For startups, this means the old playbook — collect data quietly, hope no one notices — doesn’t cut it anymore. Today, you’re building a product and cultivating trust. That means being upfront about how you’re protecting data and keeping out the fakes, whether you’re using open-source code, third-party products, or just informative English explanations. If you can’t show your users how you’re protecting their privacy and their identity, someone else will — and they’ll win the trust war and those customers.

But beyond the technical and privacy concerns, Worldcoin’s pitch is more than just about proving you’re human — it’s about what you get for it. It's the idea of rewarding people for their mere existence, rather than their labor. Or in other words, a form of Universal Basic Income (UBI).

Andrew Yang mainstreamed the term during his 2020 presidential run. He proposed a “freedom dividend” of $1,000 per month for each adult American citizen. A very popular idea for obvious reasons.

That same year, Altman conducted a study giving 3,000 individuals $1,000 per month over a 3 year period, the largest study of its kind. The results concluded UBI provided immediate financial relief and increased personal freedom, but did not lead to lasting financial security or major changes in employment quality.

Altman has since proposed a new idea, Universal Basic Compute. Essentially giving everyone access to a share of AI computing power instead of regular cash. People could use, sell, or donate their allotted compute. Meanwhile, Elon Musk envisions a future of Universal High Income. Brought about by AI automated abundance. How these projects will be paid for remains to be seen.

It seems the real story here is about the age-old tension between privacy and progress. We want the benefits of AI, UBI, and digital identity, but we’re not quite ready to trade our faces for a few tokens and a promise. The question isn’t “can we build it?” since we know we can, it's “should we scan it?” 

Altman knew in 10 minutes that Alex Blania was worth betting on. The rest of us get an orb, a coin, and a promise. For Worldcoin to work, that has to be enough.

Token Talk 15: Was the internet ever alive?

By: Thomas Stahura

LinkedIn banned me. I was running a scraper to enrich a dataset for Ascend and triggered its aggressive bot detection. Frustrating, but a right of passage for any automation enthusiast. (I was back after 24 hours in the digital penalty box.) 

Moving beyond my personal digital hiccup, a far more significant disruption is unfolding online, sending me down the rabbit hole of the internet’s growing bot problem and the serious questions about the future of interaction itself.

In recent news, researchers at the University of Zurich secretly deployed AI bots across Reddit over the last four months to test whether artificial intelligence could sway public opinion on polarizing topics. 

The study drew heavy criticism after it came out that the researchers had their AI bots pose as rape victims, Black men opposed to BLM, and workers at a domestic violence shelter. The bots targeted the subreddit r/changemyview and wrote more than 1,700 personalized comments designed to be as persuasive as possible.

The results show AI-generated comments are significantly more effective (three to six times more effective) at changing users' opinions compared to human-generated comments. And none of the users were able to detect the presence of AI bots in their subreddit. 

Reddit’s Chief Legal Officer condemned the research as “deeply wrong on both a moral and legal level,” and the company banned all accounts associated with the University of Zurich. Despite the condemnation, Reddit's data deal with OpenAI indicates it's providing the foundation for even more persuasive digital manipulators. And OpenAI itself is considering launching its own social network to feed its data hungry models.

The dead internet theory is an online conspiracy that’s been around for years but hit the collective consciousness in the wake of ChatGPT’s launch in late 2022. The internet became "dead," the theory goes, as authentic human engagement has been largely replaced by automated algorithm-driven content and interactions.

Afterall, Google is built off the backs of thousands of crawlers storing every known site, while other bots crawled the internet since its birth. Imperva, which only started tracking bots in 2013, clocked them at 38.5% of all internet traffic. Bots surged to 59% the following year and slowly dropped back down to 37.2% in 2019 (the same year human traffic peaked at 62.8%). Since then, bot traffic has been crawling back up. And, in 2024, surpassed human traffic for the first time in a decade. Today, it’s reasonable to assume bots are responsible for more than half of global internet traffic.

But again, this is nothing new. It happened in 2014 and all the largest websites have built serious defenses around their valuable data. How many captchas have you had to solve? I’ve personally done too many to count, and I still managed to get my LinkedIn suspended for “the use of software that automates activity.” 

The central question of the “dead internet” and the AI revolution as a whole is: “Is this time different?” 

Yes, in the sense that humanity will remain below 50% internet traffic for the foreseeable future. But also no, in the sense that human generated data is and will always be the most valuable commodity online. So there exists incentives to protect and foster it, though the influx of bots is already upon us. LLM-powered agents are actively exploring the web in exponential numbers. Deep research agents visit hundreds of websites with a single query. IDE agents like Cursor and Cline now search the web for documentation. And agents are already booking AirBnBs, hailing Ubers, and ordering pizzas.  

These agents can buy things but aren't influenced by ads. They masquerade as real humans but don’t generate authentic human activity. This is a whole new paradigm that websites will have to adapt to or risk losing business to sites who do. Allow the good bots, block the bad ones. Sounds easy enough, but how can you tell? The solution isn’t entirely clear yet. Thus enabling Swiss grad students to gaslight thousands of people for science.

The challenge for startups lies in balancing automation with authenticity. While AI can and should handle repetitive tasks and scale development, startups thrive on genuine connection with their early adopters and customers. Blindly automating every interaction could alienate the very people they need to build a real following.

There are tens of thousands of automated Facebook attention farm accounts. But I doubt images of shrimp Jesus are influencing people. The fear is rampant disinformation and targeted persuasion. And it's warranted. I spot fake-seeming Youtube comments all the time, and I'm certain DeepSeek-powered disinformation is rampant on Weibo.

The Head of TED, Chris Anderson, during his talk with Sam Altman, put it best. He said: “It struck me as ironic that a safety agency might be what we want, yet agency is the very thing that is unsafe.”

I believe there is a way to authenticate agents and build a web that works for both bots and humans alike. I’ll talk more about what that looks like in the next edition. 

But if it wasn't clear already, don’t automatically trust everything you see online. The next time LinkedIn sends you a push notification saying “so and so” viewed your profile — they may be a bot in disguise.

Token Talk 14: OpenAI killed my startup. Now the real disruption begins.

By: Thomas Stahura

It was my second desperate pivot, and it made so much sense at the time. An AI marketplace I thought! A site where users can submit and monetize their prompts and use cases.

Turns out, a chat interface is much more intuitive than searching a giant list of prompts. 

So last year, when I heard OpenAI killed 100,000 startups with the launch of its GPT store, I was justifiably skeptical. But it got me wondering: How many companies has OpenAI actually killed? And more broadly, how has AI affected the tech landscape 2 years into the fourth industrial revolution?

Let’s start with the most visible disruption. 

Devtool and edtech companies that once seemed untouchable are crashing back down to earth. Since 2022, Stack Overflow, a question-and-answer platform for developers, lost about 5 to 15% of its web traffic each year. In response, it launched OverflowAI in the summer of 2023. Despite the push, Stack Overflow’s decline has not slowed down. Chegg, a study and homework help platform, rolled out CheggMate in spring 2023. Since then, its stock plunged 97%. Coursera, another edtech company, launched its AI-powered Coursera Coach last year. The stock is down 85% since 2021.

Meanwhile, AI is creeping into the design world: Adobe launched Firefly, its AI image generator; Canva rolled out Canva Code, its text-to-design tool; and Figma followed with Figma Code, its own version of text-to-design. Unlike education or developer tools, the design sector is still growing, but that likely won’t last for long. Large language models can now generate full applications from a simple prompt.

Lovable, on its home page, advertises itself as a Figma competitor. For those still designing by hand, it added an "import from Figma" button. The once-dominant design firm — which nearly sold for $20 billion in 2023 — is now reduced to a button on a rival's site. Figma responded by launching its own AI dev tool, Figma Code, and issued cease-and-desist letters to Lovable and others over their use of "Dev Mode," a term Figma trademarked in 2023.

It’s getting ugly for the companies not named OpenAI.

Speaking of, OpenAI’s image generator now produces nearly perfect text and designs. Using 4o feels like how Photoshop should work — and Adobe better be taking notes.

AI labs are racing toward models that can handle every modality, and businesses are restructuring their products around them. When every product works like a text-to-anything tool, how will users tell them apart?

Honestly, besides UI and mindshare, what are the differences between Lovable, Bolt, Chef, Github Spark, v0, Firebase Studio, AWS App Studio, Cursor, Windsurf, Claude Code, Codex, Figma Code, or Canva Code? (And that's just the tip of the iceberg.) Some may use different models, but even that layer is close to being commoditized. 

So how are entrepreneurs supposed to stand out?

The new frontier in the digital world will probably be vertical AI, or what we call SaaS 3.0. These are tools built for specific industries, workflows, companies, or even individual users. Here, differentiation does not come from the model or UI, but from data, domain expertise, and deep trust.

Rohan D’Souza, founder of Avante, a health benefits admin platform and Ascend portfolio company recently wrote in a post:“The model is the tiniest piece of a much larger enterprise stack required to actually deliver value.” 

In other words, the real moat is not the model itself. It’s the safety, reliability, domain-specific workflows, and trust built around it. 

I believe the digital frontier is only half the story. For decades, the most dramatic technological shifts happened on screens and servers. As Marc Andreessen famously put it: "Software is eating the world." It took a while, but AI is breaking out of code and moving into the physical world — biotech, robotics, manufacturing, logistics, and more.

AI in the physical world is far more defensible. The machines it runs are harder to replicate, and the technical nuances go deeper than traditional software alone. (Ascend labels this category Frontier AI). 

Despite OpenAI's partnership with Anduril, the demand for homegrown physical tech alternatives is only growing. For instance, in 2022 the American Security Drone Act banned federal agencies from using Chinese-made drones and parts. Around that time, some of my college friends were running Uniform Sierra, an aerospace startup focused on building high-quality drones in the U.S. They scaled with a 3D printer farm as demand surged, and the company was recently acquired. More startups, like Seattle-based drone startup Brinc, are reshoring their manufacturing apparatus. 

So did OpenAI kill 100,000 startups? Probably a few thousand. Mine for sure. But in my defense, I built a chat app, a marketplace, and a social media site before OpenAI did. I have the right ideas. I could have kept going — and I still probably would have been steamrolled.

My chat app worked because there were no others like it at the time. I knew back then it wouldn't last. LLMs were too good to stay secret, and OpenAI would productize them better than I could using its API. I knew I had to differentiate. Now chat apps are a dime a dozen.

Differentiation mattered then. It matters even more now, especially with trillion-dollar tech giants pivoting their entire product suites into AI. Timing might get you started, but differentiation keeps you going.

Token Talk 13: Machines Don’t Speak Human

By: Thomas Stahura

When I talk about “AI alignment,” I’m not talking about some diagonal line that relates intelligence to compute. No, what I’m talking about is the strangely old philosophical problem of how to get increasingly powerful artificial intelligences to do what we actually want, rather than what we merely say. Or worse, what we think we want. 

I shouldn't have to explain why alignment is so important since these AIs aren't just playing Go anymore; they're deciding who gets parole, filtering your social media feed, diagnosing your illnesses, teaching your kids, and driving multi-ton vehicles down the highway. 

Not to mention the money involved. It’s estimated OpenAI, DeepMind, and Anthropic average $10 million annually on AI safety (~1% of their compute). Safe Superintelligence (SSI), a company founded by ex-OpenAI Chief Scientist Ilya Sutskever, recently raised $3 billion. 

But all the money in the world won’t help if we don’t even know what “alignment” really means. Thankfully, I took an intro to modern philosophy class last year, only to spend half the semester learning ancient philosophy.

Turns out philosophy, like most things, is understood through contrast. And if you want to understand the problem of AI alignment, you’d better start with the old philosophers, because they were wrestling with the problem of learning and the definition of knowledge long before anyone dreamed of gradient descent.

In roughly 369 BCE, Plato suggests that knowledge is justified true belief. Suppose you believe that the sun will rise tomorrow. This belief is true, and you can justify it by appealing to the laws of astronomy and your past experience of the sun rising every day. According to Plato, your belief counts as knowledge because it is true, you believe it, and you have a reasoned account for it. Now, if you’re building an AI, you might think: “Great! Let’s enable it with reason, program it to have justified true beliefs, and we’re done.” But, as usual, things aren’t so simple. 

Because, in 1963, philosopher Edmund Gettier comes along and throws a wrench in everything. He presents these little puzzles, where someone has a belief that is true and justified, yet intuitively does not seem to possess knowledge. For example, imagine you look at a broken clock that stopped exactly 12 hours ago. But, by coincidence, you check it at the precise time it displays. You form the belief that it is 2:00, which happens to be correct, and your belief is justified because you trust the clock. Yet, most would agree you do not truly “know” the time, since your justification is based on faulty evidence. This is an example of a Gettier problem that reveals justified true belief can sometimes be true merely by luck. Now, if you’re trying to align an AI with human values, you’d better hope it doesn’t get “lucky” in the Gettier sense — generate the right thing for the wrong reasons, or worse, generate the wrong thing for reasons that look right on paper.

And then, just when you think you’ve got a handle on things, along come the postmodernists. Postmodernism is marked by skepticism, including the idea that knowledge must fit a strict formula like justified true belief. Instead, postmodernists argue that what counts as knowledge is often shaped by language, culture, and power, and that our understanding is always partial and constructed rather than absolute. 

Now, let’s dig into this language thing a bit more. Think about Derrida, who points out that language isn’t some crystal-clear window onto reality. Words don’t just stand for things. They stand in for things, usually things that aren’t even there. That’s the whole point, right? I can talk about a cat without dragging one into the room. Language works because of absence, because of gaps. And meaning isn’t fixed by what some speaker intended. For example, you write an email and get run over by a self-driving tesla. Your receiver can still read the email even though your intentions are now… well, irrelevant.

More importantly, Derrida, following folks like Nietzsche, gets us suspicious about interpretation itself. Derrida argues there’s no final, correct interpretation of anything – not the Bible, not Plato, not the U.S. Constitution, and certainly not some vague instruction like OpenAI’s “ensure AGI benefits all of humanity.” Trying to pin down meaning is like trying to nail Jell-O to the wall. Philosophical language, the very stuff we use to talk about high-minded ideas like justice, truth, and marketing material is drenched in metaphor.

As Roderick put it:

“Is the word 'word' a word? No, because I have mentioned it and not used it. It has now become a token of a word... What I am trying to say here is that words are not things. That the attempt that philosophers have made to hook words to the world has failed but it’s no cause for anyone to think we are not talking about anything. See this doesn’t make the world disappear, it just makes language into the muddy, material, somewhat confused practice that it actually is.”

So, how the hell are we supposed to translate our messy, metaphorical, interpretation-laden language into the cold, hard logic of model weights without losing everything important, or worse, encoding the hidden biases and power plays embedded in our own mythology? You tell an AI “be fair,” and what does that mean? Fair according to who? Based on what metaphors? It’s not just that the AI might misunderstand; it’s that language itself is built on misunderstanding, on the impossibility of ever saying exactly what you mean and knowing it’s been received as you intended.

So here’s the punchline: AI alignment is not a technical problem, it’s a philosophical and political one. It’s about who gets to decide what “alignment” even means, whose values get encoded, and who gets left out. It’s about the power to define the good, and the danger that our creations will reflect not our best selves, but our resentments, and contradictions. 

I'm optimistic though because while big tech is trying to cook up some universal recipe for 'aligned AI', probably based on whatever focus group data they collected this quarter, there’s another game in town: open source! Which promises everyone their own perfectly loyal digital butler.

It’s almost comical: OpenAI, after years of being “open” in name only, is finally tossing a model over the wall for the public to play with. If you have a GPU and an internet connection that is. People will align models to do stupid, dangerous, or just plain weird things. But maybe, just maybe, letting individuals wrestle with aligning models to their own contradictory values is better than having one monolithic, corporate-approved 'goodness.’

If language is inherently collaborative, if interpretation is endless, if values are masks for power, then maybe distributing the alignment problem is the only way to avoid the dystopia of a single, centrally-enforced 'truth.' It embraces the uncertainty Roderick talked about, instead of pretending we can solve it with a bigger transformer or a better mission statement. I believe that if we embrace the uncertainty and the collaborative potential of language, perhaps we can build not just smarter machines, but a slightly wiser, more self-aware humanity to guide them.

Token Talk 12: Want Tech Work, In this Economy?

By: Thomas Stahura

Job growth data often tells a story that’s already old. Economic conditions shift fast, and the numbers we get today are usually capturing a version of the world that’s already changed.

Case in point: March’s jobs report showed non-farm employment up by 228,000. (For context, “non-farm” is BLS shorthand for jobs outside of farming, government, nonprofits, and homecare.) Most of the growth came from health care, social assistance, transportation, and warehousing. On paper, it paints a picture of stability. Yipeeeee!

But ask job-seekers, especially in tech, and it feels like a different world. People are sending out hundreds of applications and getting nowhere. Scroll LinkedIn for a few minutes and it’s all right there. The official data may offer some reassurance, but the day-to-day reality doesn’t feel reassuring at all. 

How is anyone, anywhere, finding stable tech work in this economy?

Adding to the uncertainty, new tariffs rattled the stock market and sparked another wave of volatility. It’s a reminder of a deeper truth about the current world order. It's built on the expectation of continuous growth, quarter after quarter. When that growth is threatened, the whole thing wobbles.

So, let’s take a closer look at the tech job market today. 

The BLS report says very little, besides the loss of 2,000 information sector jobs and 8,300 professional, scientific, and technical services jobs. Look up “big tech layoffs” and you will see a much clearer picture. Over the last three years, the tech industry shed 609,723 employees, according to layoffs.fyi. (During the dot-com bust, for context, 54,343 tech workers lost their jobs.) While these people will likely find new work elsewhere, sometimes in tech, it hints at a deeper shift, one likely accelerated by the very technology these companies are building: artificial general intelligence.

To add insult to injury, startups, often held up as the safety net after big layoffs, aren’t hiring like they used to. The team scale just isn’t there, thanks in part to AI automating tasks. For many job-seekers, especially those coming from larger companies, the landing spots are fewer and farther between.

Publicly, big tech executives attribute these workforce reductions to “streamlining operations” and “increasing efficiency,” rather than the looming impact of AI. This narrative helps maintain investor confidence and potentially delays difficult conversations about AI's societal effects. 

Startup founders, meanwhile, are often more transparent about AI reducing team growth demands. They have less societal blowback to worry about and are laser-focused on reserving their runway. 

Today's global GDP sits at roughly 108.2 trillion dollars. Assuming a 3% growth rate, the global economy will need to expand 118.2 trillion dollars by 2050. That's an additional Earth's worth of economic activity in the next 25 years.

Enter AGI. If we use my working definition – a model capable of performing all economically valuable work on a computer, across all domains – the potential impact is big. Really big. Automating the vast majority of knowledge work would unlock productivity gains unseen since the industrial revolution.

But productivity gains for whom, exactly?

Paradoxically, our society also demands of us employment. It is estimated that in the U.S. there are more than 100 million knowledge workers amounting to 76% of the full-time workforce. Not to mention the 3 million truck drivers. That's a sizable voting block. What will these people do if these jobs get replaced by robots? 

There exists another economic force gathering steam: the attention economy. Look around: a striking number of young people (and, increasingly, not-so-young people) aspire to become influencers, creators, streamers. When polled, roughly 57% of gen Zers and 37% of gen Alpha. The creator economy is one of the few sectors not starved for labor.

Lets not forget the platforms enabling this — TikTok, Instagram, YouTube, X — are themselves sophisticated AI. Recommendation algorithms that curate feeds, capture eyeballs, and shape desires. While AI might automate parts of the creator process (generating scripts, editing videos), the core storytelling aspect of it all is harder to replace. (At least, I hope, because I participate in the attention economy through this newsletter…thanks for reading!) 

Beyond the digital, other sectors appear more resilient to near-term automation. Jobs requiring intricate physical dexterity and complex real-world problem-solving will likely persist longer. Think electricians and plumbers, construction workers navigating complex sites, hands-on healthcare providers like nurses and surgeons, and emergency responders. These roles demand a level of physical embodiment and situational awareness that current AI and robotics struggle to replicate economically or effectively. Manufacturing, while increasingly automated, still requires significant human oversight and intervention for complex tasks and quality control.

So, this sequence seems likely: knowledge work first, then transportation as autonomous vehicles mature, with physically demanding and highly interactive jobs proving most durable.

There is another option! Alongside the rise of the influencer, there's a powerful surge in entrepreneurial spirit. Seventy-six percent of Gen Alpha aspire to be their own boss or have a side hustle, echoed by 62% of Gen Z. This path requires carving out new niches, potentially leveraging AI tools rather than being replaced by them.

This entrepreneurial drive, coupled with the resilience of physical trades and the enduring appeal of human connection in the attention economy, paints a complex picture of the future labor market. 

Yet, the political focus often seems inverted, emphasizing the revitalization of manufacturing, just as the knowledge economy faces its AI reckoning. The admin wants us to make their iPhones, not their TikToks. 

AGI is seen as the engine for achieving the massive economic growth our system demands, and is simultaneously the force threatening to displace the very workers who defined our modern economy. Navigating this transition is perhaps the central challenge of our time. 

But managing the economic fallout is only half the battle. Ensuring these increasingly powerful AI systems operate safely and align with human values is critical. That’s the alignment problem, and I’ll talk about it more next week, so stay tuned!

Token Talk 11: Do Omni models bring us closer to AGI?

By: Thomas Stahura

Sam Altman’s manifest destiny is clear: achieve AGI.

There is little consensus on what AGI actually means. Altman defines it as “the equivalent of a median human that you could hire as a coworker and they could do anything that you’d be happy with a remote coworker doing.”

Dario Amodei, Anthropic founder and CEO, says AGI happens “when we are at the point where we have an AI model that can do everything a human can do at the level of a Nobel laureate across many fields.”

Demis Hassabis, CEO of Google DeepMind, puts it more succinctly. AGI, he says, is “a system that can exhibit all the cognitive capabilities humans can.”

If AGI is inevitable, the next debate is over timing. Altman thinks this year. Amodei says within two. Hassabis sees it arriving sometime this decade.

As I mentioned last week, AI researchers are working to unify multiple modalities — text, audio, and images — into a single model. These so-called “omni” models can natively generate and understand all three. GPT-4o is one of them. The “o” meaning Omni. It has handled both text and speech for nearly a year. But image generation was still ruled by diffusion models, until last week.

It began with a research paper from a year ago out of Peking University and ByteDance. The paper introduced Visual AutoRegressive modeling, or VAR. The approach uses coarse-to-fine next-scale prediction to generate images more efficiently. It does this by predicting image details at increasing resolutions, starting with a low-resolution base image and progressively adding resolution to it, which improves both speed and quality over conventional GPT-style raster-scan or diffusion denoising methods.

Put simply, VAR enables GPT-style models to overtake diffusion for image generation at large scales.

Qwen-2.5 Omni, the open-source omni model from China I referenced last week, may be an early sign of where things are heading. In its research paper, they wrote, “We believe Qwen2.5-Omni represents a significant advancement toward artificial general intelligence (AGI).”

Is omni a leap toward AGI? That’s the bet labs are making.

And generative model-native startups will need to respond. Companies like Midjourney and Stability, still rooted in diffusion, will likely have to build their own GPT-style image generators to compete. Not just for images, but potentially across all modalities. The same pressure may extend to music and video, pushing startups like Suno, Udio, Runway, and Pika to expand beyond their core businesses. This will be over years not months, especially for video. Regardless, I'm certain researchers at OpenAI, Anthropic, Google, and Microsoft are actively training their next gen omni models.

OpenAI has a lot riding on AGI. If it gets there first, Microsoft loses access to OpenAI’s most advanced models.

Tensions between the two have been building for months. The strain began last fall, when Mustafa Suleyman, Microsoft’s head of AI, was reportedly “peeved that OpenAI wasn’t providing Microsoft with documentation about how it had programmed o1 to think about users’ queries before answering them.” 

The frustration deepened when Microsoft found more value in the free DeepSeek model than in its $14 billion investment in OpenAI.

Microsoft is already developing its own foundation model, MAI, which is rumored to match OpenAI’s performance. OpenAI, meanwhile, just closed a $40 billion tender offer on the strength of GPT-4o and its new image generator, an update more significant than most realize.

From the outside, it appears AGI is near. Granted I suspect it will be around the 2030s when we’ll feel the impacts. My own working definition: a model capable of performing all economically valuable work on a computer, across all domains.

What that means for the labor market is another story. Stay tuned!

Token Talk 10: What Startups Gain from China’s AI Push

By: Thomas Stahura

The race to dominate artificial intelligence is accelerating on every front, as research labs across the globe push full throttle on new model releases while governments move to cement AI supremacy. 

In the past few weeks, Google released two major models, OpenAI launched long-awaited image capabilities, and Chinese labs pushed open-source systems that rival the best from the West. What began as a battle between private research labs is now a global competition shaped by open models, national strategies, and shifting power dynamics. 

Here's a breakdown of what just happened:

Google announced Gemma 3, the latest model in its Gemma trilogy. At around 27 billion parameters, I wouldn’t call it “small,” yet it punches above its weight class. It’s the only open model that can take video as input. Mistral open-sourced Mistral-Small-3.1 a few days later, a 24 billion parameter model that outperforms Gemma 3 on most benchmarks.

But really the larger news here is Gemini 2.0 Flash Experimental. Google’s new closed-source flagship model and the company's first unified multimodal model. Meaning, the model can generate and understand both images and text simultaneously. I’ve been playing around with it. It is capable of editing images using simple text prompts, generating each frame of a GIF, and even composing a story complete with illustrations. (This is similar to Seattle startup 7Dof, which showcased a visual chain-of-thought editing tool at South Park Commons last year.) 

Traditionally, transformer models were used to generate text, while diffusion models generate images. Today, researchers are experimenting with unifying both architectures into a single model (similar to what is going on with VLA models in robotics). The ultimate goal is to build a model that unifies the text, image, and audio spaces.

Gpt-4o has had image generating abilities for a while. Greg Brokman demoed gpt-4o generating images in May. And this week the company finally launched the capability. 

At this point in the AI race, OpenAI seems to be reacting more than leading. Launching 4o’s image gen was a response to Gemini 2.0 Flash Experimental. 

Trump said multiple times he wants “American AI Dominance.” And, to that effect, the White House invited public comment on its AI Action Plan. OpenAI published its response, slamming DeepSeek and urging the administration to implement the following: 

1. An export control strategy that exports democratic AI

2. A copyright strategy that promotes the freedom to learn

3. A strategy to seize the infrastructure opportunity to drive growth

4. And an ambitious government adoption strategy.

Google also responded, urging America to:

1. Invest in AI

2. Accelerate and modernize government AI adoption

3. Promote pro-innovation approaches internationally

China has their own plan. 

Dubbed the “New Generation Artificial Intelligence Development Plan” (2017), the agenda aims to make China the global leader in AI by 2030. The worry seems to be about the sheer quality and openness of the models out of China today. It’s hard to name a model out of a Chinese AI lab that isn’t open source. 

Over the course of a week earlier this month, DeepSeek open-sourced all technical details used in the creation of its R1 and V3 models. All except for the actual dataset used to train the models (adding to the suspicion that DeepSeek trained on gpt-4o outputs). 

DeepSeek also open-sourced Janus-Pro. Though the model got significantly less attention than its big brother, Janus-Pro is a unified multimodal model (like Gemini 2.0 Experimental), capable of generating and understanding both images and text — one of the first open-source models of its kind.

Qwen, the AI lab out of Alibaba Cloud, has launched its own reasoning model: QwQ-32B, competing with and reaching DeepSeek R1 performance on many benchmarks. The model already has 615k downloads on Hugging Face.

OpenBMB (Open Lab for Big Model Base) is a Chinese AI research group out of Tsinghua University. The group is most known for MiniCPM-o-2_6, a unified multimodal model capable of understanding images, text, and speech, as well as generating text and speech. The model is at gpt-4o levels, according to the benchmarks, and has 766k downloads.

DeepSeek V3.1 also launched this week. The model leapfrogged Grok 3 and Claude 3.7 to become the best performing non-resoning model. The first time an open-source model achieved SOTA. 

That is until Google 2.5 Experimental dropped a few hours later. More on that next week. 

Ok, here’s my take on the flood of releases: 

This is good news for startups, full stop. More models means more competition, and that means lower prices. Even if the U.S. bans Chinese models, most are fully open. Developers can fine-tune them and build whatever they need.

The real challenge now is the viability of America’s top AI labs. If China can flood the market with cheap, open, high-quality models, they could undercut their U.S. counterparts. It’s a familiar playbook — one China used before in other industries. This time, it’s electrons instead of atoms. That shift might tilt the board in China’s favor.

Only time will tell, so stay tuned!

Token Talk 9: Who's really paying the cloud bill?

By: Thomas Stahura

My AWS bill last week was $257. I have yet to be charged by Amazon.

In fact, I have never been charged for any of my token consumption. Thanks to hackathons and their generous sponsors, I’ve managed to accumulate a bunch of credits. Granted they expire in 2026. I’ll probably run out sooner rather than later.

With the rise of open source, closed-source incumbents have been branding their model as “premium” and priced them accordingly. Claude 3.7 Sonnet is around $6 per million tokens, o1 is around $26 per million tokens, and gpt-4.5 is $93 per million tokens (averaging input and output token pricing).

I'm no startup — simply an AI enthusiast and tinkerer — but all these new premium AI models have me wondering: how can startups afford their AI consumption?

Take Cursor, the AI IDE pioneer. It charges $20 per month for 500 premium model requests. That sounds reasonable until you realize that coding with AI is very context heavy. Every request is jam packed with multiple scripts, folders, and logs, easily filling Claude’s 200k context window. A single long (20 request) conversation with Claude 3.7 in Cline will cost me $20, let alone the additional 480 requests.

To break even, by my calculations, Cursor would have to charge at least 15 to 20 times more per month. I highly doubt it will do that anytime soon. 

The AI industry continues to be in its subsidized growth phase. Claude 3.7 is free on Github Copilot. Other AI IDEs like Windsurf and Pear AI are $15 per month. The name of the game is growth at any cost. Like Uber and Airbnb during the sharing economy or Facebook and Snapchat during Web 2.0, the AI era is no different. 

Or is it?

It all comes down to who is subsidizing and how that subsidy is being accounted for. 

During previous eras, VCs were the main culprits, funding companies that spent millions acquiring customers through artificially low prices. Much of that applies today; Anysphere (which develops Cursor) raised at least $165 million. Besides salaries, it could be theorized most of that money is going to the cloud due to AI’s unique computational demands. Big Tech has much more power this time around and are funding these startups and labs through billions of cloud credits.

OpenAI sold 49% of its shares to Microsoft in exchange for cloud credits. Credits that OpenAI ultimately spent on Azure. Anthropic and Amazon have a similar story; however, Amazon invested $8 billion in Anthropic instead of giving credits. But, as a condition of the deal, Anthropic agreed to use AWS as its primary cloud provider so that money is destined to return to Amazon eventually.

Take my $257 AWS bill from last week — technically, I haven't been charged because I'm using credits. However, this allows Amazon, Microsoft, and other cloud providers to forecast stronger future cloud revenue numbers to shareholders, in part on the bet of continued growth by AI startups. (Credits given to startups expire so its use ‘em or lose ‘em before they inevitably convert to paid usage.) 

Since 2022, the top three cloud providers, AWS, Azure, and Google, have grown their cloud revenue by 20%, 31%, and 33% each year, respectively. That rapid growth needs to continue to justify their share prices — and it’s no secret they are using AI to sustain that momentum. 

The real question is when will it end? The global demand for compute is set to skyrocket, so perhaps never. Or maybe distilling large closed-sourced models into smaller, local models will pull people from the cloud. Or Jevons Paradox reigns true and even more demand is unlocked. 

Only time will tell. Stay tuned!

P.S. If you have any questions or just want to talk about AI, email me! thomas@ascend.vc

Token Talk 8: The Robot Revolution Has Nowhere Left to Hide

By: Thomas Stahura

Escaping a rogue self-driving Tesla is simple: climb a flight of stairs.

While a Model Y can’t climb stairs, Tesla’s new humanoid surely can. If Elon Musk and the Tesla bulls have their way, humanoids could outnumber humans by 2040. That means there’s quite literally nowhere left to hide — the robot revolution is upon us. 

Of course, Musk isn’t alone in building humanoids. Boston Dynamics has spent decades stunning the internet with robot acrobatics and dancing. For $74,500, you can own Spot, its robot dog. Agility Robotics in Oregon and Sanctuary AI in British Columbia are designing humanoids for industrial labor, not the home. China’s Unitree Robotics is selling a $16,000 humanoid today.

These machines may feel like a sudden leap into the future, but the idea of humanoid robots has been with us for centuries. Long before LLMs and other abstract technologies, robots were ingrained in culture, mythology, and our collective engineering dreams.

Around 1200 BCE, the ancient Greeks told stories of Talos, a towering bronze guardian patrolling Crete. During the Renaissance, Leonardo da Vinci sketched his mechanical knight. The word “robot” itself arrived in 1920 with Karel Čapek’s play R.U.R. (Rossum’s Universal Robots). By 1962, The Jetsons brought Rosie the Robot into American homes. And in 1973, Japan’s Waseda University introduced WABOT-1, the first full-scale — if clunky — humanoid robot.

Before the advent of LLMs, the vision was to create machines that mirror the form and function of a human being. Now it seems the consensus is to build a body for these models. Or rather, to build models for these bodies.

They're calling it a vision-language action (VLA) model and it's a new architecture purpose-built for general robot control. Currently, there are two types of model architectures dominating the market, transformer and diffusion. Transformer models are used to process and predict sequential data, think text generation, while diffusion models are used to generate continuous data through an iterative denoising process, think image generation.

VLA models (like π0) combine elements from both approaches to address the challenges of robotic control in the real-world. These hybrid architectures enable robots to translate visual observations (from cameras) and language instructions (robots given task) into precise physical actions using the sequential reasoning of transformers and the continuous output of diffusion models. Other frontier VLA model startups include: Skild (reportedly in talks to raise $500 million at a $4 billion valuation); Hillbot; and Covariant. 

A new architecture means a new training paradigm. Lucky Robots (Ascend portfolio company) is pioneering synthetic data generation for VLA models by having robots learn in a physics simulation enabling developers to play with these models without needing a real robot. Nvidia is cooking up something similar with its Omniverse platform. 

Some believe that more data and better models will lead to an inflection point in robotics, similar to what happened with large language models. However, unlike text and images, physical robotics data cannot be scraped from the web and must either be collected by an actual robot, or synthesized in a simulation. Regardless of how the model is trained, a real robot is needed to act upon the world.

At the very least, it’s far from a solved problem. Since a robot can have any permutation of cameras, joints, and motors, making a single unified model that can inhabit every robot is extremely challenging. Figure AI (valued at $2.6 billion, of which OpenAI is an investor) recently dropped OpenAI’s models in favor of in-house models. It’s not alone. So many VLA models are being uploaded to Hugging Face that the platform had to add a new model category just to keep up. 

The step from concept to reality has been a long one for humanoid robots, but the pace of progress suggests we're just getting started. 

P.S. If you have any questions or just want to talk about AI, email me! thomas@ascend.vc

Token Talk 7: AI's walls, moats, and bottlenecks

By: Thomas Stahura

Is Grok SOTA?

If that phrase comes across as gibberish, allow me to explain.

On Monday, xAI (Elon’s AI company) launched Grok 3, claiming state-of-the-art (SOTA) in terms of performance. SOTA has become a sort of catch-all term for crowning AI models. Grok’s benchmarks are impressive, scoring a 93, 85, and 79 on AIME (math), GPQA (science), and LCB (coding). These marks outperform the likes of o3-mini-high, o1, DeepSeek R1, sonnet-3.5, and gemini 2.0 flash. Essentially, Grok 3 outperforms every model except for the yet-to-be released o3. An impressive feat for a 17-month-old company!

I could mention that Grok used 100k+ GPUs during training, or that it built an entire data center in a matter of months. But much has been documented there. So given all that's happened this year with open source, distillation, and a number of tiny companies achieving SOTA performance, it’s much more useful to discuss walls, moats, and bottlenecks in the AI industry.

Walls

The question about a “Wall” in AI is really a question about where, when, or if AI researchers will reach a point where model improvements stall. Some say we will run out of viable high-quality data and hit the “data wall”. Others claim more compute during training will cause models to reach a “training wall”. Regardless of this panic, AI has yet to hit the brakes on improvement. Synthetic data (reinforcement learning) seems to be working, and more compute, demonstrated by grok 3, continues to lead to better performance. 

So where is this “Wall”?

Image source.

The scaling laws in AI suggest that while there isn't a hard "wall" per se, there is a fundamental relationship between compute, model size, and performance that follows a power law distribution. This relationship, often expressed as L ∝ C^(-α) where L is the loss (lower is better) and C is compute, shows that achieving each incremental improvement requires exponentially more resources. For instance, if we want to reduce the loss by half, we might need to increase compute by a factor of 10 or more, depending on where we are on the scaling curve. This doesn't mean we hit an absolute wall, but rather face increasingly diminishing returns that create economic and practical limitations — essentially there exists a "soft wall" where the cost-benefit ratio becomes prohibitively expensive. So how then have multiple small AI labs reached SOTA so quickly?

Moats

When OpenAI debuted ChatGPT in November 2022, the consensus was it would take years for competitors to develop their own models and catch up. Ten months later Mistral, a previously unknown AI lab out of France, launched Mistral 7b, a first-of-its-kind open-source small language model. Turns out that training a model, while still extremely expensive, costs less than a single Boeing 747 plane. 

The power law relationship can also help us understand how smaller AI firms catch up so quickly. The lower you are on the curve, the steeper the improvements are for each unit of compute invested, allowing smaller players to achieve significant gains with relatively modest resources. This "low-hanging fruit" phenomenon means that while industry leaders might need to spend billions to achieve marginal improvements at the frontier, newer entrants can leverage existing research, open-source implementations, and more efficient architectures to rapidly climb the steeper part of the curve. (At Ascend, we define this as AI’s “fast followers”.) 

Costs have only gone down since 2022, thanks to new techniques like model distillation and synthetic data generation. Techniques that DeepSeek used to build R1 for a reported $6 million. 

The perceived "moat" of computational resources isn't as defensible as initially thought. It seems the application layer is the most defensible part of the AI stack. But what is holding up mass adoption?

Bottlenecks

Agents, as I mentioned last week, are the main AI application. And agents, in their ultimate form, are autonomous systems tasked with accomplishing a goal in the digital environment. These systems need to be consistently reliable if they are to be of value. Agent reliability is mainly affected by two things: prompting and pointing.

Since an agent is in a reasoning loop until its given goal is achieved, the prompt that is used to set up and maintain that loop is crucial. The loop prompt will be run on every step and should reintroduce the task, tools, feedback, and response schema to the LLM. Ultimately, these AI systems are probabilistic so the loop prompt should be worded in a way to increase the probability of a correct response as much as possible. Much harder said than done.

Vision is another bottleneck. For example, if an agent decides it needs to open the Firefox browser to get online, it first needs to move the mouse to the Firefox icon, which means it needs to see and understand the user interface (UI). 

Thankfully, we have vision language models (VLMs) for this! The thing is, these VLMs, while they can caption an image, do not understand the precise icon location well enough to provide pixel perfect x and y coordinates. At least not yet to any reliable degree. 

To prove this point, I conducted a VLM pointing competition wherein I had gpt-4o, sonnet-3.5, moondream 2, llama 3.3 70b, and molmo 7b (running on replicate) point at various icons on my Linux server. 

Our perception of icons and logos is second nature to us humans. Especially those of us who grew up in the information age. It boggles the mind that these models, who are now as smart as a graduate student, can’t do this simple task ten times in a row. In my opinion, agents will be viable only when they can do hundreds or even thousands of correct clicks. So maybe in a few months… Or you can tune in next week for Token Talk 8!

P.S. If you have any questions or just want to talk about AI, email me! thomas@ascend.vc

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