Venice ($VVV): The Bubble's Mirror
Original author: nikshep
Compile: Peggy

Editor’s Note: VVV’s recent market performance has brought Venice to the forefront of the AI x Crypto narrative. According to CoinMarketCap, the Venice Token’s latest price is approximately $17.28, with a 24-hour increase of about 19% and a circulating market cap of around $795 million; CoinGecko shows a more than 60% increase over the past seven days and a market cap of approximately $694 million. Together, these figures point to one reality: the market is refocusing on this “privacy AI + token economy” project.

But what this article truly discusses is not the short-term price surge of VVV, but a more fundamental question: Where will the value of AI platforms settle when model capabilities rapidly become commoditized?

The author's core argument is that leading AI labs such as OpenAI and Anthropic are trapped in a "equity structure trap": their valuations rely on the assumption that model capabilities will remain scarce and command high premiums over the long term, but China’s open-source models, low-cost training, open-weight ecosystems, and cloud-based deployment are rapidly driving down the price of model capabilities themselves. In other words, the most expensive segment of the AI industry may be becoming the hardest to sustain profitability in.

Within this framework, Venice is viewed by the author as an inverted structure: it does not train models, but rather leverages open-source model capabilities; it does not rely on centralized data retention, but emphasizes privacy and TEE proofs; it does not turn users into training data, but instead integrates users into the platform economy through mechanisms such as VVV staking, subscription burns, and DIEM compute rights. What the author truly aims to convey is that Venice is not merely a “tokenized AI application,” but an experiment in using tokens to reconstruct the relationship between consumers and software.

What matters most is not whether Venice can directly challenge OpenAI, but whether the AI market is splitting into two segments: one continuing to serve customers willing to pay for cutting-edge models and accept enterprise-grade compliance and data retention; and another shifting toward “good enough” open-source model capabilities, placing greater emphasis on privacy, censorship resistance, low cost, native agent access, and user ownership. If this split occurs, Venice’s opportunity lies not in winning the entire model war, but in becoming the inference layer and settlement rail for the open agent economy.

Therefore, this article presents a classic structural bullish argument: it is not merely betting on an increase in VVV’s price, but on the convergence of several trends—the commoditization of the model layer, open-source models catching up, the rise of agent-based payments, and the user-owned economy.

The risk lies precisely here—if open-source model progress slows, token burns fail to keep pace with growth, or Venice fails to genuinely cultivate user relationships, this narrative will be revalued. But at least for now, VVV’s market performance shows that the market is beginning to pay a higher premium for this story of “same demand, opposite economic model.”

The following is the original text:

These labs are pouring hundreds of billions of dollars into trying to defend a moat that is evaporating in real time. GLM-5.1 has outperformed GPT-5.4 on the most challenging programming benchmarks—it is open source, licensed under MIT, and trained on Chinese hardware that the U.S. has attempted to block. The cost of training frontier capabilities has dropped by approximately 95% over the past eighteen months. Every dollar of OpenAI’s $852 billion valuation rests on the assumption that these changes don’t matter. But they do. And Venice is the only consumer-grade AI platform that will directly benefit economically when this reality is finally repriced by the market; even if such repricing never occurs, its investment thesis remains sound.

The core argument of the article in April that article was that Venice holds a unique position in the agent economy. This assessment still holds—usage has tripled, the burn ledger has exceeded 42% of the genesis supply, DIEM has been repriced by 75% within six weeks, and the token price has more than doubled since I wrote that in-depth analysis.

But the "Seven Advantages" framework I proposed in April may have underestimated what is actually happening. Venice is not an AI company with a privacy label that happens to issue tokens. It is a new economic structure for consumer software: users are owners, the platform is the track, and value is measured not in equity but in computational power rights.

This structure is not a stack of features, but the only configuration capable of surviving the impending changes at the model layer. What the bubble is built upon, Venice stands in direct opposition to. The same market, the same demand, a completely opposite economic model. This is the mirror.

This is the argument I didn’t clarify in April. Now I’m adding it.

Equity structure trap

OpenAI, Anthropic, and Together AI share a commonality unrelated to their products: their investors expect equity returns denominated in U.S. dollars, in the hundreds of billions of dollars, and demand these returns within an accelerated timeline.

It sounds ordinary until you follow this logic further.

OpenAI’s $852 billion valuation implies it will need to generate annual revenues of approximately $200 billion to $280 billion by 2030 to justify this multiple. The company currently earns $2 billion per month but reported a $13.5 billion loss in the first half of 2025; meanwhile, as inference costs surged fourfold to $8.4 billion, its adjusted gross margin declined from 40% to 33%. Compute and talent costs consume 75% of total revenue. Microsoft will also take an additional 20% before 2032. OpenAI expects its compute spending to reach $121 billion by 2028, with a projected loss of $85 billion that year alone, and profitability not expected until after 2030.

Anthropic is caught in the same trap, just on a different scale. With a $380 billion valuation, a $30 billion ARR run rate, and projected training costs of $42 billion by 2029, Google committed $40 billion last month, and Amazon added $25 billion—but both are essentially circular cloud credits rather than true equity capital. The five major hyperscale cloud providers have collectively committed $660 billion to $690 billion in AI infrastructure spending for 2026 alone. Goldman Sachs estimates cumulative spending from 2025 to 2027 will reach $1.4 trillion, roughly triple the spending from 2022 to 2024. Sam Altman has personally signed off on $1 trillion in AI deals, while OpenAI’s revenue stands at just $13 billion.

These are not ordinary companies. They are sovereign-level infrastructure bets disguised as software firms. Their valuations require the model layer to remain perpetually expensive. But the reality is that the model layer is becoming increasingly cheaper.

Decoupling

Over the past 60 days, the relationship between AI capital expenditures and AI capability has fractured, as evidenced by the release of three open-weight models.

Z.ai's GLM-5.1, released on April 7, scored 58.4 on SWE-Bench Pro, surpassing GPT-5.4's 57.7 and Claude Opus 4.6's 57.3. It is open-sourced under the MIT license and trained entirely on Huawei Ascend chips, without using any NVIDIA hardware; meanwhile, Z.ai remains on the U.S. Entity List, prohibited from accessing H100s. Its API pricing is $1 per million tokens for input and $3.2 for output, making it 5 to 8 times cheaper than Claude Opus’s $5 / $25.

Moonshot’s Kimi K2.6, released on April 20, became the top-ranked open-weight model on the Artificial Analysis Intelligence Index with a score of 54, compared to 57 for leading closed-source labs. It outperformed GPT-5.4 on agent tasks: achieving a HLE-with-tools score of 54.0, higher than GPT-5.4’s 52.1. Its SWE-Bench Verified score of 80.2 nearly matched Claude Opus’s 80.8. Cloudflare prices it at $0.95 per input and $4 per output, making it approximately 15 times cheaper than Claude Opus under heavy workloads. The initial Kimi K2 training cost only $4.6 million.

DeepSeek V4-Pro, released on April 24, ranks second on the Intelligence Index, behind only Kimi K2.6, and outperforms all other models except the top three frontier proprietary labs. It is licensed under the MIT license. The training cost of DeepSeek V3 was $5.6 million.

Three Chinese labs, 60 days, all open source, all achieving or surpassing state-of-the-art performance on at least one major benchmark, priced 5 to 15 times cheaper, with one even running on sanctioned hardware. The capabilities that supported OpenAI’s valuation in 2024 are now freely downloadable on Hugging Face, deployable on rented hardware, and continuing to improve each quarter.

This is not the so-called “Chinese AI moment.” Structural arbitrage at the model layer is happening in real time. A scholarly paper from March 2026 directly stated: “Pre-training scale has become decoupled from cutting-edge AI capabilities.” The share of Chinese open-source models in global usage has grown from 1.2% in 2025 to 30%. Apple is evaluating DeepSeek, Qwen, and Doubao for integration into iOS 27. AWS, Azure, and Google Cloud all offer DeepSeek deployment. Today, 80% of startups seeking VC funding are built on open-source models. Meta’s Llama series was intentionally released to drive commoditization at the model layer—when a $1.6 trillion market cap company is the most aggressive price reducer in your market, it’s clear where margins will flow.

Every dollar of OpenAI’s $852 billion valuation assumes these changes are irrelevant. It assumes enterprise customers will indefinitely pay premium prices for token-based capabilities, even when GLM-5.1 can offer similar capabilities at one-eighth the cost; it assumes Kimi K2.6’s open weights don’t matter; it assumes DeepSeek selling at less than 3% of the price of frontier models is inconsequential. It assumes these labs can simultaneously achieve tenfold revenue growth and expand profit margins in a market where competitors offer their products for free.

Jai Das of Sapphire Ventures has referred to OpenAI as the “Netscape of the AI era.” Mark Zuckerberg has also publicly acknowledged the existence of AI bubble dynamics. In March, the Pentagon officially classified Anthropic as a supply chain risk because Anthropic refused to allow Claude to be used for mass surveillance and autonomous weapons; meanwhile, OpenAI and Google signed “all lawful uses” agreements to avoid the same fate. Centralized AI companies are subject to government coercion, and their architectures cannot reject such coercion. Venice’s architecture can.

These labs are not unaware of the issues. They simply cannot pivot. The investors who wrote checks based on an $852 billion valuation did not buy into a future where the model becomes commoditized. They bought into a future where the model maintains a high premium indefinitely. These are two entirely different companies, and for the latter to truly materialize, it must first write down the valuation of the former.

This is the trap. The issue isn’t with the rejection mechanism stack or the logging architecture. The real problem is that the only investors who can tolerate Venice’s economic structure are those who already hold VVV.

It's not one market, but two markets.

From here, this argument no longer requires a bubble burst to hold true.

Assume these labs barely survive. Assume GPT-6 remains the best in class, Claude Opus 5 continues to lead in reasoning, and Gemini maintains its frontier in multimodality. Assume enterprise contracts last long enough for these companies to secure refinancing and weather their valuation pressures.

This isn't important either. The market will split.

Frontier intelligence accounts for only a small fraction of total inference demand. The vast majority of real-world workloads—programming assistance, writing, analysis, image generation, video, agent execution, customer service, research, summarization—have already reached “good enough” levels months ago. GLM-5.1’s coding capability in production is now comparable to GPT-5.4. Kimi K2.6’s agent execution capability is on par with Claude Opus 4.6. DeepSeek’s general reasoning ability is essentially on par with any model outside the absolute top of the leaderboard. For 80% of real-world needs, the open-weight ecosystem is already sufficient—and improving every quarter.

These requirements demand intelligent attributes that labs are structurally unable to provide: privacy, uncensored outputs, no account needed, no logging, native agent access, predictable costs, and user ownership. Labs serve a small subset of high-end customers willing to pay enterprise prices and accept monitoring. Venice serves everyone else—and that’s precisely the larger, faster-growing half of the market.

In a bull market scenario: these labs collapse, and Venice takes over the entire market. In the base case scenario: the market splits, with Venice holding the larger side. Even in a bear market scenario—where these labs continue to dominate frontier capabilities and no repricing event occurs—Venice remains one of the few consumer-grade AI platforms capable of serving the 80% of inference demand that doesn’t require frontier capabilities and cannot accept the labs’ business models.

This argument does not require a crash to occur. It only requires the open-source curve to continue along the path it has already taken.

Why did Venice capture the larger half of the market? Not because it was destined to win all. It might, but the structural answer is simpler than that.

Venice is the only consumer-facing AI platform that allows users to own the equity of the services they use. Stake VVV to earn rewards and gain lifetime Pro access. Lock sVVV to mint DIEM, securing a permanent compute equity stake that appreciates as inference costs become commoditized. Every paying user fuels a burn cycle that compounds and enhances the positions of all other users. This isn’t just a feature—it’s an entirely different relationship between consumers and products, one that Big AI cannot offer due to equity structures that exclude users as owners.

Look at what users truly need but labs cannot provide. Privacy is not a policy—it’s verifiable TEE proofs, zero retention, and an architecture where nothing can be seized. For 99% of intelligent use cases that don’t require filtering through corporate brand safety committees, uncensored output is essential. Open-source frontier models can be deployed within days of release, because Venice doesn’t need to defend a moat that forces the model layer to remain perpetually expensive. Agent-native access—autonomous API keys, x402 wallet payments, zero human intervention—because the agents being deployed today simply cannot work with anything else.

Each of these forces is independently strengthening. As data breaches increase and regulations tighten, demand for privacy is growing. As users grow increasingly frustrated with "brand-safe AI products" that routinely block everyday tasks, demand for censorship resistance is growing. Open source is closing the "good enough" gap every quarter. The share of agents in total reasoning demand is doubling. None of these forces point to a lab. They all point to Venice.

Mirror

A platform built on the opposite of every bubble assumption, whose many characteristics seem accidental until you see the overall shape.

Zero training cost. Venice has never spent a dollar training a model. Every release from Llama, Qwen, Mistral, GLM, DeepSeek, and Kimi is a free upgrade. Those labs have spent hundreds of billions of dollars trying to maintain a lead measured in months. Venice spends nothing and rides directly on the curve they pay to push. When GLM-5.1 was released at one-eighth the price of Claude, it was a margin expansion event for Venice—but a survival threat to companies trying to charge premium prices for equivalent capabilities.

Zero data retention. At laboratories, privacy is a policy commitment; here at Venice, privacy is a mathematical structure. OpenAI Enterprise does not default to using customer data for model training, and customers can set retention windows—but during inference, prompts still flow through OpenAI’s servers and may be accessed by authorized personnel for abuse investigations, support, and legal matters. Policies can change. Vendors can also be compromised—in November 2025, Mixpanel leaked API customer names, emails, and organization IDs via SMS phishing. Runtime data can also be exfiltrated through novel vulnerabilities—Check Point disclosed a ChatGPT vulnerability in March that silently leaked conversation content via a DNS side channel. Even with contracts mandating zero retention, the architecture remains trust-based. Venice’s TEE attestation transforms privacy guarantees into cryptographic guarantees. Secure enclaves process prompts, return results, prove execution, then discard inputs. Venice cannot see your data because the architecture forbids it. This is not a privacy moat—it’s a balance sheet that grows stronger as data regulations tighten.

Token appreciation is mechanically tied to usage. Every paid request buys and burns VVV on the open market. Tiered subscription burns scale with revenue growth: Pro ~$2, Pro+ ~$5, Max ~$10. Emissions have been reduced five times over the past 18 months, with another halving planned before midsummer. 42% of the genesis supply has already been burned. No allocations go toward investor returns, as there are no investors. Every dollar of revenue is compounded back into the assets owned by stakers.

Users are an asset class, not a product. This is a point that no one has clearly articulated. On centralized platforms, users generate data, which becomes training input, and that training input becomes the platform’s moat—users are the product. On Venice, users consume tokens through staking, subscriptions, and paying for inference fees; these tokens are burned, thereby increasing the value of every holder’s position. Users are assets. The economic vector is the exact opposite of virtually every other consumer software business in the world.

DIEM is a fixed-income instrument powered by reasoning capacity. 1 staked DIEM equals a $1 credit that auto-renews daily and remains valid indefinitely. It can be traded on Aerodrome or unlocked by burning to reclaim the original sVVV stake. During the lock-up period, it earns approximately 80% of the standard VVV staking yield. This is not a regular token, but a fixed-income instrument backed by AI infrastructure. As underlying compute power becomes commoditized, each DIEM can purchase more reasoning capacity annually while its nominal claim remains constant. The lab is issuing equity based on an asset that is depreciating; Venice is issuing perpetual claims on an asset that is continuously appreciating.

Put together, what you get is not “an AI company with a crypto flavor.” You get an entirely new form of consumer software: every economic relationship between users and the platform is mediated by assets owned, priced, traded, and profited from by the users themselves. And whether or not those labs survive, these properties hold true. They are not a bet on collapse, but a structural advantage that compounds in any macro environment.

Why now?

The agent economy is coming, and the timing coincides precisely with these labs running out of funding runway.

Coinbase Agentic Wallets have processed over 165 million transactions on x402. Google AP2 has launched with over 60 partners. Visa has released the Trusted Agent Protocol. Mastercard has invested $1.8 billion in stablecoin infrastructure—the largest stablecoin transaction ever. Coinbase launched Agent.market in April, with 69,000 active agents trading on the platform. McKinsey estimates that by 2030, agent-mediated consumer commerce will reach $3 trillion to $5 trillion.

Each of these agents requires a reasoning service provider. But they cannot use OpenAI or Anthropic in serious scenarios. The lab’s compliance architecture requires KYC; their revenue models require logging; their content policies require rejection. Agents cannot fill out registration forms, cannot enter CVVs, cannot agree to terms of service that may change next quarter. Coinbase’s CEO put it bluntly: AI agents cannot meet KYC requirements and cannot use traditional banking systems.

Thus, as China’s open-weight models are arbitraging beneath the core businesses of these labs, the most important new category of AI infrastructure—autonomous agents—is structurally incompatible with their architectures. Agents reinforce market fragmentation: high-end demand remains at the top, while everything else moves toward agent-native.

Venice serves both ends of this transaction. The autonomous API key flow is live—smart agent staking VVV, signing tokens, minting keys, and paying with DIEM, all without human intervention. x402 wallet payments are now live on all paid endpoints. A single credential grants access to JSON-RPC across 11 chains. Every Eliza, Fleek, OpenClaw, Hermes, and NanoClaw agent is ready to use out of the box. The agents being deployed today run on Venice’s infrastructure because no other option can simultaneously offer permissionless access, privacy, censorship resistance, and native agent support.

When the commercial scale of agent intermediaries reaches the trillions of dollars predicted by McKinsey, and those labs hit the walls built into their equity structures—whether or not they actually do—Venice has already become the reasoning layer of this economy.

The arguments for April are no longer speculative. On April 7, daily usage reached 50 billion tokens and 1 million images. GLM-5.1, Kimi K2.6, and DeepSeek V4 all launched on Venice within days of their release, with privacy contracts unchanged. DIEM’s execution discount has been repriced from 57% in early March to approximately 32% today—the market is repricing reliability, not incremental utility. Once the discount falls below 20%, DIEM will cross $1,500 purely through mechanical mathematics. Staking inflows have exceeded $15 million. Over 32 million VVV tokens have been staked, locking up approximately 70% of the circulating supply. The tiered subscription burn mechanism went live in April and is generating significant monthly burns; at current rates, even without the next emission reduction, VVV will turn net deflationary in Q3.

Every judgment in the April article has either been compounded or become even clearer. None have been weakened.

The article in April stated that Venice was the only platform combining seven specific advantages—a judgment that still holds. However, what I didn’t clearly explain at the time was why: these seven advantages are not a stack of features, but rather the natural form of a consumer software company that doesn’t need to meet venture capital equity return requirements. Those venture capitalists are investing in equity based on assets that are about to be commoditized.

There are two possible paths for this market. The first is that these labs are overwhelmed by their own equity structures, and Venice takes over the entire technology stack. The second is market fragmentation—labs retain a small segment of high-end demand willing to pay enterprise prices and accept monitoring, while Venice owns everything else: the larger, faster-growing half of the market, where “good enough” intelligence combines with privacy, uncensored outputs, agent-native access, and user ownership.

The endpoint of both paths is Venice becoming the reasoning layer of an open agent economy. This argument does not require a bubble to burst; it merely requires the open-source curve to continue along the trajectory it has already set—fact is, it does so every quarter, faster than the market updates its models.

Venice is built on this bet. Three months ago, when I made this call at $2, no one was listening. A month ago, when the price reached $8, people started to pay attention. Now at $18, the market still hasn’t fully grasped this structural thesis—the part that hasn’t been priced in is what happens when both scenarios ultimately converge on the same answer.

The bubble is built on the assumption that the model layer will maintain a high premium indefinitely. Venice’s compounding is based on the trend of the model layer moving toward being free. Whether the bubble bursts suddenly or deflates slowly, the outcome of this trade is the same.

The same market. Opposite economic models.

The lab cannot keep up. Miners cannot capture users. The protocol is being handed over to the foundation. Value will ultimately concentrate in a few places, as always: the brands people choose, the tracks agents operate on, and the currencies they use to price things.

Venice is building its brand, operational infrastructure, and issuing currency.

The next chapter is not a celebration. The real question is: Will the structural argument raised in the April article be repriced as venture-backed comparable companies run out of options, or will it be repriced as the market naturally fragments around them?

Based on current evidence, both events are proceeding on schedule.

Not investment advice. Please do your own research.

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