i also like to add these other unconventional projects to this real yield / revenue generating narrative:

$CC
: $60m in the last 30d, rank number 3 behind tether and circle

$CARDS
: $14m in the last 30d, $8.6m gross profit, 85 to 90% buyback floor

$SKY
: $12.7m in the last 30d, ranks the 3rd most protocol that pays its token holders

$ZINC
: $5m in the last 30d, launched recently, revenue 3.5x higher in the last 7 days. gamified private proof of work mining protocol on Solana

$WLFI
: $11m in the last 30d, trump mentioned that they would pay unfreeze Iran's assets in USD1, which would be interesting to see how this would play out

@Polymarket
: $22m in the last 30d, this figure would grow exponentially as the worldcup goes on

$HYPE
: $63m in the last 30d, pays the most to its holders and needless to say, its everyone favourite

https://x.com/arndxt_xo/status/2069118607792173529
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I hate to rain on everyone’s parade, because I really do believe in tokenizing financial securities on general purpose blockchains. But these stats everyone keeps posting about massive onchain stock volume deserve context.

The *vast* majority of onchain stock volume is coming from a handful of AMM pools with swap fees essentially set to zero. That makes it dirt cheap to trade, despite these pools having <$1mn of liquidity.

Within these pools, 95%+ of the volume is coming from around 10 bots that just flip a small position back and forth again and again

Again, not trying to say onchain equities aren’t a very important development. But any time you see a chart like this for any sort of metric in crypto you should drill down into the data. Almost all crypto stats don’t mean what you think they mean at first glance

https://x.com/0xdoug/status/2069484171735515414
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Analyzed 10k borrowers in Morpho's top markets by debt. The same protocol is serving 3 personas:
- loopers
- liquidity borrowers
- directional leverage traders

most markets are loop-heavy, with the top 5 borrowers accounting for most of the debt

the great unbundling might be coming

https://x.com/0scaronchain/status/2069428261315280903
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the chinese AI models are dominating, along with the obvious split of the AI model market into 2 very different games

the first is revenue

the second is volume

OpenAI, Anthropic and Google still dominate revenue because enterprises buy trust, support, security, procurement, and integration. that part of the market moves slowly and is heavily shaped by cloud GTM, solution architects, and existing enterprise relationships

but OpenRouter is showing the leading edge of volume

by may 2026, chinese open-weight models were roughly 61% of tokens consumed on OpenRouter. four of the top five models were Chinese. Llama, once the default open-weight leader, has effectively disappeared from the top rankings

the cost-performance center of gravity for open-weight inference has shifted east

DeepSeek-V4-Pro pricing at roughly 12x below GPT-5.5 makes the point clear. the mass market does not always pay for the absolute frontier. it pays for the cheapest model that is good enough for the task

openRouter usage shifted heavily toward code, with programming rising from roughly 11% of usage at the start of 2025 to more than 50% by mid-2026. coding is high-volume, repeatable, and price-sensitive. that is exactly where cheap, capable open-weight models compound

premium reasoning is a high-margin niche. cheap, open, good-enough inference is becoming the volume layer

if the best open weights are increasingly Chinese, large enterprises will hesitate. export controls, procurement risk, data sensitivity, and political optics matter far more to a Fortune 500 buyer than to a startup trying to cut inference cost

so the market likely bifurcates, where startups chase performance per dollar, while enterprises stay with approved Western vendors longer:

- that delay creates a temporary distortion, not a permanent moat

- the real investment implication is that margin is moving away from the model layer

- it accrues above the model through distribution, workflow ownership, and application lock-in

- it accrues below the model through cloud, inference routing, optimization, and compute infrastructure

the model itself still matters, but in the volume tier it is becoming increasingly replaceable


https://x.com/arndxt_xo/status/2069994796048158939
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