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Today on MCG: @ColbySaysHi | @prism_lp | $PRISM $PRISM is the first token where holding is providing liquidity. Each whole $PRISM you hold auto-mints one Prism NFT (a 1/5000 share of the same Uniswap v4 LP position) INSANE TEK Highlights from our convo: 03:18 - Uniswap v4 launched with a hooks whitelist, hooks didn't take off until UniPet (a viral unicorn NFT mint), then a Prism dev took it further 04:50 - How Prism works 05:38 - Full-range concentrated liquidity means fees accrue regardless of price/market cap 06:25 - Token unit economics 08:00 - Spectrum index tokens 14:32 - Colby's own product 15:00 - 10% of all Spectrum index fees 19:24 - Article release: "Retail is right to hate crypto" 22:18 - Why burn vs distribute 27:30 - PMF 30:00 - Spectrum V2 35:00 - @Uniswap team has reached out to learn how hooks are being used in the wild

🚨 EXCLUSIVE: ADA LLUCH BANNED FROM EUROPEAN PARLIAMENT 1) Conservative political commentator Ada Lluch has been barred from entering the European Parliament after publishing a video from inside the institution that featured members of The Left group. 2) According to a decision signed by Parliament Secretary-General Alessandro Chiocchetti, the incident took place during a March 26 visit to Brussels. 3) Parliament's document says the footage was recorded without a media permit and later posted on Lluch's public X account, making the video itself the central issue in the case. 4) Officials also cited procedural violations, arguing that Lluch was not continuously accompanied by her host MEP or an accredited parliamentary assistant during her visit, despite having been granted access to the building. 5) The ban takes effect immediately and could be extended further after review, meaning a journalist and commentator is now facing exclusion from the European Parliament over a video that Parliament says should not have been recorded.

INTERVIEW: near:native’s AI Money Thesis—Intents, Privacy, & Tokenomics | Sal Ternullo @NEARProtocol keeps showing up in strange places: cross-chain wallets, privacy apps, AI infrastructure, and now the emerging agent economy. @sal_ternullo, CEO of @svrn_ai, joins us to explain why he thinks this is not another NEAR pivot, but the original thesis finally coming into focus. They dig into NEAR Intents, AI money, tokenomics, privacy, fee capture, agentic commerce, and why SVRN is trying to commercialize the NEAR ecosystem rather than simply hold the asset. [TIMESTAMPS] 0:00 Intro 2:23 Why NEAR Keeps Showing Up 3:58 Pivot or Return to Roots? 5:34 The “AI Money” Thesis 7:47 Intents, Zashi, and Real Product Market Fit 9:31 Tokenomics, Buybacks, & Value Accrual 12:39 NEAR’s Different Approach From Ethereum 15:54 Fees, Scaling, and First-Party Apps 20:57 Cross-Chain Liquidity as NEAR’s Moat 25:14 The Contrarian Investor Case for NEAR 28:20 Privacy, Enterprises, and AI Agents 36:25 What SVRN Is Building for NEAR 42:48 Investor Sentiment & Final Thoughts

HIP-4 CPI outcome markets look close to going live on Hyperliquid mainnet. The interesting part: resolution appears to be validator-based. From the current market config, CPI would resolve into exactly one outcome: below 4.3%, exactly 4.3%, or above 4.3%, based on the official BLS CPI print. This also looks like the next phase of HIP-4: canonical, validator-deployed markets first (where the protocol accrues all the value), then permissionless builder-deployed outcome markets later. In other words, HIP-4 may follow a similar path to perps: native markets first, builder-deployed markets later.

Case one: Strategy (Nasdaq: MSTR). In August 2020, Strategy began buying Bitcoin. BTC value per diluted share: ~$2.34. Today, BTC value per share: $164.71 Net of Credit/Preferred Shares: $129.53 That’s ~55× in 5.7 years. Important note: Strategy's average Bitcoin buying price is roughly $75,500. Bitcoin today is roughly $77,000. They've made marginal returns on holding Bitcoin itself, yet net BTC value per share grew ~55×. To deconstruct MSTR’s NAV/Share returns: ~3% came from Bitcoin price going up. ~97% came from MSTR issuing shares above NAV (proceeds were then used to buy more BTC). If Bitcoin dropped 50% to ~$38,500, Strategy would face a large loss on their BTC NAV. Net BTC value per share would be ~$27.7, ~12x greater than the original BTC NAV per share. The company would have lost money on its investments, but retained returns from accretive share issuance. In the scenario that Bitcoin doubled, the net BTC value per share would rise to $294.24. In this scenario, the company would have achieved substantial returns from both its investments and also accretive share issuance.

The decentralized intelligence thesis keeps building. Grayscale Bittensor Trust (ticker: $GTAO) is open for private placement for eligible accredited investors. Learn more and see important disclosures:

From emerging therapies to tactics for multidisciplinary collaboration, this free, CME-accredited video podcast series combines expert insights with the latest scientific data to increase your clinical confidence in the management of generalized myasthenia gravis.

Here's the #1# thing most people don't know about Warren Buffett: There is nothing special about Buffett’s stock picking. That doesn’t mean that Buffett wasn’t a great investor. He was! Buffett was, by far, the greatest investor in history, by a huge margin. Over 486 months between October 1976 and March 2017 –— 41 years –— Berkshire Hathaway’s Class A stock earned an average excess return of 18.6% per year above U.S. Tbills. Annualized volatility was 23.5%. Sharpe ratio: 0.79. Berkshire’s Sharpe ratio of (0.79) is roughly 1.6x times the broad U.S. stock market’s Sharpe ratio of 0.49 over the same period. Among all large-cap U.S. stocks and mutual funds with 30-plus-year continuous track records, those are unmatched numbers. A dollar invested in Berkshire on October 31, 1976, was worth more than $3,685 by March 31, 2017. A dollar invested in the S&P 500 with dividends reinvested over the same period was worth approximately $76. Buffett beat a passive index by a multiple of 48. But he didn’t do it with stock picking! Three researchers at AQR Capital Management –— Andrea Frazzini, David Kabiller, and Lasse Heje Pedersen –— dissected Berkshire’s 50 years of investments through 2013. They expanded and republished their findings in 2018 in the Financial Analysts Journal, which is the most highly respected industry financial journal. Their work won the Graham and Dodd Award for the best published paper of the year. The paper is called Buffett’s Alpha. They found, after accounting for cheap leverage (from the insurance float) and exposure to a handful of publicly documented factor premiums, Buffett’s investment skill –— the portion of his returns that cannot be explained by any mechanical strategy –— is 0.3% per year. That's statistically indistinguishable from zero. In other words, the alpha that Berkshire enjoyed for 50 years (as it compounded capital at 24% a year!) wasn’t due to Buffett’s stock picking. So, how did he do it? He did it by gaining access to a huge amount of investment capital that he did not own, for free. Buffett’s track record was built on leverage. That’s a dirty word for most investors, but it's the secret behind Berkshire. The AQR researchers had access to something most Buffett commentators do not: 40 years of Berkshire’s audited financial statements and the full quarterly history of the public 13F stock portfolio. The researchers asked a specific question: If I take Berkshire’s monthly stock returns from October 1976 through March 2017, and I run a linear regression against a set of well-documented risk factors –— market beta, size, value, momentum, and two newer factors called Betting-Against-Beta and Quality-Minus-Junk (detailed below) –— how much of Buffett’s performance can the factors explain? And after the factors have been stripped out, how much excess return remains? The data show clearly there are a few qualities that drove Berkshire’s results. First, Buffett has always preferred large-cap stocks, contrary to the popular image of him as a small-cap value investor. He buys elephants. Second, no surprise, Buffett buys cheap. Berkshire is almost six standard deviations away from neutral on the value axis. So far the picture is ordinary. Every large- cap value manager in America loads positively on size and on value. Buffett’s genius lies in the last two factors. These last two factors are a little complicated, but please stick with me. There’s a new factor, that, like value and size, characterizes Buffett’s strategy. It’s called Betting-Against-Beta (“BAB”). What it means is intentionally investing in stocks with very low volatility. The BAB factor captures the excess return that accrues to investors who own low-beta stocks. Low-beta stocks have historically earned higher risk-adjusted returns than high-beta stocks. Financial theory teaches that higher beta (higher risk) should mean higher return. But it doesn’t. The opposite occurs, in fact. And Buffett was one of the very first people to figure this out. Why does this factor persist? In an efficient market, once that factor is known to investors, then they should bid the price up on low- beta stocks until it no longer provides an edge. The explanation, per the theory of AQR’s Frazzini and Pedersen’s theory, is that because ordinary investors do not use leverage and seek high returns, they create persistent excess demand for more volatile stocks. (Having worked with retail investors for 30 years, I can assure you that is true.) But, an investor with access to cheap leverage –— Warren Buffett, for instance –— can exploit the mispricing by owning the low-beta names and levering them up to produce market-beating returns. And the last factor that matters to Buffett is quality. Buffett buys companies with high returns on invested capital. Quality-Minus-Junk (“QMJ”) is a factor described by Cliff Asness, also at AQR with Frazzini, and Pedersen, in a 2019 paper in Review of Accounting Studies. The QMJ factor captures the return to owning stocks of high-quality companies –— profitable, growing, safe, with high payout ratios –— against stocks lacking those characteristics. QMJ has been positive and statistically significant in every major developed equity market for which it has been measured. Berkshire’s loading is 0.37, with a t-statistic of 4.6. –– meaning it is highly significant to Berkshire’s results. In plain English: Buffett only buys large, high- quality, low-volatility stocks of the highest quality. But, Berkshire’s results were not, in any way, unusual. Any investor buying these same kinds of stocks would have earned those same returns –– about 16% a year over time. So how did Berkshire compound at 23% a year? To figure that out, AQR’s researchers built a Berkshire replica. They constructed a simple, rules-based, publicly investable portfolio that mechanically tilts toward large-cap, cheap, low-beta, high-quality stocks, and levers it 1.6- to- 1 to match Berkshire’s insurance float leverage. The correlation between their replica’s returns and Berkshire’s were virtually identical. The authors’ conclusion is unambiguous. “In summary, we find that Buffett has developed a unique access to leverage that he has invested in safe, high-quality, cheap stocks and that these key characteristics can largely explain his impressive performance.” Berkshire’s cost of insurance float has averaged almost three percentage points below the Treasury bill rate across 50fifty years of data. In roughly two-thirds of all years, Berkshire has been paid to hold other people’s money. That is not an investment strategy. That is a financing miracle. It is also the living, breathing heart of Berkshire Hathaway. It’s what Buffett built, starting in 1967 when he paid $8.6 million for National Indemnity’s $19.4 million of float. And it is the factor every retail investor admiring Berkshire’s returns has never paid any attention to. The 1.6-to-1 leverage that AQR measured over the full period, financed at this negative cost, explains the dollar magnitude of Berkshire’s returns. How do we know? An unleveraged version of the same stock portfolio –— which you can approximate by looking at the 13F holdings alone –— has earned an average excess return of 12% percent per year. It’s Berkshire’s leverage that magnifies this excess return to 18.6 %percent. How does this square with Berkshire’s reported gains? Berkshire’s 18.6% excess return, plus the T-bill rate that averaged roughly 4.7% over 1976–2017, gives you a total nominal return of roughly 23% per year, which is the figure you usually see quoted for Berkshire’s historical performance. The 23% tells you what Berkshire returned. The 18.6% tells you how much of that return was compensation for taking investment risk, as opposed to the baseline yield every lender to the U.S. government was earning anyway. With both of Berkshire’s “edges” –— systematic factor exposures to cheap, high-quality, low-volatility stocks and roughly 1.6-to-1 leverage delivered with insurance float –— you get Berkshire Hathaway’s 23% annual gains over 60 years. It’s the structure that’s genius, not the stock picking. And that's very important because it means the original Berkshire formula can work for any investor. I show you exactly how, in my new book.