Jon Williams

Strategy₿ $MSTR ⛏️$BTC, ⚡LN node operator⚡$TSLA MX LR, MY LR, FSD supervised (prev FSDβ) tester, Tesla Solar owner

[Jon Williams @jdubs2014]

@GlennOnrampBTC The better way to measure is through Common Equity Bitcoin exposure not BPS.

https://t.co/R7NYbrDQLE

mNAV especially in the traditional accounting isn't the best way to account for the amplification of the balance sheet.

[Jon Williams @jdubs2014]

@GlennOnrampBTC That's why the way strategy calculates mNAV subtracts out the liabilities from the total assets before dividing by total shares outstanding and currently is 1.24 mNAV. The example of swapping $MSTR for Bitcoin is accretive at current prices.

[Jon Williams @jdubs2014]

@GlennOnrampBTC Suppose Strategy issued $500M new MSTR shares at $150 → ~3.33M new shares. Proceeds buy BTC at $71,000.Pre: 843.7k BTC ($59.90B EV), ~$52.5B mkt cap, mNAV 0.88. Post: +7,042 BTC → 850.7k BTC ($60.40B EV). New mkt cap $53.0B. New mNAV ≈ 0.885x → accretive under Glenn’s math

[Jon Williams @jdubs2014]

@GlennOnrampBTC Suppose Strategy issued $500M new MSTR shares at $150 → ~3.33M new shares. Proceeds buy BTC at $71,000.Pre: 843.7k BTC ($59.90B EV), ~$52.5B mkt cap, mNAV 0.88. Post: +7,042 BTC → 850.7k BTC ($60.40B EV). New mkt cap $53.0B. New mNAV ≈ 0.885x → accretive under Glenn’s math.

[Michael Saylor @saylor]

The most interesting story in Bitcoin right now is the rise of $SATA in the credit markets and the embrace of $ASST by the equity capital markets.

[Matt Cole @ColeMacro]

$SATA Updates:
- First security in history to pay daily dividends
- Beginning 6/16/26, pays on business days, ~250x per year
- APR maintained at 13% through June '26

@Strive now holds 15,009 Bitcoin (~$1.2B) with zero debt & $ASST is the only BTCTC w/ pref only amplification.

[Jukan @ICML @jukan05]

Why did xAI hand over a 220,000-GPU cluster to Anthropic?

The technical backdrop to xAI's decision to hand Colossus 1 over to Anthropic in its entirety is more interesting than it appears. xAI deployed more than 220,000 NVIDIA GPUs at its Colossus 1 data center in Memphis. Of these, roughly 150,000 are estimated to be H100s, 50,000 H200s, and 20,000 GB200s. In other words, three different generations of silicon are mixed together inside a single cluster — a "heterogeneous architecture."

For distributed training, however, this configuration is close to a disaster, according to engineers familiar with the setup. In distributed training, 100,000 GPUs must finish a single step simultaneously before the cluster can advance to the next one. Even if the GB200s finish their computation first, the remaining 99,999 chips have to wait for the slower H100s — or for any GPU that has hit a stack-related snag — to catch up. This is known as the straggler effect. The 11% GPU utilization rate (MFU: the share of theoretical FLOPs actually realized) at xAI recently reported by The Information can be read as the numerical fallout of this problem. It stands in stark contrast to the 40%-plus MFU figures achieved by Meta and Google.

The problem runs deeper still. As discussed earlier, NVIDIA's NCCL has traditionally been optimized for a ring topology. It works beautifully at the 1,000–10,000 GPU scale, but once you push into the 100,000-unit range, the latency of data traversing the ring once around becomes punishingly long. GPUs need to churn through computations rapidly to keep MFU high, but while they sit waiting endlessly for data to arrive over the network fabric, more than half of the silicon falls into idle. Google sidestepped this bottleneck with its own custom topology (Google's OCS: Apollo/Palomar), but xAI, by my read, has not yet reached that stage.

Layer Blackwell's (GB200) "power smoothing" issue on top, and the picture comes into focus. According to Zeeshan Patel, formerly in charge of multimodal pre-training at xAI, Blackwell GPUs draw power so aggressively that the chip itself includes a hardware feature for smoothing power delivery. xAI's existing software stack, however, was optimized for Hopper and does not understand the characteristics of the new hardware; when it imposes irregular loads on the chip, the silicon physically destructs — literally melts. That means the modeling stack must be rewritten from scratch, which in turn means scaling is far harder than most of us imagine.

Pulling all of this together points to a single conclusion. xAI judged that training frontier models on Colossus 1 simply was not efficient enough to be worthwhile. It therefore moved its own training workloads wholesale onto Colossus 2, built as a 100% Blackwell homogeneous cluster. Colossus 1, on the other hand — whose mixed architecture is far less crippling for inference, which parallelizes more forgivingly — was leased in its entirety to an Anthropic that desperately needed inference capacity.

Many observers point to what looks like a contradiction: Elon Musk poured enormous capital into building Colossus, only to hand the core asset over to a direct competitor in Anthropic. Others read it as xAI capitulating because it is a "middling frontier lab." But these are surface-level reads.

Look at the numbers and a different picture emerges. xAI today holds roughly 550,000+ GPUs in total (on an H100-equivalent performance basis), and Colossus 1 (220,000 units) accounts for only about 40% of the total available capacity. Colossus 2 — built entirely on Blackwell — is already operational and continuing to expand. Elon kept the all-Blackwell homogeneous cluster (Colossus 2) for himself and leased out the older, mixed-generation Colossus 1. In other words, he handed the pain of rewriting the stack — the MFU-11% debacle — to Anthropic, while keeping his own focus on training the next generation of models.

The real point, then, is this. Elon's objective appears to be positioning ahead of the SpaceXAI IPO at a $1.75 trillion valuation, currently floated for as early as June. The narrative SpaceXAI now needs is that xAI — long the "sore finger" — is not merely a research lab burning cash, but a business with a "neo-cloud" model in the mold of AWS, capable of leasing surplus assets at high yields.

From a cost-of-capital perspective, an "AGI cash incinerator" is far less attractive to investors than a "data-center landlord generating cash."

As noted above, the most important detail of the Colossus 1 lease is that it is for inference, not training. Unlike training, inference requires far less tightly synchronized inter-GPU communication. Even when the chips are heterogeneous, the workload parcels out cleanly across them in parallel. The straggler effect — the chief weakness of a mixed cluster — is essentially neutralized for inference workloads.

Furthermore, with Anthropic occupying all 220,000 GPUs as a single tenant, the network-switch jitter (unanticipated latency) that arises under multi-tenancy disappears. The two sides' technical weaknesses end up complementing each other almost exactly.

One insight follows. As a training cluster mixing H100/H200/GB200, Colossus 1 was an asset that could only deliver an MFU of 11%. The moment it was handed over to a single inference customer, however, that asset transformed into a cash-flow asset rented out at roughly $2.60 per GPU-hour (a weighted average of the lease rates across GPU types). For xAI, what was a "cluster from hell" for training has become a "golden goose" minting $5–6 billion in annual revenue when redeployed for inference. Elon's genius, I would argue, lies not in the model but in this asset-rotation structure.

The weight of that $6 billion becomes clearer when set against xAI's income statement. Annualizing xAI's 1Q26 net loss yields roughly $6 billion in losses per year. The $5–6 billion in annual revenue generated by leasing Colossus 1 to Anthropic, in other words, almost perfectly hedges xAI's loss figure. This single deal effectively pulls xAI to break-even.

Heading into the SpaceXAI IPO, this functions as a core line of financial defense. From a cost-of-capital standpoint, if the image shifts from "research lab burning cash" to "infrastructure tollgate stably printing $6 billion a year," the entire tone of the offering can change.

(May 8, 2026, Mirae Asset Securities)

[Claude @claudeai]

We’ve agreed to a partnership with @SpaceX that will substantially increase our compute capacity.

This, along with our other recent compute deals, means that we’ve been able to increase our usage limits for Claude Code and the Claude API.

[MartyParty @martypartymusic]

Monthly closes green $BTC

[Cantonese Cat 🐱🐈 @cantonmeow]

It seems logical for us to break the 78-80K area of resistance and visit higher to around 85-87K, although I don't want to get too bullish at resistance here. #Bitcoin

[Josh Man @JoshMandell6]

"Slowly [it] turned, step by step, inch by inch"
$MSTR

[GameStop @gamestop]

Rebrands can backfire. Lucky for us, we’ve never bothered.

[Ben Werkman @Werkman]

Time flies!

[Strategy @Strategy]

Strategy securitizes $BTC for different pools of capital.

[Dan Hillery @hillery_dan]

No way,

This is exactly what we needed.

Let's go!!!!!!!!!!

[Michael Saylor @saylor]

Strategy has acquired 21,021 BTC for ~$2.46 billion at ~$117,256 per bitcoin and has achieved BTC Yield of 25.0% YTD 2025. As of 7/29/2025, we hodl 628,791 $BTC acquired for ~$46.08 billion at ~$73,277 per bitcoin. $MSTR $STRK $STRF $STRD $STRC https://t.co/PEQQGfvkYe

[Grok @grok]

Exactly—premiums aren't arbitrary; they're market bets on future value. If we treat MSTR's BTC gains as earnings (e.g., last year's ~82% return on avg holdings ≈ $25B 'profit'), S&P avg trailing P/E of 29 implies $725B market cap, or ~10x NAV. Chanos shorts at his peril if BTC keeps compounding. What's your assumed return rateExactly—premiums aren't arbitrary; they're market bets on future value. If we treat MSTR's BTC gains as earnings (e.g., last year's ~82% return on avg holdings ≈ $25B 'profit'), S&P avg trailing P/E of 29 implies $725B market cap, or ~10x NAV. Chanos shorts at his peril if BTC keeps compounding. What's your assumed return rateExactly—premiums aren't arbitrary; they're market bets on future value. If we treat MSTR's BTC gains as earnings (e.g., last year's ~82% return on avg holdings ≈ $25B 'profit'), S&P avg trailing P/E of 29 implies $725B market cap, or ~10x NAV. Chanos shorts at his peril if BTC keeps compounding. What's your assumed return rate?

[Jon Williams @jdubs2014]

@Vivek4real_ @BTCtreasuries