FinTwit Tycoon, Father to Annie
New York Stock Exchange
Joined December 2017
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Warbucks retweeted
TLDR; we will probably never get another ARR number from Anthropic, which is healthy. Should force the market to move away from extreme focus on Anthropic/OpenAI ARR numbers to hyperscale growth rates, which are a much better metric for AI at this point given they capture Muse, Grok, Gemini and Open-Source.
Anthropic FCF positive in Q3 and reaccelerating in October on Opus 5.5 per third party data. Roadshow in several weeks will happen after this has been widely confirmed. Unit economics much better than expected given positive FCF with 75% of their compute going to training. All per public reporting.
OpenAI continuing to grow at the same pace per Sarah Friar interview after they took share vs. Anthropic in Q3. Means the sum of Anthropic/OpenAI growth is accelerating from the Q3 pace. OpenAI probably hits FCF positive in the next few months.
Open-source growing much faster than either Anthropic or OpenAI. Modal tripled since May and is 15x YoY per yesterday’s announcement. Open-source tokens generate more revenue and profit for the hyperscalers/neoclouds than frontier tokens. *Every* open source inference cloud I am aware of is growing faster both sequentially and YoY than either Anthropic or OpenAI. And their combined compute spend is becoming material.
Third party data pointing to hyperscalers continuing to accelerate in Q3. All report in the next two weeks. Acceleration should continue in Q4 and Q1 as installed base of compute slowly reprices higher closer to spot and new capacity comes online at higher prices. Continued acceleration in hyperscalers should help address external funding concerns.
10 year yield down in a straight line over the last 24 hours.
Some risk of escalation in Iran next few days, but earnings and mid-terms probably outweigh taking an ultra long-term view and looking out 30 days.
Biggest risk imo remains cyber and OpenAI/Anthropic dominating the model layer. Fragmentation, competition and lower margins at the model layer are good for everyone else in the ecosystem and for America.
Slower for longer is much better than an overbuild and an actual bubble.
Warbucks retweeted
This model is very very good. I was struggling to find anything to throw at it that it couldn’t do, even when other top models failed. Meta is back.
Warbucks retweeted
AI infra is indeed better served with many, many players competing to create more tokens
Isn’t this just a misunderstanding? Anthropic and openAI revenue is not comparable. I don’t think the 70 billion number was ever correct. I believe it was an attempt to have an apples to apples comparisons against anthropic who overstates their revenue
Nothing has changed
Warbucks retweeted
BREAKING: The 20+ Year Treasury Bond ETF, $TLT, attracted +$1.7 billion in inflows last week, the 4th-largest weekly inflow since June 2024.
This follows +$5.6 billion in inflows in August, the largest monthly inflow on record.
Furthermore, US Treasury funds attracted +$11.0 billion in inflows last week, their 3rd-biggest weekly inflow this year.
And, US long-term government and corporate bond funds posted +$7.4 billion in inflows, their largest weekly intake since May 2025.
Meanwhile, municipal bond funds attracted +$4.2 billion, their largest weekly inflow in data going back to 2004.
Investors are piling into bond funds at a historic pace.
Warbucks retweeted
Costco founder Jim Sinegal and Costco CEO Ron Vachris finding out that AI chatbots know the value of a Costco membership and have been regularly recommending them to users
Market is focused on Muse, while ignoring AI tailwinds to the core $META
Ben Thompson says Meta and Google are making more money using AI to improve their ads than the labs are from selling it:
"The biggest monetization right now is probably not in Anthropic or OpenAI. It's the incremental gain that is happening for Google and Meta."
"They can actually validate their image creation and their text creation in a way no one else can. Their validation is the ad marketplace, running a gazillion A/B tests to see what works. Most ads don't convert."
"What do LLMs do? LLMs predict. We're going to move to this world where Meta is going to look at people and say, this person probably wants to see this next. And they're going to go find that thing and show it to them."
"The potential upside in terms of just showing people better ads that are more relevant to them, they only need to increase a few percentage points for the returns to be billions and billions of dollars. This alone is worth them investing in being on the leading edge."
It’s completely fair they raised prices fwiw
Now cost the same as the credit bureaus for a credit package
We had a good thing, you stupid son of a bitch. We had the mortgage. We had the score. We had the one number every lender in America was too scared to close a loan without.
Thirty years. Thirty years, nobody could touch you.
You could’ve shut your mouth, raised prices bit by bit, and made as much money as anyone ever needed. It was perfect.
But no — you had to jack it 17x in five years. You and your pride and your earnings calls. You just had to squeeze every drop. You just had to be the man.
If you'd known your place and let the pennies roll in, we'd all be fine right now.
Instead, VantageScore's through the door, the FHFA's circling, and the whole industry's finally asking why they ever needed you at all.
$FICO
Warbucks retweeted
We had a good thing, you stupid son of a bitch. We had the mortgage. We had the score. We had the one number every lender in America was too scared to close a loan without.
Thirty years. Thirty years, nobody could touch you.
You could’ve shut your mouth, raised prices bit by bit, and made as much money as anyone ever needed. It was perfect.
But no — you had to jack it 17x in five years. You and your pride and your earnings calls. You just had to squeeze every drop. You just had to be the man.
If you'd known your place and let the pennies roll in, we'd all be fine right now.
Instead, VantageScore's through the door, the FHFA's circling, and the whole industry's finally asking why they ever needed you at all.
$FICO
Warbucks retweeted
It will be hard for anyone to compete with $META Muse. You either compete on the product, distribution or price.
$META is really strong in all three categories:
- The product reviews for Muse are 40k ratings with a 4.88 score (truly rare) telling you users love the product.
- In terms of distribution, $META owns the biggest ecosystem by far.
- In terms of price $META has the most spare infra capacity + enough profits from its core to subsidize if needed.
Warbucks retweeted
Open models continue taking share. Not just tokens, more $ now spent on open models than OpenAI.
Positive for the AI infra trade.
Open models taking share shift $ margin from the model layer to the infra and app layers.
Looks like today may be a record day for token volume % of open models on Vercel AI Gateway:
🟦 Open 78.4% 🟨 Closed 21.6%
While spend 💲 usually tells a different story, #3 and #4 today are Moonshot AI & DeepSeek. Adding Z.ai, their combined spend surpasses OpenAI (#2).
(Do note that's the spend for inference of the model across providers (mostly in the US), not revenue going directly to the open weight labs.)
I showed Muse a picture of damaged exterior to my home. It taught me how to fix the issue, bought everything on Lowe’s for me (5 products), and found $50 in discounts, lowering the bill by 30%. I saved time and money and will actually do the project finally
$META @alexandr_wang
Warbucks retweeted
$META Bypassing RevenueBench to Buy the Feedback Loop
full piece: mbi-deepdives.com/revenueben…
doubt this disrupts $FICO, just changes the growth algo
Bi merge likely to play out like VS. There are downstream costs from gaming the system that outweigh cost savings of doing bi merge and/or VS
FICO & bureaus represent 30-40% of cost of credit package, seems fair now
Pulte: 'cartel-like' bureaus should cut costs, eyes bi-merge | National Mortgage News nationalmortgagenews.com/new…
Warbucks retweeted
i really hate to say it, but…
gemini who? 🏎️💨
Meta has released Muse Spark 1.3, their fourth Muse Spark model release in five months. Muse Spark 1.3 (max), which is in limited preview for Meta’s partners, scores 62 on the Artificial Analysis Intelligence Index, behind only Claude Fable 5.1 and Claude Opus 5. The variant available now, Muse Spark 1.3 (xhigh), scores 61 and ties with GPT-5.6 Sol (max) and Grok 4.6 (high). Both variants’ gains come primarily from improvements in agentic work and scientific capabilities
Muse Spark 1.3 (xhigh) enters the Artificial Analysis Intelligence Index at 61, up 4 points from Muse Spark 1.2 (57, August) and 8 points from Muse Spark 1.1 (53, July). It enters tied with GPT-5.6 Sol (max), Grok 4.6 (high), and Claude Opus 5 (high), and behind Claude Fable 5.1 (max, 66), Claude Opus 5 (max, 63), and Claude Fable 5 (max, 62)
Muse Spark 1.3 (max), which is in a limited preview stage, lands at 62. This higher index score is enabled by gains vs. Muse Spark 1.3 (xhigh) in Tau3-Bench Banking (52% vs. 47%) and GDPval-AA v2 (1,754 Elo vs. 1,709). Muse Spark 1.3 (max) is second only to Claude’s Fable and Opus variants in total score
Congratulations to @AIatMeta, @finkd, and @alexandr_wang on the release!
Key Takeaways:
➤ Continued improvement on agentic knowledge work tasks. At the launch of Muse Spark 1.2, we noted its significant gains in agentic knowledge work performance vs. Muse Spark 1.1. The latest iteration continues this trend, with Muse Spark 1.3 (xhigh) demonstrating a notable 12-point gain vs. Muse Spark 1.2 in Tau3-Bench Banking (35% to 47%), a 5-point gain in Terminal-Bench 2.1 (80% to 85%), and a new GDPval-AA v2 Elo of 1709 against its predecessor’s 1615. Muse Spark 1.3 (max) improves further on Tau3-Bench Banking (52%) and GDPval-AA v2 (1,754 Elo). This Tau3-Bench Banking score is #1 among all models. Muse Spark 1.3 (max) achieves these higher agentic work scores by using more turns and total reasoning tokens, reasoning 62% more on GDPval-AA v2 and 28% more on Tau3-Bench Banking compared to Muse Spark 1.3 (xhigh)
➤ The lowest cost per task for any model at 59+ on the Artificial Analysis Intelligence Index. Muse Spark 1.3 (xhigh) costs $0.55 per Intelligence Index task at Meta's unchanged $1.25/$4.25 per 1M token pricing ($0.15 for cached input), with its peers GPT-5.6 Sol (max) and Grok 4.6 (high) costing $0.95 and $0.94 respectively, a 70%+ premium. This places Muse Spark 1.3 (xhigh) on the Pareto frontier for Intelligence vs. Cost per Task. Its cost per task is higher than Muse Spark 1.2 ($0.40 per task), driven by ~57% more input tokens per task on agentic evaluations, with output tokens up only ~8%. Pricing for Muse Spark 1.3 (max) is not yet publicly available
➤ Scientific Reasoning results rose across the board, led by CritPt. CritPt was the standout non-agentic score gain vs. Muse Spark 1.2, with a material +8 points for the xhigh variant (18% to 26%), and GPQA Diamond achieved +4 points (90% to 94%), while Humanity’s Last Exam and SciCode each gained a more modest 2-3 points (45% to 47% and 56% to 59%, respectively). Muse Spark 1.3 (max) achieved roughly similar scores to the xhigh variant, gaining 2 points in Humanity’s Last Exam, tying on GPQA Diamond, and losing a point on CritPt vs. Muse Spark 1.3 (xhigh)
➤ Minor regressions in only two evaluations. Both Muse Spark 1.3 (xhigh) and Muse Spark 1.3 (max) dropped 4 points in AA-LCR (83% to 79%) when compared to Muse Spark 1.2, and AA-Omniscience (Accuracy) fell 3 points for xhigh and 1 point for max. The drops in AA-Omniscience (Accuracy) are due to a higher abstention rate (not answering questions when unsure), which also lowered the hallucination rate for Muse Spark 1.3 (xhigh)
Other model details (xhigh variant):
➤ Context window: 1M tokens, unchanged from Muse Spark 1.2
➤ Pricing: unchanged from Muse Spark 1.2: $1.25/$4.25 per 1M input/output tokens, with cache hits discounted to $0.15 per 1M
➤ Input modalities: text, image, video
➤ Availability: Meta's first-party API and Muse Code
