#MetaMuseWonderUp3%
About MetaMuseWonderUp3%
Meta launched Muse Wonder Oct 12 for complex tasks, coding and long-term memory, competing with OpenAI and Anthropic. It is available via Meta Model API and Muse Code, with some paid features; Muse assistant integration will follow. Shares rose over 3%; analysts later raised targets. Gains cannot be tied solely to the launch. Meta sees 2026 capex at $130B-$145B. Adoption and revenue impact remain unproven. Ahead of Q3, can it improve ad efficiency and grow subscriptions to support AI spending?
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In our health workstream at Meta we did a project where we wanted to see what research papers medical experts could write using a Muse Wonder coding scaffold that could access a database of 5M electronic health records we licensed from a vendor. The goal is that with 5M patients in this database, AI could do research to answer arbitrary questions like “did people who take statin have higher rates of diabetes later in life”? The thing we were betting on here is on what happens when you put human experts, a strong coding model, and access to data all together
We worked with a small group of medical experts to help them use this AI tool and the punchline is that they all found that this setup enabled a much faster pace of health research. They generated ten manuscripts in around two weeks, with one researcher (Paddy Ssentongo) generating three. Throughout the process, they collaborated with each other to review manuscripts and write skill files to help our coding scaffold improve at doing research. The medical experts liked how quickly they could test different hypotheses and choose the best ones to continue working on
Health research is pretty unique in that it is super important to be able to interpret noisy data and have taste to know which variables are important, which was still a significant value add from human researchers. Given that medicine is one of the oldest fields in human history and it is ultimately about humans, keeping a substantial human component in this type of research is probably important. In the near future I see AI primarily as a tool to facilitate human-led research. With the right scaffold I believe health researchers can be 10x or even 100x more productive using AI tools that have appropriate access to the wealth of data that the world has collected about human health
One way to see this work is through the broader lens of AI for science. You can think of science as two parts: (1) collecting raw data about the world, and (2) synthesizing that raw data into understanding. In this project we already had existing data about the world and we aimed to accelerate part (2) using AI. Eventually, I imagine a world where we have such great AI tools that the time to synthesize raw data into understanding is driven towards zero. In that world, science will be substantially faster and most of the work will be focused on collecting the right raw data via physical experiments
We did this in a pretty short amount of time (which was the point) and I think the future of scientific research will be more faster and more exciting than ever
AI at Meta
We’re exploring how AI can accelerate health science research.
While AI has made significant breakthroughs in formal sciences like mathematics, health data science depends on imperfect real-world data and consequential choices about cohorts, variables, and causal assumptions.
To explore how AI can assist health researchers, we built a human-led research scaffold powered by Muse Spark 1.3 and Muse Wonder with access to 5M retrospective de-identified electronic health records and publicly available data. A cohort of medical experts used the system to conduct original research, accelerate hypothesis generation, code execution, and manuscript drafting while maintaining full authority over scientific decisions.
Today, we’re sharing 10 research manuscripts produced through this exploration, spanning cardiology, oncology, infectious disease, emergency medicine, and more.
Read more in our research blog:
🌐 Mark Zuckerberg Is Taking $META AI Game to the Next Level! 🧠
Meta is pushing deeper into artificial intelligence with its new Muse Wonder models, designed to do much more than simply answer questions.
⚙️ Imagine an AI that can write code, solve complex problems, analyze financial documents, and handle multi-step tasks.
That’s the direction Meta is heading!
📊 Why does this matter?
Mark Zuckerberg wants AI to become a bigger part of Meta’s future. If these tools deliver real value, they could open new growth opportunities for the company.
💸 But there’s a challenge: AI development costs billions, and investors want to see real results.
🔎 Can Zuckerberg turn Meta’s massive AI investment into long-term growth?
The next chapter of Meta’s AI journey will be worth watching!
#Meta #MarkZuckerberg #ArtificialIntelligence
Every model on @Meta's MemoryBench chart lands within about 7 points of each other. Different labs, same result: memory is where the work is now.
Meta leading Muse Wonder with “Active Memory” is the latest proof.
One question none of the benchmarks ask: whose memory is it, and can you take it with you? 📷
Every lab is building memory into its own model. Great if you run one agent on one stack. But most real stacks don’t. You build across models, and each one remembers you in its own silo. Switch models, start over.
Walrus Memory is the other half: a portable memory layer that sits outside any single model.
Context that follows your agent across sessions, models, and runtimes.
@AIatMeta
JPMorgan already blocks outside AI agents like Meta's Muse from customer accounts.
Jamie Dimon said so on yesterday's earnings call.
This was inevitable.
Let the agent wars begin!
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The block covers any third party logging into a Chase account with the customer's credentials to pay bills or move money.
However.
Read-only data through Plaid still works. There's no timeline for letting agents act.
Dimon called them "rogue agents" and warned customers of a "liability transfer" when they hand an agent their logins. He also told regulators to prepare for what agents could do to deposits.
JPMorgan is building its own in the meantime.
Smart Cash is in pilot and moves money between your Chase accounts for yield.
The big firms will block first and ask questions later.
- Amazon blocked Muse last month with a list of complaints that read like a term sheet: tell us, identify the agent, don't store our customers' logins.
- JPMorgan's statement is similar.
I think it's sensible as a starting point to say. Not all agents can have unfiltered access.
But
Consumer agents are inevitable. You can't block everything forever.
Security is everything. And it will happen on the banks' terms, because they remember open finance.
That ended a 2-2 draw. Screen scraping got fintech in, then JPMorgan charged Plaid for the data and the fees stuck.
Expect the same playbook, faster. The bank checks which agent it is and what the customer delegated, keeps logins out of it, then charges for the connection.
The risk for banks is you keep the account and lose the customer. Your salary still lands at Chase while your agent shops for your next card, savings rate and insurance policy somewhere else.
So block the scraper. Then be the thing their agent uses. Give it a permissioned way in, read-only first, with a step-up in your own app before any money moves.
I don't want your agent. I want my agent to use your thing.
I wrote about this twice:
Agent Wars Pt 1, Amazon vs Muse:
Agent Wars Pt 2, The agentic bank run?
CNBC:
Dan Ives (@DivesTech) says Meta ($META) is a “core name to own” as AI shifts from models to agents.
He says Meta’s frontier AI model and 3.6B daily users give it an edge as it chases OpenAI and Anthropic.
Truist now sees Muse adding $28.5 BILLION in incremental revenue for $META by 2030
That's the base case behind its Street-high $1,000 price target
"With AI infusing higher performance across ads, wearables and Muse (building non-ad revenues from powering personal superintelligence, Muse for Enterprise/SMBs), we see Meta as an AI winner long-term"
For Q3, Truist expects $63.9 billion in revenue, up 25% YoY, at the high end of guidance and above the $63.3 billion consensus
It also expects ad prices to rise double digits again, helped by Advantage+, with growth "increasingly driven by monetization and retention rather [than] net new users"
Kyle Reidhead | Milk Road
$META just got two big price target raises: Truist to $1,000 from $763 and Pivotal to $930 from $790
Truist sees Meta as a long-term AI winner, with strong engagement and monetization supporting results at the high end of expectations
Pivotal says AI returns are coming into focus.
Muse hit 2.8M installs in its first 12 days, faster than ChatGPT's early mobile launch, and they see Muse Wonder at or near parity with the leading frontier labs
Zuckerberg said something interesting
He openly admitted that Meta had to commit to spending 100 to 200 billion USD on AI infrastructure before the model was even validated. At that time, even Alexandr Wang, who was in charge of AI, couldn't guarantee success. The CFO repeatedly warned that failure would be disastrous. But Zuckerberg said, "I have to do it," a typical case of being forced into it.
Meta will release its Q3 earnings on October 21, and the market is watching to see
#DailyOrbit
Cheeky Muse* is back! Agrippa Zuckerberg @finkd and Euler Wang @alexandr_wang came to the pub to discuss their new frontier model, Muse Wonder.
* made with Muse Wonder 🤝
00:00:24 Muse’s breakout use cases
00:07:28 The model is the product
00:14:35 Muse Wonder
00:24:56 How frontier models are built
00:48:26 Betting $100B
01:07:21 Agentic commerce
01:19:34 OpenRouter
01:20:06 Beyond the smartphone
01:27:00 Funding
01:34:06 Why AI won’t be winner-take-all
01:40:46 Meta Superintelligence Labs (MSL)
02:08:11 Working for Mark Zuckerberg
John Collison
Cheeky Pint on palannut! Mark Zuckerberg @finkd ja Alexandr Wang @alexandr_wang tulivat pubiin keskustelemaan uudesta frontier-mallistaan, Muse Wonderista.
00:00:24 Musen läpimurtokäyttötapaukset
00:07:28 Malli on tuote
00:14:35 Muse Wonder
00:24:56 Miten frontier-mallit rakennetaan
00:48:26 Panostus 100 miljardilla dollarilla
01:07:21 Agenttipohjainen kaupankäynti
01:19:34 OpenRouter
01:20:06 Älypuhelimen tuolla puolen
01:27:00 Rahoitus
01:34:06 Miksi AI ei tule olemaan winner-take-all
01:40:46 Meta Superintelligence Labs (MSL)
02:08:11 Työskentely Mark Zuckerbergille