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Meta Platforms, Inc.

META

218 stories

$1.6T Market cap · 2026-10-28

@ParadisLabs

Paradis on X

Quick thoughts ahead of $META earnings shortly: Key question: What does the monetization roadmap look like for Muse? Despite app downloads showing that AI capex is translating into real demand, my gut feeling is that Zuck will give a more hollistic view on AI monetization that lacks any real detail. This ultimately boils down to Meta's strategy of scale first, monetize later. Which then raises the usual capex questions particularly with Meta now positioning themselves as a frontier AI lab with their new Muse Wonder models. They'll need more compute if they're really taking this AI thing seriously. The current FY26 capex guide is $130-145B, so any increase to this without a clear AI monetization roadmap could be detrimental if I take a glass-half-empty view...particularly with consensus FCF deeply in the red at roughly -$9B for Q3. So how do Meta finance this exactly? Is it via their Blackrock agreement, or some combination of debt/equity? Again, something we'll need clarity on, layered on top of their monetization plans. I ultimately think that Zuck needs to really sell the longer-term AI vision today.
@convequity 1 click

Convequity on X

Our AI Bubble Barometer (ABBX) says the hyperscaler complex is not yet in bubble territory. Across the hyperscalers and neo-clouds we track, enterprise value relative to the capital invested in the business has fallen from 7.67x at the 15 May 2026 series start to 6.12x at the 15 Oct 2026 close. Two-year forward returns on that capital were 3.07x the cost of capital in Q2'26, down from Q1'26. Still comfortably value-creating, but the direction is lower, which fits this stage of the cycle: data centres and chips go on the books immediately while the revenue they earn arrives later. Q3 2026 reporting starts with Meta ($META) on Wed 21 Oct after the close, with Alphabet, Microsoft and Amazon the following week. We will watch whether forward returns keep falling as the capital base grows ahead of revenue. Falling returns alongside rising valuation multiples would be our classic bubble signal.
@convequity 2 clicks

Convequity on X

$META's Muse download numbers measure Meta's distribution, which was never in doubt. What decides whether Muse is a business is what the agent completes for a user, and nothing reported this week measures that. Sensor Tower puts Muse among the fastest US app launches on record, with early retention above the average for rival AI apps, and says a large and rising share of Meta's own ad spend now goes to it. That ad spend likely explains much of the install curve, and Meta can run the same ads for any product it owns. 1. Tasks completed. Nothing Meta or Sensor Tower has reported says how many sessions end in a booking, a purchase or a message sent for the user. Those are what a user would pay for, and in our view they are also where a merchant fee could come from. 2. Where the revenue shows up first. In our view Muse's first financial effect is on the ad business Meta already runs. When shopping happens inside Muse, Meta sees the whole path from what a user wants to what they buy, which sharpens its ad targeting. If users start shopping in Muse, Meta can also sell sponsored placement in its answers. Further out, ads could become part of the conversation itself, something we suggested earlier for Meta AI and which applies even more to an agent like Muse. A user preparing for their first marathon with Muse could be offered trainers, energy gels and hydration products in a conversational manner as the plan takes shape, rather than seeing an ad placed beside it. We expect Muse to show up in ad pricing before it appears as a subscription or a take rate. 3. The sharing suspension. Meta paused conversation sharing after security researchers found shared chats could expose personal and business information. That suggests people are already giving Muse real work with real data and sharing it with colleagues. It also means workplace use will meet the data-handling limits that slowed enterprise use of consumer chatbots. A real catalyst for $META will be Meta disclosing how many tasks and transactions Muse completes, especially if the numbers are impressive.
@ParadisLabs 3 clicks

Paradis on X

Once again, the GPU depreciation bear thesis is dead. Elon on the $SPCX call: "The value of compute per watt will increase over time, not decrease... that's why you see things like even old computers like H100s renting for more than they rented when they were new." When supply is this constrained, GPU rental prices are set by the value of the marginal token, not the replacement cost of the chip. Models keep getting better, and better models raise the value of every installed watt faster than new GPUs deflate the old ones. Various datapoints all stack up: -> $SPCX says compute contracts are renewing "at higher dollars per watt, not lower dollars per watt" -> 1-year H100 contract pricing went from ~$1.70/hr to ~$2.35/hr between Oct 2025 and Mar 2026 (a 2022 chip!) -> $CRWV signed an A100 contract running into 2029 (a 2020 chip) and said older-gen pricing is at / above a year ago, and raised list prices ~25% in July. -> $NBIS's first Blackwell auction cleared 15% above its previous high and as a reminder....they told customers H100 on-demand goes up ~17% (from Oct 1). -> Zuckerberg: $META is getting offers for its compute "at a significant premium over what we paid for it". -> Lenders are now lending against GPUs out to 2030 e.g. $NBIS borrowed against its GPUs at SOFR+250 in July. Feels like the 2-3 year GPU useful life bear thesis keeps getting setback. Ofc, the one thing that ruins the party is the S/D gap closing -- Rental prices fall back toward replacement cost + older GPUs get repriced on perf/W. Which won't happen anytime soon.
@ParadisLabs 1 click

Paradis on X

For AI/Semis stocks, it feels like every earnings season becomes signficantly more important than the last. This time is the same, but for different reasons which has led to market chopiness: - Macro instability (Iran, oil, yields, inflation) - Midterms - $CRWV credit stress - AI ROI concerns via frontier lab ARR's (& pending hyperscaler earnings on top). Today: $SPCX Wed: $META, $TXN, $LRCX Thu: $BE, $WDC, $NOK Let's see if good EPS will be enough to turnaround sentiment.
@StableBread 2 clicks

Fajasy on X

I tried to work out who’s behind the AI data labeling companies that approached Nubeva Technologies $NBVAF (TSXV: $NBVA). CEO Randy Chou didn’t name the data labeling companies (firms that turn experts’ knowledge into training data for AI labs) or the customers behind them. “If a data labeling company’s coming to us, somebody behind them is asking,” Chou said, adding that the customer typically isn’t limited to cybersecurity companies. OpenAI is one potential candidate, since my previous write-up concluded that the frontier model Nubeva got access to in September was most likely OpenAI’s, through its Daybreak program for vetted defenders. OpenAI launched GPT-6 Astra on September 3, 2026, the day Nubeva announced its access. It’s the first model OpenAI rated Critical for cybersecurity capability. But access to OpenAI’s model wouldn’t prove OpenAI is also behind the data labeling companies, since any lab building cyber models could want labeled malware. Among data labeling companies, three of the largest are publicly recruiting or selling binary analysis expertise, the skill of figuring out what malware programs do: → Scale AI: Its product manager posting calls the cybersecurity portfolio “a product line that does not exist yet” and says the hire will recruit “malware analysts.” → Turing: Sells CyberBench, security tasks for training and testing frontier models, with malware analysis and reverse engineering among its ten categories. TechCrunch called Turing “a key coding provider for OpenAI” in March 2025. → Mercor: Recruits security experts, preferring reverse engineering or binary analysis backgrounds, and counts OpenAI, Anthropic, and Meta $META as customers. OpenAI cut ties with Scale in June 2025, after Meta bought 49% of Scale and hired its founder, Alexandr Wang. So if OpenAI is behind the approach, Turing and Mercor are the likeliest data labeling companies, since both sell to OpenAI and already sell or buy binary analysis expertise. Either way, these data labeling companies are worth $2.2-29B, so even Turing, the smallest, could pay the top of my $12-19M sale range for Nubeva for less than 1% of its last valuation.
@convequity 3 clicks

Convequity on X

$META bulls say its ad cash flow lets it undercut the AI labs on price. The Muse Wonder launch shows Meta matching their prices instead. Meta released Muse Wonder and Muse Wonder Full on Monday and says they score level with Claude Fable 5.1 and GPT-6 Astra on its own tests. Three details from the launch. 1. Wonder costs $4 per million input tokens and $20 per million output tokens on Meta's API. TechCrunch puts that in the same price band as GPT-6 Sol and Opus 5.5. Meta is pricing with the labs, not under them. 2. The stronger model, Wonder Full, is not on the API at all. It is only available to Muse Code subscribers. A developer comparing API models is not buying the model Meta uses to claim the frontier. 3. Both models are distributed only through Meta's own API and Muse Code subscriptions, and the launch says nothing about releasing the weights. I read this as Meta adopting the labs' business model rather than attacking it. The frontier model is a subscription product, and the API is priced at the labs' level rather than below it. That weakens one part of the bull case. If Meta is not undercutting, the price pressure on the labs that bulls expected has not arrived with this launch. The question for the results on 21 October is what Muse subscriptions, Muse Code and the API are earning against the spend on the Prometheus data centre that trained these models, not whether Meta can take the labs' customers on price.
@convequity

Convequity on X

We think Amazon ($AMZN) blocks Meta's ($META) Muse shopping agent partly to protect its advertising business, while Shopify ($SHOP) welcomes it to earn payment fees. Amazon cites authorization and security concerns. We also see an advertising business to protect. Amazon earned $68.6 billion in advertising revenue in 2025, including payments from sellers to place their products prominently in front of shoppers. An agent that selects products without taking customers through those sponsored listings could reduce sellers' willingness to pay for them. Amazon still handles payment and delivery, which gives it bargaining power even when an agent chooses the product and places the order. Shopify welcomes Muse through Shop Pay. It can keep earning payment-processing fees when agents complete purchases through its systems, and it has no comparable advertising business to defend. We think shopping agents could favor merchants whose products stand out for quality or features over merchants whose sales depend heavily on paid placement. That could give independent brands another way to reach customers, which would benefit Shopify as more purchases pass through its merchants' stores. Agents and established platforms could still agree to share advertising revenue. We are watching whether agents improve product discovery or simply become another place where sellers pay to be recommended.
@aleabitoreddit 2 clicks

Serenity on X

Sure... Random train of thoughts on markets now, optics like $LITE / memory + $IREN / Anthropic. On Memory: - Demand visibility on $MU / Samsung / SK Hynix extend into 2030-2031. Samsung has LTAs covering 70% of their capacity into 2031 (likely with SK Hynix type price floors)... and their operating profit is already absurd. There's a consensus that shortage in 2027 will be much more severe than 2026... Even $SNDK expends gross margins to exceed 80% entering 2030. You're going to hear Acer and other CEOs that want memory prices to go down cite bear cases like CXMT/YMTC, and Chinese players. But just looking at supply procurement that $NVDA is doing + hyperscalers, demand visibility is absurd for the next 5 years. And by that time, cumulative FCF coming in from these take/pay + pricing floor contracts might exceed some of their entire marketcaps so it feels like there's some baseline. Near-term is always volatile (IV dropped a ton across $EWY to $DRAM to $MU), so people with call options likely got nuked, along with underlying prices. But if Samsung is 3x 2027E, has 5 year LTAs with 70% of capacity committed from hyperscalers, and price floors, it's kinda hard to see these names suffer a sustained crash... For optics: - $LITE is literally sold out into 2029, starting discussions into 2030. With demand visibility lasting into 2031… with hyperscalers largely funding the capacity buildout. Their 2028 Greensboro fab expansion was ~$5B in UHP laser capacity revenue alone, yet it’s basically allocated toward $NVDA (50%+ with options for more) and co from channel checks. Existing supply chain bottlenecks are extremely severe as optical content/XPU skyrockets entering 2027-2028. So everything from DSPs/TIAs, photodiodes, lasers, InP/SOI substrates, wafers (epi, SOI, etc) are expected to go brrr. And what does that imply to anyone else with enormous amounts of capacity coming online like $AAOI + $SIVE + $MTSI capacity expansion in 2027? Or other players like Sumitomo / Furukawa? But again it's not quite the same situation as memory since the BOM % is extremely low in a cluster... If micron/samsung raised another 2000% hyperscalers would go bankrupt. But doing that for InP substrates seems possible. Or a 100% increase on CW laser content. I actually thought $LITE was overvalued at $100B+ MC, relative to capacity expansion on your smaller optical players to their respective MCs. So I chose to position accordingly (But lumentum does deserve premiums for demand visibility). For the companies leading the charge like $NVDA / $AVGO / $MRVL: - Nvidia has 70% forward revenue growth FY2028 which is supply constrained growth. - Broadcom 100% Y/Y for 2027/2028 which is also supply constrained growth. - Marvell FY2031 upgraded guidance to $70B-$90B from $46B analyst projections... They all seem to be very happy.... and read through on demand for next 2-3 years is enormous (also echoing what $INTC CEO said today on the panel with $AVGO CEO). So you have overwhelming growth on memory + photonics + from the leaders... But the nuance is not everything in the AI supply chain get the same economic benefit. HBM/Laser/CPU/MLCC and other players in the bottlenecks look to be printing money. And seem to be the largest beneficiaries of the current bottlenecks. Test players are also getting price hikes + capacity allocated by players like $NVDA, but economic impact seems a tad lower than other players since forward P/E for many I modeled were still in the 45x+ range even if you hiked prices like 10-30%. As for capex intensive players... there's some like $IREN ($25-30B for 2027) that despite a deal with Anthropic ended up dropping. I don't think markets seem to appreciate players spending a lot of money + ongoing financing (think they're like halfway through their $6B ATM). Just some hopium for $IREN investors, $NBIS also signed a deal with $META at the start of the year (but markets didn't care at all, since Nebius dropped to ~$70-80 at the lowest point?). But did 6 months later, as the time to revenue inflection gets closer and you're past the ATM/capex window, markets would likely care more. I don't think it's irrational but at the same time, the fundamentals have improved from Mirantis acquisition to others. I also think it was known for awhile it was either OpenAI/Anthropic from CEO comments earlier too? But either way to be a more risk-off environment where funds seem to be rotating to larger names as a flight to safety. $CRWD / $PLTR as just two example, but my take is that the valuations of these rotations are reaching clown levels. And that certain AI supply chain beneficiaries in comparison are undervalued now. I was thinking markets would just rotate to names that are extremely profitable like Samsung or names that have <2X P/E, so it was surprising to see $KOSPI and some legacy memory players get sold off today. Since all of this is colliding with uncertainty about sht macro environments and renewed sustainability concern of AI capex/commitments from frontier labs like OpenAI/Anthropic from due to that FT piece recently. I've always been sounding the alarm about the contractual backlog OpenAI has with $CBRS, $MSFT, $ORCL and others earlier in the year. But OpenAI did just raise $122B or so , so I don't quite think they're at risk of going down anytime soon. I also don't think overall capex can just exponentially go up, but even if it stalls / hovers around a certain point or even drops... Capex toward certain areas like optics will still explode as hyperscalers fight over allocations when CPO scale up comes about, but might drop in others where more capacity comes online like test or DRAM. So it's a lot more nuanced than capex down = everything down or vice versa. I also slightly agree with community agreements that open weight models wouldn't lessen demand on AI infrastructure (otherwise $NVDA wouldn't be funneling funds into Reflection), but all the contractual obligations that Anthropic has might get messy if their models/company get cannibalized. Either way, I'd expect some of these frontier lab valuations to likely compress but I do think Jensen knows what he's doing though to avoid contagion. But basically: - Extremely difficult market now, can't just throw money at any AI supply chain company like $GLW and expect it to go up. There's cause for concern of rate hikes mixed with financing per $AVGO CEO but not to the point you just blindly sell off everything. Also I think markets are stupid and ties baskets together so that leads to mispricing + individual buying opportunities with certain chokepoints too. If capacity + demand is visible and forward p/e compresses like $AAOI to single digits off hundreds or thousands of percent growth from hyperscalers off $471m transceiver revenues and hundreds of thousands of monthly ELSFP capacity. Or $SIVE coming online with 100M+ laser capacity + multiple foundries with ASP hikes on lasers. I wouldn't lump them into other AI supply chain names in IP like $RMBS or others. My take is that AI supply chain beneficiaries with closer inflection points, demand visibility, and forward P/E compressing due to economic leverage would likely outperform medium term despite current volatility. TLDR: - Memory strongest risk/adjusted return with LTAs + pricing floors extending to 2030's + buybacks. - My opinion is that optical supercycle for 2027-2028 with CPO/NPO is going to look absurd relative to capacity coming online of smaller suppliers. - Overwhelming fundamentals/growth colliding with sht macro + media scares - Markets are stupid and groups things in baskets and sells them all off together or buys overvalued names, creating short/long opportunities I'd give a prayer to people on short term options given underlying drop + IV decline, but there could be a reversal... who knows. Most valuable thing right now is time.

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