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Palantir Technologies Inc.

PLTR

24 stories

$471B Market cap · 2026-10-21

@convequity

Convequity on X

$NVDA is in a bind. In our view it needs enterprise AI adoption to grow quickly, but a typical enterprise cannot afford Nvidia's GPUs and does not have the resources to build and run an Nvidia stack directly. So enterprises take the cheaper and more manageable option and access AI through the hyperscalers. They skip the large upfront cost, and they can run inference on the hyperscalers' custom ASICs, or at least have that option versus Nvidia's GPGPUs. In our view, those ASICs are the more efficient inference choice where a hyperscaler offers them. To curtail that, we think Nvidia would have to cut GPGPU prices sharply enough to make direct enterprise adoption realistic. That means lower margins. Dell ($DELL) was supposed to be the way around the bind, a channel that puts Nvidia compute in front of enterprises that will not build the stack themselves. We think Dell has mostly used the relationship to secure GPU supply and grow its own revenue and margins, not to open the smaller enterprise market Nvidia needs. Palantir ($PLTR) is still the exception on enterprise AI adoption, not on buying Nvidia compute directly. Its US commercial revenue grew 149% year on year last quarter, accelerating from 133% the quarter before. Outside Palantir and coding agents, we see little enterprise success. The bind stands. Direct enterprise adoption of Nvidia compute is still progressing very slowly, and we do not see a way out that leaves margins where they are.
@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.
@convequity

Convequity on X

Why I think Nvidia buying Reflection AI helps Palantir more than it hurts $NVDA wants its models inside enterprises, not just hyperscalers, neo-clouds and AI labs. Buying Reflection AI would speed that up. The FT reported on 10 Oct that Nvidia is in early talks to buy Reflection, raise its stake, supply it more chips, or hire its team and license its technology. Bloomberg's source says no terms have been reached. Nvidia has already invested $800m, so a buyout or a bigger stake means pricing a company it already partly owns. The enterprise push Nvidia agreed in September to buy Hugging Face for $12.9bn. Jensen Huang says more than 200,000 companies use it to deploy AI. Reflection is validated on Dell's AI Factory with Nvidia, and $PLTR's sovereign AI stack runs on Nvidia's AI factory design. Reflection is built for enterprise code. Its Asimov agent reads a company's code, Slack messages, emails and documents. Its first model, Beam (501bn parameters, 23bn active), is trained for coding. Reflection says the weights ship this month under Apache 2.0, for on-prem or air-gapped use. The bear case: Nemotron is moving too slowly at Palantir One reading is that Nvidia wants Reflection because its Nemotron models are moving too slowly with Palantir. I think that is unlikely, given how Palantir has executed. At AIPCon on 10 Sep, Nvidia's supply-chain team said Nemotron 3.5 Lightning, post-trained in Palantir, beat Nemotron 3 Ultra by a large gap on Nvidia's own benchmark. It runs on-prem and retrains on one GPU in a couple of hours. Cisco will offer Nemotron post-trained with Palantir in its AI Factory with Nvidia. L3Harris said open models fine-tuned on its own data beat the frontier models it had used within 48 hours, at 95% lower cost. Palantir has kept shipping open-model deployments. In June it launched an engine to run Nemotron in sovereign environments, and customers own the retrained models. On 1 Oct Armada became its first certified modular data-centre partner. On 12 Oct $ONDS formed Regna AI on AIP and open-weight models. The likelier case: Nvidia is backing more than one route Palantir fits open models to a company's data, permissions and workflows. Reflection is built to read the company's code. Buying a team that has already built that is quicker than building it in-house. The limit is vendor API rules Reflection reaches a company's systems through the vendors' APIs, and vendors decide what passes through them. $SAP's April API policy bars AI agents that plan or execute API calls, and bulk data extraction, outside SAP-endorsed pathways. Since May 2025, Slack's API terms bar third-party apps from using Slack data to train a large language model or bulk-exporting messages. Those rules apply to Palantir too. In May, SAP made Palantir AIP for data migration an SAP Endorsed App. An independent Reflection would likely need similar approval from each vendor whose systems it reads. Who pays the compute rent Reflection trained Beam on Nvidia GB300s rented from $SPCX at Colossus 2 in Memphis, under a June deal worth up to $6.3bn: about $150m a month through 2029. $NBIS agreed in July to sell it $1bn more capacity. If Nvidia buys Reflection, it takes on those contracts and pays SpaceX rent on Nvidia chips that SpaceX bought. In an acqui-hire, the contracts stay with what remains of Reflection unless the deal says otherwise. The FT says an acqui-hire would likely avoid a long regulatory review. Nebius holders should watch which company ends up owing that $1bn contract. What it means for Palantir The risk is an independent Reflection selling its own forward-deployed team and turnkey stack, which is how Palantir sells. If Nvidia owns Reflection, I expect Beam to reach AIP customers the same way Nemotron does, with each customer fine-tuning it on its own data inside Palantir.
@convequity 1 click

Convequity on X

Software multiples have fallen further since our January report. As of 7 Oct 2026, 238 companies in our all-software watchlist have EV/Sales (NTM) data on Koyfin. The median is 3.1x, down from 3.7x in January. The average is 5.4x, down from 6.2x. The 25th percentile is 1.8x, down from 1.9x. The 75th percentile is 5.5x, down from 6.1x. 191 of the 238 (80%) trade below our 6.7x private equity floor. 113 trade below 3x. The Bessemer Cloud Index shows the same pattern. Its median is 4.7x, down from 5.1x in January. 46 of its 68 companies (68%) trade below 6.7x. The cheapest are $CXM at 0.9x, $SPT at 1.1x, $MNDY at 1.7x and $WIX at 1.8x. In our watchlist, only 21 companies trade above 10x. The most expensive are $PLTR at 44.5x, $CRWD at 42.4x, $NET at 38.6x, $PANW at 24.2x, $DDOG at 19.5x and $SNOW at 16.3x. Growth still earns a higher multiple. The 44 names expected to grow revenue 20%+ a year over 3 years have a median of 5.2x. The 85 names expected to grow under 10% have a median of 2.6x. Even for the faster growers, the 5.2x median is below the floor. The market is paying up for very few names. We think that is right. Many software companies have slowing growth, high costs and no clear AI strategy. We would not buy the sector, or a cloud index, on valuation alone. In software, we focus on the few companies rebuilding their products around AI, and our SaaS Survival Scorecard tests key SaaS names against the pressure from AI agents.
@convequity

Convequity on X

We agree with Ampersand: an agent is vaporware without deep access to the customer's systems of record. But the result is headless SaaS, where the agent becomes the interface, and headless SaaS does not keep today's economics. Convequity's SaaS Survival Scorecard splits a vendor into three layers, the interface, the workflow and the data, and scores how many of the three it keeps once agents arrive. 1. Deep integration into the system of record is the headless path: the user works through Claude, ChatGPT or another third-party agent, and the vendor keeps workflow and data underneath. Our scorecard calls this Route 2: roughly half the SaaS value is replaced, and the vendor is survivable but diminished unless outcome pricing rescues it. It is the most likely near-term route, and a starting point rather than a stable endpoint. 2. Model companies will expand into the workflow layer to keep growing, and a vendor whose workflows mainly connect other SaaS APIs is highly absorbable. A vendor left with only its data layer is plumbing: it still earns on agent traffic, but it gives up a lot of the value. A vendor that keeps hard-to-replace workflows retains more of it. 3. More agents does not mean more SaaS revenue in every domain. Back-office cost centers like HR, accounting and IT service management have a natural ceiling, because making the work cheaper does not create more of it. Agents cannot create demand for an extra tax filing. Revenue-generating functions like sales and marketing can grow with agents, because extra work there produces extra revenue. 4. Keeping all three layers is rare. $PLTR is the full Route 3 case in our scoring so far, because its ontology works as a quasi system of record and it has model-layer leverage. $PANW approaches Route 3 only within security operations, where sensors and inline enforcement are hard for an agent to absorb. Nearly every other vendor will give up the interface. Ampersand's integration layer and our scorecard describe the same future: the agent is the interface, and the vendor keeps workflow and data. We differ on the economics: a vendor keeps more revenue where the work is unbounded and it holds its workflows, and less where the work is bounded or it is left with only its data.
@convequity 3 clicks

Convequity on X

What $MNDY can learn from Cursor’s breakout and $PLTR’s head start General productivity AI may still be waiting for the foundation-model capabilities that helped coding applications take off. That leaves a possible breakout ahead for MNDY. But waiting for the next model release would give its competitors the same opportunity. Our latest SaaS Survival Scorecard explores what MNDY could build in the meantime. During 2023 and early 2024, Cursor had already built its coding editor and an early harness around models such as GPT-4 and GPT-4 Turbo. That surrounding software supplied codebase context and helped developers apply and review changes. But the models still struggled to perform complex coding work reliably. Cursor had built a way to deliver AI assistance, while the intelligence available through it limited how much work developers could delegate. The turning point came with Claude 3.5 Sonnet in June 2024, which Cursor made its default model the following month. Its stronger coding capabilities made the application Cursor had already built much more valuable to developers. Subsequent releases, including Claude 3.7 Sonnet and Sonnet 4, extended what it could do. General productivity could experience a similar improvement. Coding, however, has an advantage that helps explain why it advanced earlier: verifiability. Code can be run. Tests can check whether it produces an expected result. A training system can try different approaches and reward the ones that pass. This gives model developers repeatable feedback about what worked. Work management combines verifiable actions with decisions that are much harder to judge. Did an agent send the document to the correct approver? That can be checked. Should the approval have been requested in the first place? Should one customer’s urgent request interrupt another team’s project? The answer may depend on commitments, exceptions and relationships that nobody has written down. The result may only become clear weeks later. Rewarding an agent for closing tasks quickly could even encourage it to close work before the customer’s problem is resolved. This helps explain why narrower AI-native applications have progressed faster. Harvey focuses on legal work. Sierra focuses on customer interactions. Their narrower scope makes it easier to define the job, connect the necessary systems and assess whether the customer received value. Legal interpretation and customer disputes still require judgment. The advantage is a more specific problem than “help everyone across this organisation work more effectively”. MNDY therefore has work to do before foundation models improve. It could capture more of the knowledge that managers use to coordinate people and departments, then turn that knowledge into rules and records an agent can check. For example, when a manager repeatedly overrides an assignment, the agent could ask why. When a project requires an extra approval, it could identify the condition that triggered it and ask the responsible person to confirm the rule. Over time, MNDY could connect those rules to named customers, documents, dependencies and permitted actions. It could then check whether the handoff was accepted, whether the work was reopened and whether the intended outcome occurred. That would improve the information available to today’s models and create better examples for evaluations and eventual post-training. $PLTR shows how valuable this preparation can become. Its forward-deployed engineers have spent years working directly with customers and the people who use its software. Those conversations reveal how work actually gets done, including exceptions that a standard process diagram leaves out. Consider a manufacturer waiting for a component. One customer can accept a substitute. Another requires a quality approval. A third faces a delivery penalty that makes the delay much more expensive. An engineer working with those users can discover these differences and build them into the software. This means the AI agent has explicit rules and relationships to follow instead of having to guess from a collection of documents. PLTR’s ontology also gives data from different systems a common vocabulary. The agent can recognise that an account in one application and a customer in another refer to the same organisation. This accumulated work helps explain why PLTR could make earlier, less capable models productive when it introduced AIP in 2023. It also gives PLTR an advantage that is difficult to reproduce quickly. MNDY’s off-the-shelf approach - as with typical SaaS - has historically placed more responsibility on customers to describe their processes through boards and automations. That makes the product easier to distribute, but leaves much of the unwritten knowledge in employees’ heads. PLTR’s customer-specific implementations have captured more of that knowledge through direct engineering work. MNDY’s opportunity is to find a cheaper, repeatable way to capture it across its installed base. Its existing harness work is a start. Context, memory, tool controls and evaluations take it beyond basic retrieval, which is why our report scores its model-layer integration at 2.5. The larger opportunity is to make more customer workflows explicit and verifiable. That could improve agent performance before the next model breakthrough arrives. Better foundation models would then operate with a maintained understanding of how each customer works. If customers depend on that understanding to complete work reliably, MNDY would have a stronger reason to retain their business than access to a model its competitors can also buy. The full MNDY report develops this analysis within our SaaS Survival Scorecard. #AgenticAI #SaaS
@convequity

Convequity on X

The $PLTR and $NVDA work on Nemotron, NVIDIA's open model family, is still under the radar, unlike partnerships that promise more than they deliver. In our view it could be industry-transforming, because each side needs the other: Nemotron is the Western open model that fits, and Palantir is the only vendor that can put a model into an enterprise securely, safely and privacy-first, and limit it to what each person may see and change, which is why AIP, Palantir's AI platform, took hold and why NVIDIA needs Palantir. Palantir has no proprietary model and leans on closed models from OpenAI and Anthropic. Chinese open models are almost certainly off the table for a U.S. government contractor. On August 26 Jensen Huang said every major company needs its own proprietary AI, and open models make that possible. On June 29 Palantir launched an engine that runs custom Nemotron models for U.S. agencies and critical industries on networks cut off from the outside. The customer can train this American open model further on its own data and keep the weights, the trained model. On September 10 they announced a supply-chain stack, first on NVIDIA's own supply chain. On July 14 NVIDIA said other companies already specialize Nemotron for their own domains. The test is whether that stack moves from NVIDIA to named customers in manufacturing, energy, healthcare, automotive or aerospace.
@convequity 1 click

Convequity on X

We’ve been mega-bullish on $PLTR since late 2020. We expected it to come out on top of our SaaS Survival Scorecard. It did. Most software will sit under someone else’s AI window. Palantir is one of the few that can reasonably be that window — the place where work, decisions, and data meet. The edge is breadth plus depth. Breadth: hundreds of kinds of operational work on one substrate, not one app. Depth: it reaches down into the source data layer. A lot of the records still live in old ERP and CRM systems. Palantir doesn’t have to own those databases. It normalizes the mess, puts a shared map on top — objects, relationships, allowed actions — and that map becomes the ground truth. Think of SAP and Salesforce as the ground floor. Palantir is floor 1: where the business actually runs. That creates data gravity and speed. Once decisions live on floor 1, going around Palantir is the hard path. One thing that is not a proven Palantir product yet is refining models on the customer’s own missions — post-training and reinforcement learning on their data, with the customer keeping the weights. That is the NVIDIA Nemotron work. Until recently it was a partnership. Two weeks ago at AIPCon 11, NVIDIA showed it live in its own supply chain. That’s the first public install. Not a general customer rollout. A few months ago the stock was subdued even after strong results, on the fear that model labs would expand into Palantir’s territory. A working Nemotron collaboration takes some of that risk off the table. The risk now is different: Nemotron has to become a frontier-quality model. If it does, Palantir is the best — and probably the only — company positioned to deliver true sovereignty over enterprise intelligence. If it isn’t smart enough, post-training and RL on Palantir’s stack may still not be good enough for enterprises. Watch two windows for Nemotron progress. The nearer one is Q3 earnings commentary — Palantir usually reports that quarter in early November. Management may say more there. The other is the next AIPCon, Palantir’s customer case-study day, roughly every quarter - possibly early December. That’s where live customer proof will show up.
@convequity

Convequity on X

Agentic AI is changing the question for every SaaS name. The old one was “how big is the market?” The new one: when agents do the work, does the vendor get bypassed, survive as plumbing, or own the stack? We built a scorecard for that split — and ran the first three deep dives: • $PLTR — Route 3 ownership bet (ontology / multi−domain ops). Structure elite; valuation still has to earn it • $DDOG — Route 2 plumbing agents need. Host telemetry is hard to absorb casually; coding agents + first-party collect is the watch item. Multiple roughly fair • $NET — purest agent-volume rails. Unabsorbable network footprint; physics caps the model layer; ~40x prices in a lot Condensed public version below. Full Part 2 + series for Convequity subscribers.

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@convequity

Convequity on X

Palantir remains our marquee software conviction. Growth is accelerating as ontology and governance become essential for secure AI deployment in government and enterprise. The bear case is model dependency, but this is where $PLTR shines. Enterprises risk losing their information and knowhow edge by feeding proprietary data to foundation model providers — their own suppliers. Satya Nadella’s “Reverse Information Paradox” captures it perfectly. Microsoft did it with Lotus/WordPerfect (Excel/Word), and Anthropic is already demonstrating such practices with Claude Design competing with Figma — and countless other examples too. Palantir’s strong data control and governance moat positions it to avoid the trap. Developing its own foundation model could be a game-changer, offering true IP protection.

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