SentinelOne directly competes with CrowdStrike, Microsoft Defender and Palo Alto in endpoint security. It’s also quickly expanding into cloud security, which adds Zscaler and pretty much every large next-gen platform as competition.

It specializes in small-and-medium-sized business (SMB) clients and is expanding up-market. While CrowdStrike’s overarching platform is called Falcon, SentinelOne’s comparable suite is called the “Singularity Platform.” Core products include Endpoint Detection and Response (EDR). EDR offers constant monitoring and protection of endpoints (like a company iPhone). It unveils, prioritizes and responds to observed threats. Like CrowdStrike, it offers highly autonomous services and a slick, lightweight agent to drive efficient work and interoperability. This, in turn, means overarching coverage and superior breach protection vs. legacy incumbents.

Also similar to CrowdStrike, SentinelOne boasts a complementary data analytics platform (which it calls the Singularity Data Lake). This lake can ingest structured data from a multitude of diverse security products. It’s the perfect sidekick for everything it offers, as it can seamlessly collect data once, and recycle that data across as many relevant use cases as it needs to. This capability is especially important for the firm’s Extended Detection and Response (XDR). XDR is simply EDR with more diverse data usage to extend protection beyond solely the endpoint.

The Singularity Data Lake ingests data via “log scale,” which means logarithmically organizing and storing information. The company also says customers get lower cost and faster querying speeds with it. The service of aggregating data (or “logs”) to help organizations uncover and remediate threats is called Security Information and Event Management (SIEM). It recently launched an AI-augmented SIEM tool… fittingly named AI SIEM.

All in all, there are three compelling effects of this product architecture:

Just like CrowdStrike (noticing a theme?), it’s also actively expanding into cloud security. Important cloud security acronyms:

Agent vs. Agentless in Cloud:

CWP takes an agent-based approach, while CSPM is agentless. Agent-based requires a direct software installation, while agentless does not. One isn’t objectively better than the other. Agentless is considered cheaper, easier to deploy and easier to scale. It’s perfect for lower-stakes use cases like configuration analysis and is an ideal complement to CWP. Companies just starting out with finite budgets, massive potential scaling needs and a lack of hyper-sensitive data can adopt an agentless approach. Agent-based is considered more comprehensive and has more complete visibility. Industries with tighter regulation, more sensitive assets, a need for real-time EDR and more complex compliance are well served by agent-based. By offering both, SentinelOne can address both markets, thus eliminating the need for disparate point solutions.

GenAI:

PurpleAI is SentinelOne’s overarching GenAI platform layer to up-level its product offering. It’s quite similar to CrowdStrike’s Charlotte AI, in that it can actively detect anomalies, summarize cases, help orchestrate remediations and fix issues with a human analyst’s permission. All of this pushes beginner-level security analysts to much higher levels of capability. This matters a lot in our budget and talent-constrained world.

The bright spot of this quarter’s demand was 33% Y/Y remaining performance obligation (RPO) growth; this was 5% ahead of expectations.

More on the guide in the piece.

AI:

SentinelOne’s Purple AI assistant continued to deliver 100%+ Y/Y bookings growth and a “record high attach rate” of 25%. AI also continues to raise average deal value by 25% as Purple AI product introductions build immediate traction. Its AI Auto Triage tool, which investigates and ranks threats with an ability to build plans and actionably resolve issues, is working well. The Hyperautomation product, which provides pre-built, no-code integrations and malleable templates for common cybersecurity issues like ransomware, has also been successful.

To build on this momentum, it launched Purple AI Athena. It views this as the “first true end-to-end agentic AI platform for security.” This means preventative maintenance, breach protection and security cleanup can all become more conversational. It lowers the required skill set for analysts and unlock goal-oriented coverage. This also unlocks 3rd-party data integrations, so that larger firms with data spread out across many vendors can more easily use this product. It pushes GenAI assistants from aggregating and offering good insight and recommendations, to being capable of conducting end-to-end workflows in the most efficient way possible. The plan is to keep adding more and more AI-based utility to create more momentum and up-selling activities down the road.

Cloud:

In a move to drive more cross-selling and more platform-level adoption, it launched a unified Cloud Security Suite. This doesn’t entail a batch of new product releases, but instead a reorganized go-to-market motion that bundles all of the acronyms we covered in the 101 section. The hope is to make larger deals easier to pursue and close. During the quarter, Cloud was the driving force behind a new Fortune 500 contract. SentinelOne secured the contract based simply on superior product efficacy.

Tech Efficacy & Financial Strength Disconnect:

The company has great tech. That has been the case for a long time and is why it leads Forrester endpoint security rankings. It’s how it gets away with calling its CWP tool #1 in the world, how it has led MITRE attack evaluations for 5 years, how it won Endpoint and Cloud Security Vendor of the year from SC Awards and why customer service scores are so lofty. When people do try SentinelOne, they tend to love it. And while it does continue to gradually take market share across its core products, the tone of the Q&A was one of disappointment, as SentinelOne again missed on annual guidance, missed on the vital net new ARR metric and lowered its initial targets.

So where’s the disconnect coming from? I was hoping it would be because the aforementioned sunsetting of a legacy product last quarter was a larger than expected headwind. That would be non-structural and unconcerning. But it isn’t that. The team blamed macro fragility for the disappointment. Trade wars elongated the sales cycle and forced them to “take a more measured stance on full-year growth.” That’s what they said. Churn rates were called normal and win rates were called strong. Some large deals were just delayed. I think amplifying this issue is that its go-to-market still needs more work – especially with large enterprises. That’s frustrating, considering other software vendors like Cloudflare fixed their problems so much more quickly. SentinelOne is now shifting to a more platform-wide go-to-market motion, which very modestly held back results. 

Still, the main issue was macro, with trends beginning to improve in May, but not convincingly enough to offset April’s weakness. It also sounded like they’re approaching heightened uncertainty from a place of prudence, but that’s what they said last quarter when they offered initial annual guidance (below street consensus).

I just have this unshakable feeling that CrowdStrike (direct competitor) won’t talk about these same issues when it reports earnings next week. They just seem to combat headwinds more gracefully than SentinelOne can, and that has a lot to do with their elite cross-selling engine.

I think macro will ~magically~ be just fine for CrowdStrike because I think SentinelOne is forming a precarious pattern of stringing together excuses to blame underwhelming results. My favorite question from the call was on why SentinelOne is growing in line with CrowdStrike despite being 1/5th of its size and not dealing with global outage headwinds. The team dodged most of the question, but did say they’re more focused on new logo growth over cross-selling as a reason for top-line weakness. While this does set them up for strong future cross-selling, ideally SentinelOne could walk and chew gum at the same time. Their competition certainly does.

“When we think about the second half in a more holistic way… demand is still strong, and pipeline is still strong. So all of that just points us again to fundamentals being intact.”

Founder/CEO Tomer Weingarten

“I think as we look ahead — unknowns around federal purchasing and global trade are still present. We're trying to be mindful and reflect that in our outlook.”

Founder/CEO Tomer Weingarten

More on Cross-Selling and Platform-Wide Adoption:

While I want go-to-market improvements to come faster, there still is some progress to compliment. Its more nascent data solutions arm crossed $100M in ARR during the quarter while ARR per customer set a record high and rose 10%+ Y/Y. It signed multiple Fortune 500 logos and now has 40% of customers with 3+ modules (3X over the last 2 years) and 20% with 4+ modules (4X over the last 2 years). So again… not a total lack of progress. Just signs that are more subtle and less needle-moving to overall results than I’d personally expect.

To hopefully accelerate platform-wide contracts, the company is embracing a more flexible module purchasing process that allows customers to utilize product commitments at their leisure and among the modules they want. It makes mixing and matching usage seamless and is very similar to CrowdStrike’s Falcon Flex.

Partners:

To help improve go-to-market, it launched Partner One. This is a new center for managed security service providers, value-add resellers and system integrators to more easily sell S’s product suite. It boasts a more simplified tiering structure with explicitly stated incentives.

Public Sector:

“Near-term uncertainty” around federal budgets was again cited, although it called the pipeline strong. It thinks being the first FedRamp High AI security offering will unlock larger federal opportunities.

“Purple AI is now the first and only cybersecurity agentic AI solution approved for US government organizations.”

Founder/CEO Tomer Weingarten

I am annoyed with this company and team. I can’t call results terrible, as it’s still growing at a nice clip and expanding margins. But I will call these results disappointing… again.

To the SentinelOne team,

If you’re leading the world in product efficacy across multiple pillars… Why is macro hurting you more than most of the enterprise software names we cover? Why is fixing go-to-market taking so long? Why is your growth engine slowing materially faster than it’s supposed to? Why can’t you outgrow your main competitor despite being tiny compared to them and not dealing with unique headwinds like they are? Why am I supposed to believe SentinelOne is world-class when its financial results aren’t? Outperforming RPO growth is nice… but it is not enough.

The company remains very cheap, but the valuation discount becomes more deserved every quarter. I am tempted to make a decision to move on from this name and I’d understand if other shareholders did so following this report. I am going to wait and see what CrowdStrike has to say on their call next week. If macro is mysteriously not hurting them like it is for this company, it’s likely that I either significantly reduce exposure to SentinelOne or fully exit. SentinelOne is not like Trade Desk where we have one quarter of blunders amid a sea of flawless execution. For SentinelOne, the blunders are becoming the theme.

They need to do better. They need to stop blaming everything else for their financial shortcomings. They need to start proving that their world-class tech can lead to a world-class business in a highly compelling market… like it should. My patience is running out.

Nvidia designs semiconductors for data center, gaming and other use cases. It’s unanimously considered the technology leader in chips meant for accelerated compute and Generative AI (GenAI) use cases. While it specializes in chips, it does a lot more than that too. Its toolkit includes chips, servers, switches, networking and cutting edge software. It designs the entire next-gen data center layout with slick software integrations so customers can enjoy the best of accelerated compute. Nvidia calls these data centers “AI factories.”

The following items are important acronyms and definitions to know for this company:

Chips:

Connectivity:

Software, Models & More:

DGX: Nvidia’s full-stack platform combining its chipsets and software services.

As a reminder, last month, the federal government imposed license-based restrictions on Nvidia’s H20 GPUs. These were the lower-powered chips specifically designed to meet heightened export limitations imposed last year. Per Jensen, the new rules effectively shutter the Chinese market for Nvidia’s high-end chips; it does not see an ability to build compliant Hopper chips that would be competitive in that market. As a result, Nvidia incurred a $4.5B charge in Q1, as it had excess inventory that no longer was tied to expected demand. This was supposed to be $5.5B, but the firm was able to repurpose some raw materials.

It did not ship $2.5B worth of H20 chips during the quarter. Next quarter will feature a full 90-day impact of these H20 restrictions, which is why the expected charge will rise from $4.5B this quarter to $8.0B next quarter. There will likely be charges in Q3 as well. As we work through the financial parts of this piece, I will offer context adjusting for these temporary expenses.

Huang walked a fine line between praising the federal government and criticizing it. On the positive side of things, he complimented removing the AI diffusion rule, which opened several Middle Eastern markets for business. At the same time, he harshly criticized this specific Chinese restriction, as he thinks Chinese AI running on American infrastructure will help the USA lead this tech revolution. It would mean more developers and researchers optimizing for the U.S.-based models and would tighten this nation’s strong grip on the AI boom.

Missed revenue estimate by 1.5%. This includes an $8B H20 headwind, which is $3B larger than expected. Had the headwind been as expected, revenue guidance would have been a little more than 5% ahead of consensus. It beat 71.7% GPM estimates by 30 bps and missed EBIT estimates by 1% – again related to the larger-than-expected China headwind. It continues to see a clear path to a mid-70% GPM this year.

AI Chip Boom Runway:

The main debate surrounding Nvidia today is about how long this current supercycle will last. When looking at reiterated massive CapEx budgets from mega-cap tech, new $40B chip orders from Oracle, the fact that agentic reasoning models consume 100x-1,000x more compute than their predecessors and massive sovereign AI deals being struck, things still look excellent. 10% Q/Q growth for the data center and a 200%+ 2-year demand CAGR for that segment says it all.

But how do we know when things will eventually slow down? What would cause that? In my mind – three things to focus on. First, any challenge to its large tech lead would diminish its near-monopoly hold on the GPU market. Many bright analysts in the space seem to think AMD’s newest GPUs will rival Nvidia’s. I’ve been hearing that for over a year and also hearing AMD pounding their chests about catching up to Nvidia as well. The issue? They’re catching up to the chips that Nvidia is already upgrading with massive performance gains. It is very hard to catch the leader when they’re debuting new platforms every 12 months that boast giant leaps in power and efficiency. If anyone can catch Nvidia, it’s probably AMD. But? Nobody has done it. That’s why Nvidia is fetching a 60%+ EBIT margin right now. Tech leads inherently foster pricing power and Nvidia’s pricing power remains abundant. So that part of the runway equation looks very good.

The second piece is how much hardware improvement Nvidia has left in the tank. It needs to keep delivering large boosts in productivity with every single new chip and switch. That’s how it can debut hardware that is better-suited for newer reasoning and agentic models. And? That’s the only reason large customers will remain motivated to keep shelling out tens of billions of dollars for every new platform Nvidia launches. For context, Microsoft is using 5X more tokens per quarter on inference. Driving chip upgrades is how Nvidia will keep that pattern humming.

If Nvidia keeps exponentially improving its tech for more advanced models and use cases, clients really are left with no choice but to buy. If Amazon is running cloud workloads on much better chips than Google Cloud or Microsoft Azure, that’s a massive advantage that the other two won’t complacently accept. If Amazon is running these same workloads on slightly better chips than Google or Microsoft, there’s naturally a lower sense of urgency for the others to spend. Slower improvement would incentivize others to just go with what they already have (and pocketing that extra CapEx). 

This performance improvement piece of the demand runway also looks very good. Blackwell GPUs offer 30x token throughput gains vs. older Hopper GPUs on Meta’s Llama 3 model. It also tripled performance per GPU and, with the help of NVLink 72, 9Xed GPU linking on Meta’s popular model. And generally speaking, Blackwell averages 40x speed and throughput gains vs. Hopper for a typical client. That certainly doesn’t sound like pace of improvement is grinding to a halt.

“We remain committed to our annual product cadence with our road map extending through 2028 tightly aligned with the multiple year planning cycles of our customers.”

CFO Colette Kress

The three model scaling laws all have plenty of room to keep improving and fostering better chip performance. Even pre-training processes, which entail adding more data to models, have a ton of upgrading left to do. And for post-training (retraining models with reinforcement learning) and inference time scaling and reasoning (making models think harder when desired answers are more complex), both scaling laws remain chock-full of opportunity.

The final piece is tightly tied to the second piece. It's return on investment. If companies (mainly hyperscalers) are enjoying sub-one year payback periods on their infrastructure spend, of course they will keep investing heavily into the opportunity. As of last quarter, $1 spent was netting $1.25 per year in revenue to foster those required excellent returns. Companies cannot embrace modern app or data architecture without embracing AI infrastructure first. And? They cannot embrace AI infrastructure with the efficiency needed (to avoid rampant compute inflation) without Nvidia. That’s why hyperscalers are so easily renting purchased GPU capacity at excellent margin.

These are the three things to focus on for bulls. Every chip boom has proven to be cyclical. While this cycle is far larger than any other we’ve had, its revenue opportunity isn’t unlimited. As of May 2025, all three variables that factor into the unknown demand runway look very good.

“Global demand for NVIDIA’s AI infrastructure is incredibly strong. AI inference token generation has surged tenfold in just one year; as AI agents become mainstream, the demand for AI computing will accelerate.”

Founder/CEO Jensen Huang

Data Center Footprint Partnerships & Sovereign AI News:

As discussed during the quarter, Nvidia will build AI factories in Texas and Arizona to manufacture Blackwell (and eventually Rubin) Supercomputers. It’s also working with Foxconn and the Taiwanese Government on a new AI supercomputer.

And as highly publicized, it partnered with the Kingdom of Saudi Arabia to build AI factories in that nation, as well as with the UAE to build a 5 gigawatt AI cluster in Abu Dhabi. It has a “line of sight to building tens of gigawatts in the not too distant future.” That bodes very well for the runway longevity debate already discussed. The UAE deal features OpenAI, Oracle, Cisco and SoftBank partnerships. 

Countries across Asia and Europe are now urgently racing to deploy next-gen AI infrastructure, and Nvidia is by far the best candidate to help them do that. Sovereign has emerged as a “new growth engine,” with many more deals coming (per Jensen).

Data Center Compute Product Updates:

In its perpetual quest to always be improving GPU performance as client needs evolve, Nvidia has already extracted 50% higher overall performance for its Blackwell GPUs through these software-level optimizations (in just 30 days). They expect that to continue, with the help of Blackwell Ultra and Dynamo. Speaking of which:

Blackwell Ultra is an update to the Blackwell platform. Per Nvidia’s investor materials, it comes with more compute scalability, large inference performance gains and is meant for the most complex agentic and physical AI use cases. It also released Blackwell Dynamo, which is essentially a batch of software optimizations to make its GPUs more efficient for goal-oriented agentic tasks. This is already enabling 30x throughput gains for Deepseek’s R1 reasoning models vs. pre-Dynamo Blackwell. Capital One is using Dynamo to reduce output token latency by 80%. Anything it can do to extract more hardware efficiency from the software side of its business means more differentiation vs. less vertically integrated competition. 

As CFO Colette rightfully said, this is why a fully integrated and world-class software arm to complement its GPUs is so important. It means Nvidia can launch unmatched hardware and then make it even more unmatched through this layer of innovation.

I think its NVLink Fusion launch is also worth highlighting. This allows companies to build “semi-custom” AI infrastructure with Nvidia and its integration ecosystem. GPUs are general-purpose in nature. They’re not granularly designed for every single niche use case like an Application-Specific Integrated Circuit (ASIC). This can help Nvidia capture more of that demand by pairing with Marvell and a few other partners and more easily emulate purpose-built hardware.

Data Center Networking & Supercomputer System Updates:

On the networking side, it launched its Spectrum-X and Quantum-X networking switches with silicon photonics. I had to look this up. This type of switch uses light-based and electricity-based components onto a single chip package, which means more compute density, more GPU connections and lower latency. Makes sense. To quantify this, power efficiency is up 250% with this approach, network resiliency is up 1,000% and customer time to market improved by 30%.

Perhaps most importantly, its Grace-Blackwell (GB) NVLink 72 Supercomputer entered full-scale production on schedule. It’s delivering far lower cost per inference and large manufacturing yield gains. Noticing a theme? Improvements are expected to keep rapidly rising and contributing to Blackwell being NVDA’s strongest product launch ever. The new GB 300 system (which uses Blackwell Ultra) should deliver another 50% boost to inference density and memory vs. GB 200. Notably, GB300 architecture is quite similar to GB200, which means lower-friction adoption and lower transition risk.

“NVLink is a new growth vector and is off to a great start with Q1 shipments exceeding a billion dollars.”

CFO Colette Kress

Singapore:

There has been some controversy about Singapore vastly over indexing in terms of chip orders per GDP. This is because most of its large clients “use Singapore for centralized invoicing.” 99% of Singapore billings were for U.S.-based orders. Great to hear.

Quick Notes on NeMo (already defined) Driving Great Outcomes:

Quick Notes on Other Revenue Segments:

Gaming and AI PCs benefitted from improving supply conditions. It expects that to keep playing out next quarter. Its developer platform (GeForce) now has the “largest footprint of PC developers.” It launched new Blackwell-featured RTX laptops that “double frame rate and slash latency.”

In Professional Visualization, tariffs hurt demand a bit during the quarter. Still, Omniverse (its product enabling affordable and scalable digital twin creation), is saving Taiwan Semi “months of work” and accelerating Foxconn thermal simulations by 150X. Pegatron is using Omniverse to lower assembly line defect rates by 67%.

In Automotive and Physical AI, Uber, Boston Dynamics and several more companies are using its Isaac Groot model for humanoid robot app developing and other physical AI use cases. It also officially released Cosmos, which is its world foundational model with an intimate understanding of laws of science for Physical AI apps.

Excellent quarter despite the China headwinds. The guidance is stellar when considering the revenue hit from H20 export bans is $3B larger than expected. This just goes to show how strong momentum remains for the rest of its business. As long as this cycle lasts, it is Nvidia’s world and we’re just living in it. Jensen is a superstar; this company is iconic; this quarter was again great. The beats were smaller than we’ve gotten used to, but that’s inevitable as Nvidia trains sell-siders to expect explosive upside every quarter. Eventually estimates naturally catch up. And regardless, the results they’re putting up at this scale are simply bonkers.