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As a product manager, I challenged myself on NBIS, who are the customers today, tomorrow, and what's the endgame? Surely it can't be just cloud compute.
Nebius is following a proven, sequenced go-to-market strategy that mirrors what they already executed at Yandex. The current AI cloud business is stage one which funnily enough "Yandex Cloud" was the last segment at Yandex before the spinoff with Nebius.
The segment they are building toward, Physical AI, is stage two. Physical AI demand is real, commercially viable, and structurally dependent on exactly the kind of compute Nebius sells.
With this article I hope you will understand three things: how Nebius's current customer segments work and why they exist in this order, what Physical AI actually looks like on the ground in terms of real products, real unit economics, and real adoption curves, and why the expansion into Physical AI follows the same adjacency logic that turned Yandex Search into Yandex Taxi, Market, and Cloud.
I've about 1400 shares (40% port) accumulating since 2021, and haven't sold since. Not financial advice.
# The Yandex Playbook is Running Again
Arkady Volozh founded Yandex in 1997 and built it into the search engine in Russia with >70% domestic market share against Google \[1\]. From that core, the team sequenced into ride-hailing (Yandex Taxi, 2011), e-commerce (Yandex Market), and cloud. Each adjacency unlocked new customer segments where after capturing a meaningful share, they moved on to a new one. Yandex Taxi ended up taking the Post-Soviet countries by storm while Yandex Market resulted in becoming a real competitor in the fulfillment business. When I mean by storm, I mean that the app and platform was so intuitive that even Eastern European/Central asian grandmas and grandpas were using it seamlessly.
By November 2021 Yandex reached a $31B peak market cap \[2\] and earned the title of "Google of Russia". After the Russian-Ukraine war, the Dutch holding company sold its Russian assets for $5.4B and rebranded as Nebius Group \[2\] but the team, the future blueprints and the sequencing discipline remained as Arkady enabled the safe transition for the entire workforce.
Let's move on to how this playbook now applies to Nebius today.
# Who are Nebius's customers today?
Nebius has two customer segments that serve completely different needs.
The first segment is AI-native startups like Cursor, Mistral and Black Forest Labs \[3\]. These are teams building AI products like AI-native IDEs, foundation models and image-to-video generation. They need a cloud platform where they can train models, run inference at scale, and ship product fast. They are buying the full software stack: GPU clusters, orchestration tools, ML operations, storage, and the developer ecosystem around it. They chose Nebius because it was purpose-built for AI workloads and gives them the elastic scaling they need to go from prototype to production without re-architecting their infrastructure.
From a commercial standpoint: these customers find Nebius, try the product, and expand their usage as their AI workloads grow. If Black Forest's FLUX sees high utilization, they rely on frustration-free scaling with Nebius to enable uninterrupted service. The reason why these companies appreciate Nebius is because it allows them to fully focus on their core competency and not have to worry about maintenance, scaling, integration or otherwise. They can focus on product development, marketing, distribution and branding. This is confirmed by Roman Chernin, Nebius Co-Founder and Chief Business Officer \[4\]:
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Customer acquisition costs are low given the onboarding is seamless. Retention is driven by Nebius platform being embedded into their workflows while Nebius provides white-glove support directly from Nebius engineers. This is all that a start-up really needs: a functional and intuitive platform to handle their high demand workflows, and a team ready to stand-by if it breaks.
The second segment is hyperscalers like Microsoft and Meta. These companies already have their own software. They do not need Nebius's platform tools and likely will never need them. What they need is raw GPU capacity, power, and cooling at massive scale so their own models can run seamlessly.
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Margins are lower for hyperscaler deals because they do not use Nebius software, so why does Nebius take these deals if the margins are lower and the customer relationship is thinner? Because the hyperscaler contracts are the financing engine. That capital flows directly into expanding capacity (more data centers, more clusters) for the higher-margin AI cloud business that serves the first segment.
In other words, the two segments are not competing for attention and are designed to work together.
# Who are the customers of tomorrow?
Chernin laid out the next customer segments explicitly. Beyond 2027, the target shifts to mainstream enterprises.
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This is exactly why the Nebius roadmap is so predictable for me.
It is a classic implementation of Geoffrey Moore's Go-To-Market strategy and the Nebius team has a clear understanding of the technology adoption lifecycle
In short, the technology adoption lifecycle showcases the various customer segments that a new and emerging technology must satisfy in order to be successful. It represents a psychographic of the entire user personas for these segments like early adopters, early majority, late majority and laggards.
There's one important detail to know: each customer segment, is dependent on the preceding segment to tell them "it's safe to use, you'll like it". Nebius wooed the visionaries and tech enthusiasts like Cursor and Black Forest labs who capture 16% of the customer base (left tail). These companies are always observed by the watchful pragmatists, the Microsoft and Meta of the world who are willing to try out a new tech, only if it's been proven to work in some fashion.
Attempting to go from early adopters to the early majority like Microsoft, is where most companies die.
Nebius has crossed the chasm. But the story does not end there because there is at least 50% of their customer base out there. This is why Chernin is mentioning Siemens.
Think about what a company like Siemens (late majority) actually needs. They run manufacturing operations across dozens of countries. They are beginning to deploy AI for quality inspection, predictive maintenance, and supply chain optimization. They do not have internal teams of GPU infrastructure engineers and they already use AWS or Azure for their general-purpose cloud workloads and they are not going to rip that out. What they need is a specialized AI compute provider that handles the GPU-intensive work their existing cloud vendor does poorly or prices uncompetitively.
That is the gap Nebius is positioning for. The product is not a replacement for AWS as it services too many areas that Nebius wont but rather Nebius is the compute layer for AI-specialized workflows that sits alongside AWS.
# Hows the progress with the late majority?
Two recent moves show you the team is already building the commercial infrastructure for this late majority segment. The TD SYNNEX partnership (October 2025) gives Nebius distribution through thousands of resellers and system integrators in North America \[7\]. This matters because enterprise procurement relationships take years to build and TD SYNNEX already has them.
What do I mean by enterprise procurement relationships needing years to be built?
In short, Siemens will never trust anyone who doesn't have the backing of another veteran company in the industry. Siemens needs to ensure that others have tried and succeeded in using a new vendor for any function, without this guinea pig, they will not sign anything.
Nebius through TD SYNNEX gets immediate access to the buying committees at companies like Siemens without needing to hire hundreds of enterprise sales reps first. They do not need to introduce themselves to the hundreds of late-majority companies to explain who they are, they use the reputation of TD SYNNEX to achieve that and get them in the door.
So now you have early adopters and early majority being serviced, and you're already penetrating into the late majority.
Let's summarize. Each one of these customer segment Nebius captures generates the revenue, credibility, and infrastructure that funds the acquisition of the next segment. AI-native startups proved the product and tech works which then convinced the hyperscalers to fund the build-out because they like to listen to the early adopters. The pragmatist hyperscalers also validated the platform for mainstream buyers like Siemens who wouldn't even touch Nebius with a 10-foot pole if Microsoft didn't give their blessing. Finally, once late-majority signs on to a service, they're usually in it for the long-haul until the pragmatists give their blessing to another up and coming leader.
But what happens next? Once Nebius captures the late majority, does the company stop growing? No, they move on to another niche!
That brings us to the final part of the roadmap (the end-game, so to speak)
# The Segment They Are Building Toward
Most people hear "Physical AI" and think humanoid robots walking around factories in 2040 but the reality in my opinion is much more mundane, not much different than today and more commercially grounded. I would go as far as to say, if you've seen how Amazon fulfillment centers operate, across conveyances, robotic arms, Kiva robots maneuvering shelves, then just imagine that in the future, it'll be outside of fulfillment centers.
But what scale are we even talking about?
Citi GPS published a detailed report in December 2024 sizing the Physical AI market across nine categories. They predict that 1.3 billion AI robots will be operational by 2035 \[8\]. But let's look at what Physical AI actually looks like today, what the unit economics are, and why every category depends on cloud compute.
# What is already scaled today?
Waymo is conducting 100,000 paid rides per week in the US \[8\], over 60 Chinese cities have issued AV road test licenses, and Tesla launched Robotaxi in Austin in 2025 \[8\]. Add to this, Citi forecasts 1.8 billion autonomous vehicles by 2050 with a 17.4% compound annual growth rate and the compute requirements for AV development rely on reinforcement learning all of which are GPU-intensive workloads that run in the cloud.
Then we have delivery robots which are scaling fast. Starship Technologies has completed over 6 million deliveries across 60+ locations worldwide where it took them 78 months to reach the first million deliveries and only 7.5 months on average for each of the next five million. On the domestic side, Avride, approximately 83% owned by Nebius, is already operating in two of the highest-growth categories Citi covers. It runs delivery robots in Austin, Dallas, and Jersey City through Uber Eats, and has university deployments logging roughly 1,300+ daily deliveries \[9\].
Humanoids are the newest category and the most futuristic of them all. Citi's payback period analysis shows that at a $25,000 unit price (which is what Elon Musk has projected for Tesla's Optimus), the payback period against a US factory worker earning $28/hour is approximately 9 weeks \[8\]. But It's going to take many years to achieve $25K production and it requires mass production. Citi forecasts 648 million humanoids and a $7 trillion humanoid market by 2050 \[8\].
Pretty damn wild numbers.
You may agree or disagree with the projections of Citi, but know that there will be new entrants that will attempt to capture this emerging market as well as incumbents who will look to expand their market share. In other words, companies are going to try hard to break in.
I believe that is where Nebius is the picks-and-shovel play for this segment.
Every single one of these categories depends on cloud compute. The autonomous vehicles need compute to run their simulations and the delivery robots need route optimization and obstacle detection models retrained on real-world data. The humanoids need multimodal AI that processes vision, language, and touch simultaneously which is the most intensive workloads.
The workloads are structurally identical whether you are training a code generation model or a robot navigation model, you need large clusters of the latest GPUs running for extended periods. A robotics company evaluating cloud providers cares about the same things an AI-native startup cares about like performance, uptime, elastic scaling and cost efficiency. Basically, for Nebius it doesn't matter if its an image-to-video generation platform using its inference or a robot, they'll provide the same service of sub-second latency and uptime to both.
Another key point by Geoffrey Moore (Godfather of go-to-market strategy) is how you need partners across the ecosystem in order to deliver a whole product. A whole product is an umbrella of value-added services and features, including partnerships when taken as a whole provides a compelling product for the customer. Nebius is tackling the partnership piece via The Nebius Robotics and Physical AI Summit where it provides AI cloud compute credits to robotics startups \[7\]. It's pretty neat, don't pay them money, but pay them in credits so they end up giving Nebius a trial and result in staying as a customer. It is the same strategy AWS used to become the default cloud for a generation of software startups: subsidize the early adopters, make them successful on your platform, and then capture the revenue as they scale.
If Nebius becomes the default compute platform for robotics companies the way it is becoming the default for AI-native software companies, the revenue growth compounds as the entire Physical AI market matures. If the Physical AI market doesn't expand and Citi is wrong, this is where the bear case sets in.
# The Bear Case
We are going to fall off a cliff if for whatever reason demand dies down, let there be no mistake. If the AI spending cycle corrects, Nebius is deploying $5B in capex into a market that may not absorb it. Empty data centers with hardware obligations are the worst outcome for a capital-intensive business. This is going to be catastrophic for the entire sector given +$600B in CapEx by Mag7 and will have profound effects to every company that ever used the word AI in their transcripts.
Supposedly Chernin's language shows they are aware of a potential "winter", they frequently cite "Supply will catch up to demand" in the future. But planning for a downturn and surviving one are different things, especially when your capex guidance tripled within a single fiscal year.
The other very real bear case, which not as catastrophic as above, but still stings is customer concentration. Just two hyperscaler contracts (Meta, Microsoft) represent their entire market cap. If Microsoft or Meta delays capacity, renegotiates terms, or builds competing internal capacity (which Meta is actively doing), the revenue trajectory shifts and dives off a cliff. Meta is actively building out data center capacity for themselves, even if they refuse to renew their 3 year contract, that will have an outsized impact on Nebius stock price as it'll invoke panic: "is Nebius not good enough? Have they lost their edge? Is this the end?"
The other piece is just plain old competition that could compress margins. CoreWeave, Lambda (Private), and other neoclouds are building similar offerings and as supply catches up with demand (and it will eventually), pricing power erodes. Nebius has a cost advantage from vertical integration, but cost advantages shrink in a price war. The question is whether the software layer and switching costs from the enterprise segment are established before the commoditization wave arrives.
In other words, can Nebius make the managed platform so sticky that it's just not worth switching?
Finally the Physical AI timelines are long, 2027 is optimistic in my view and the projections are too optimistic.
If the Physical AI segment takes five years longer than expected, the stock has to be justified entirely on the AI cloud business. Investors should be clear about what they are paying for today versus what they are paying for in expectation.
# What the Evidence Tells Us
The reason I wrote this article is because I think the Nebius investment narrative is incomplete. Most FinX coverage focuses on whether the AI cloud business can hit its ARR targets and whether the hyperscaler contracts justify the valuation. Those are fine questions, but they evaluate Nebius as a static business rather than a team running a sequenced playbook where each phase is designed to fund and de-risk the one that follows. These mathematical projections by FinX are not really understanding the product roadmap and business strategy that is to come, hence the article.
When I look at this company through the lens of customer segmentation and product strategy, the roadmap becomes readable. Nebius captured the innovators and early adopters (Cursor, Mistral, Black Forest Labs) by building a platform that solves their specific pain: elastic, full-stack AI compute with engineering support, so they can focus on shipping product. That customer base gave Nebius the credibility to cross the chasm and sign multi-billion dollar contracts with pragmatists like Microsoft and Meta, who validated the infrastructure for the broader market. Those contracts open the door to the late majority, the Siemens and BMWs of the world, who will need specialized AI compute at scale but will never build it themselves. The revenue stability that comes from serving enterprise customers across the adoption lifecycle is what makes a long-horizon adjacency like Physical AI financially viable rather than reckless.
And then there is Avride, Nebius keeps teasing this (at least that's what it feels like to me) but maybe I'm over-analyzing.
Nebius owns 83% of a company that is already running robotaxis on the Uber platform and delivering food through Uber Eats across multiple US cities and Japan. A cloud provider can only pitch robotics companies on Nebius infrastructure specs and pricing but can Nebius point to Avride and say: we will run our own autonomous driving and delivery operations on this platform, at production scale, in the real world?
Basically what if Nebius says: we can do robotics & autonomous better, and we will.
That is a fundamentally different sales conversation because it proves the platform handles Physical AI workloads under real constraints with real evidence, not just in benchmarks. Avride is simultaneously a revenue-generating robotics business, an internal proof-of-concept for the AI cloud, and a reference customer that attracts external robotics companies to the platform.
# Two things I would encourage investors to watch
First, whether robotics and autonomous systems companies start showing up in Nebius's customer disclosures the way Cursor and Mistral did in 2025, because that confirms external demand for Physical AI compute is flowing through neocloud providers.
Second, whether Avride's commercial operations are visibly scaling on Nebius infrastructure, meaning larger fleets, more geographies, and growing delivery volumes, because that confirms the internal proof-of-concept is compounding. Both signals together would validate that Nebius is capturing the Physical AI segment from both sides:
1. selling the picks and shovels to the broader market
2. while building with them through Avride.
If those signals show up, the Nebius thesis expands from "fast-growing AI cloud provider" to "the compute backbone for both digital and physical AI," which is a meaningfully larger addressable market and a meaningfully different valuation framework.
We are not there yet. But the pieces are on the board, the sequencing is deliberate, and the team has done this before: In 2019, Yandex reported that its autonomous vehicle fleet had driven over 1 million miles in autonomous mode, joining the "million-mile club".
# Sources
\[1\] Statista - Yandex domestic search market share (\~72%)
\[2\] TechCrunch - "Yandex to sell its remaining Russian businesses for $5.2B" (Feb 2024); peak market cap $31B (Nov 2021)
\[3\] Nebius Group Q3 2025 Earnings Call Transcript - Cursor, Black Forest Labs, World Labs named as customers
\[4\] The Information - "Nebius Co-founder, Roman Chernin, on the Future of AI Models" Roman Chernin interview
\[5\] Nebius Group Q4/FY2025 Earnings - $1.2B ARR exiting 2025; 830% YoY core AI cloud growth Q4; capacity/power guidance
\[6\] Reuters -"Nebius leverages Microsoft, Meta contracts for AI expansion" (Dec 3, 2025) Roman Chernin interview
\[7\] StockTitan - TD SYNNEX partnership; Tavily acquisition ($275M, Feb 2026); Robotics and Physical AI Summit
\[8\] Citi GPS- "The Rise of AI Robots: Physical AI is Coming for You" (Dec 2024) - 4.1B robot forecast, gating factors, use-case methodology, payback analysis, adoption data