저자: DanIves및진행자미상 · 외국인
요약 보기
AI 인프라 수요가 1~2년간 시장 과소평가 중이며 실제 가치는 엔터프라이즈 소프트웨어로 이동. 팔란티어, 서비스나우, 스노우플레이크를 엔터프라이즈 AI 수혜주로 제시. 중간선거 변동성 언급하나 펀더멘털이 결국 주가를 결정한다고 주장. 영상 내 수치 다수는 자동인식 오류 가능성 있음.
다듬은 글
We will walk through these narratives. Remember the “ SaaS apocalypse” earlier this year. Look what's happening here. I'm not saying that there's no point in this . Of course, there is some truth to the excitement we have seen around bonds and CDS spreads. But the reality is that this acceleration is happening because of the demand for chips, hyperscalers, and use cases.
And that's why I think the reporting season can be optimistic. But I think compared to where we are now, this is just the beginning. If you look at demand, at capacity, and where things are headed. Even if we discard the part of data centers that will ultimately not be built, I still believe that the market is massively underestimating the numbers for the next 1-2 years.
We are approaching the midterm elections. There is always great volatility during this period . But in the end, it all comes down to fundamentals. What is the demand for chips? What are enterprises doing about hyperscalers? And what do the usage scenarios look like? This is what will determine whether stocks will go up.
And I still believe that we are in the third year of a 10-12 year development cycle. Dan Ives has just highlighted the main arithmetic that defines the modern technological landscape. While financial markets worry about whether hyperscalers are overpaying for AI infrastructure, the main value is being shifted with a multiplier of $5-6 directly into enterprise software deployment.
Published financial data confirms that while hyperscalers' capital expenditures approach a total multi-year commitment of $800 billion, enterprise-grade software is turning that computing power into growing cash flows. Leading software developers are increasing total contract value by more than 140% year-over-year, driven by revenue retention rates of 125% to 157% on enterprise AI platforms.
This indicator signals that corporations are moving from initial model testing to full scale, integration of automated workflows, and active use of data lakes. To get this multiplier effect without the direct risks associated with semiconductors or capital-intensive infrastructure, the market's attention is focused on three enterprise software vendors.
The first is an AI operating system provider that delivers over 85% quarterly revenue growth and maintains a 150% revenue retention rate by transforming complex language models into working enterprise processes . The second is an enterprise workflow platform that supports a 98% contract renewal rate and generates multi-billion dollar subscription revenue by automating enterprise IT operations.
The third is a cloud data platform that supports pay-as-you- go enterprise consumption models, with over nine billion dollars in unfulfilled commitments. Topping our list of high-potential companies transforming artificial intelligence into ready-to- implement enterprise solutions . While the prevailing financial narrative suggests that high infrastructure costs will reduce software profitability, Palantir's lightweight architecture has found itself on the winning side of a fundamental margin shift.
With over $9.2 billion in cash and short-term treasury obligations without any data centers, the company is buying computing power rather than building it. As the value of AI tokens falls, Palantir's underlying cost of revenue also decreases, allowing lower compute prices to directly impact gross and operating profit growth.
In addition to reducing computing costs , Palantir unlocks unprecedented operational leverage through its partner-driven deployment model . Historically, scaling required hiring expensive engineers who worked in the field to integrate software. Today, strategic partners like Fujitsu, Bain, and specialized hardware alliances fund engineering , manage deployment, and bear customers' infrastructure costs while Palantir licenses its highly profitable platform.
This structural shift has already increased adjusted operating margin to 62 %, significantly outperforming traditional software competitors. Moreover, its proprietary AI platform , AIP, connects enterprise data directly to real-world operations through its own ontology, generating an impressive “ Rule of 40” score of 155%—nearly four times higher than the industry benchmark.
This momentum is underpinned by explosive revenue growth, highlighted by 93% year-over-year total revenue growth and a staggering 149% jump in US commercial revenue. Importantly, Palantir maintains a huge competitive edge thanks to its deep integration with the government, backed by the highest level of security IL6 and high-profile defense contracts such as the TITAN system for the US Army.
With over $6.2 billion in residual value of commercial deals in the U.S. and an expanding pipeline in both the public and private sectors, Palantir operates as a neutral operating system, independent of models. Rather than taking on the risks associated with semiconductors or capital-intensive equipment, Palantir remains ideally positioned to capture the multibillion-dollar wave of enterprise software as organizations move from experimentation to full-scale deployment.
Moving on to our second key software titan , ServiceNow Inc., ticker NOW, represents an enterprise orchestration powerhouse that is undergoing a structural transformation from a traditional SaaS model to a fully integrated platform as a service. While there have been concerns in the broader software sector that autonomous AI agents could replace legacy seat-based licensing, ServiceNow's deeply embedded architecture turns generative tools into a direct source of revenue.
By moving from staff-limited pricing to consumption levels known as “assists,” the company is effectively removing the ceiling on revenue growth. Instead of relying solely on the number of employees, revenue is continuously scaled as autonomous digital agents perform millions of cross-departmental transactions .
At the heart of this extension is a single data foundation that combines a configuration management database and a workflow data structure. This architecture eliminates corporate information silos across IT, human resources, customer service, security, and application development. By organizing workflows through a central action system, ServiceNow acts as an essential control tower, managing both human workers and autonomous digital identities under a single set of governance rules.
This deep integration is reflected in extraordinary operational stability, as evidenced by customer retention rates that consistently hover at 97-98% among over 8,800 enterprise customers, including nearly 90% of the Fortune 500. The financial impact of this shift in consumption is already gaining significant momentum.
The annual value of AI contracts has surpassed $1 billion, heading towards $1.5 billion by the end of the year, and is projected to account for nearly 30% of total annual contract value by 2030. Over the past 12 months, subscription revenue exceeded $14.3 billion with constant currency expansion of over 22%, underpinned by huge contract commitments reaching $29 billion .
Additionally, non-GAAP operating margin remains at 31%, generating over $4.6 billion in free cash flow for the year, while management is targeting a long-term “rule of 60” profile with a free cash flow margin of over 40%. Rather than being supplanted by standalone models, ServiceNow remains uniquely positioned as the indispensable infrastructure needed to deploy, regulate, and scale generative AI on a global scale .
Rounding out our trio of enterprise transformation projects is Snowflake Inc., ticker SNOW, a foundational cloud data platform that serves as the indispensable data layer for every enterprise AI application. Before any corporation can deploy advanced language models or agent-based workflows, its data must be unified, structured, and accessible without compromising security or management.
Snowflake's cloud-based architecture decouples compute from storage, allowing enterprises to run heavy machine learning workloads and real-time query engines on huge unstructured data sets. Because the $ 1 to $6 multiplier shifts costs toward enterprise tasks, Snowflake sits directly at the “ gateway” where raw data is transformed into useful analytics.
Unlike legacy hard databases, Snowflake operates on a purely consumption-based monetization model, where customers pay solely for the computing power and storage resources they use. As enterprises move from initial model testing to running autonomous AI agents in thousands of daily workflows, data consumption grows exponentially.
This dynamic is amplified by Snowflake Cortex AI, which enables non-technical business teams to build their own generative AI applications and query massive data lakes using simple natural language. By embedding large language models directly within the customer's protected data boundaries, Snowflake eliminates the security risk associated with transferring sensitive corporate data to external third-party model providers.
The quantifiable strength of Snowflake's usage model is proven by its stellar performance and the huge amount of revenue waiting to be recognized. Over the past 12 months, the company generated over $3.2 billion in product revenue, delivering a strong 35% year-over-year revenue growth rate . Corporate customer commitments remain extremely strong, as evidenced by an impressive $5.2 billion in remaining contract obligations (RPOs), while net revenue retention remains stable at 127%.
With over 10,000 active enterprise accounts, including over 500 customers generating over $1 million in product revenue in the last 12 months, Snowflake continues to address the fundamental data costs required to scale next-generation enterprise AI . As model inference costs continue to decline, the volume of enterprise queries in Snowflake's data cloud will only grow , providing sustained, multi-year revenue growth momentum.
As Dan Ives rightly pointed out, the real opportunity lies not just in building the infrastructure, but in capturing the wave of further implementation in the $1 to $6 range. By anchoring your portfolio with category leaders like Palantir, ServiceNow, and Snowflake, you benefit from real enterprise monetization, huge order backlogs, and expanded operating leverage. Subscribe now for deeper institutional market analysis .
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