NeutralMSFTMicrosoftMicrosoftQQQNasdaqNasdaq

4d ago

MS Portfolio Update | Oct 2026

September was all about macro. Oil touched $100 again, the Fed raised rates for the first time since 2023, and the yen carry trade once again raised concerns about a broader market sell-off. Despite the headlines, the Nasdaq gained 3%, driven by semis, with the SOXX index up 18% as it recovered from the July drawdown. Meta’s Muse launch helped the semis rally by renewing expectations for AI-driven semiconductor demand. But beneath all the noise, one thing started to change decisively: interest rates . After a long time, bond yields started to take center stage for all investors, as yields kept rising to new highs, with the current 10-year yield at ~5.25%. Higher bond yields generally mean one of two things: increasing risk in the overall economy or high demand for the limited capital available. Either way, this will limit companies’ ability to invest in new projects, and the benchmark returns investors demand will keep climbing as yields rise. Historically, rising rates marked the end of major investment cycles in railways, the internet boom, and housing. With rates now moving higher again, the question is whether today’s AI investments continue to grow despite rising costs of capital. What’s pushing yields up Long-term yields are driven by two main factors: inflation expectations and the term premium, i.e., the additional return investors demand for locking in their money over long horizons. Inflation expectations are rising, with oil hovering around $100, tariffs feeding into prices, and loose fiscal policy, with the deficit at ~7% of GDP. Inflation has been so sticky that the Fed is now expected to raise rates 2 times this year to fight inflation. The term premium is set by supply and demand for bonds, and both sides are pressuring yields. On the demand side, some of the largest U.S. bondholders, like Japan and Norway, are looking to offload their Treasuries to fund other agendas. Japan wants to defend the yen against the USD and needs more money to intervene in the market. Norway wants to fund AI capex. On the supply side, hyperscalers are increasingly competing with U.S. Treasury bonds for the same pool of capital. It can because the S&P rates Microsoft bonds at AAA, which is one notch above the federal govt. itself. AI capex is expected to pull in ~$300B of debt in 2026 and a further ~$400B+ in 2027, with total AI capex reaching ~$1.2T in 2027. This would make hyperscaler debt issuance equivalent to ~40%+ of fresh Treasury bond issuance. Source: JP Morgan Research AI debt remains small in aggregate, but on a new-issuance basis, the additional supply is significant. As hyperscalers increasingly rely on debt to fund AI infrastructure, they are competing with Treasuries for the same pool of capital, putting pressure on yields. How we are positioning: There is no way out of high yields without less borrowing for AI capex or by the U.S. government, along with lower inflation expectations. But a slowdown in AI capex or government spending would hurt the market. With AI capex estimates now at $1.2T, our analysis of sustainable capex suggests this should generate ~$2T in revenue for infrastructure companies. As interest rates climb and infrastructure firms slowly ramp revenue, we think we are entering a phase where further AI capex growth beyond 2027 will be limited, if it continues to grow at all. That said, we are not forecasting an end to the AI capex boom. We see AI as a transformative technology and expect continued investment, but the pace will moderate. The pace of AI capex going forward will be determined by how quickly new applications emerge that use AI. For instance, Muse is one such example. Therefore, we see this as a good time to slowly shift our portfolio from companies largely driven by demand generated by AI debt (i.e., semis) to other AI enablers that drive the diffusion of the technology into the economy. To summarize, despite a good September, markets have a tightrope to walk from here, with multiple macro headwinds and little room for further upgrades to the AI capex outlook. Given this evolving situation, we are doubling down on the broadening of AI trade we highlighted earlier and focusing on AI enablers . Portfolio Update & Position Changes: As part of our portfolio transition from AI Capex-driven companies to companies enabling AI adoption, we are making the following changes: Read more

NOWServiceNow, Inc.NeutralServiceNow, Inc.

1w ago

Deep Dive: ServiceNow Inc ($NOW)

Most software companies are built to solve problems for business heads and CXOs. That makes them systems of record, i.e., a single source of truth for one function or a dashboard for the people running it. Salesforce and HubSpot are the systems of record for customer and sales data, Workday for employee data, and SAP for finance. Automations built in them work within their own departments, like sales emails in Salesforce or payroll in Workday. Companies’ workflows, however, span across departments. Take, for instance, a new employee joining a large corporation. First, HR enters the employee in Workday. Then they raise requests to IT for a laptop, to security for app access, and to facilities for a desk and badge, with her manager chasing all of them. This is how work gets done in most large companies. Employees bounce across teams, tools, and people, chasing data and permissions to finish a task. Work that moves between these systems has no single owner. ServiceNow was built for these kinds of workflows spanning across teams. Its founder, Fred Luddy, was inspired by the pink and yellow paper slips that carried approval requests from one department to the next through company mailrooms in the 1990s. Fred Luddy turned that slip system into software. Once an employee files a request, the platform sends it to the right person to approve or act on it and notifies anyone impacted. Over time, the platform became an important layer that moves work across internal systems run by a company. Hundreds of ready-made connectors can be plugged into ServiceNow to connect Workday, Salesforce, SAP, etc. With ServiceNow, the new hire’s Workday entry opens every task at once. IT gets a ticket to ship the laptop, Okta creates her logins, and facilities assigns a desk. Her manager tracks it all in one place. While Workday remains the system of record, ServiceNow runs everything needed for onboarding. Most enterprise tasks touch the IT department one way or another, and that’s where ServiceNow started. IT provisions logins in Okta, secures laptops in Intune, fixes servers flagged by Datadog, etc. NOW tracks all of those apps, laptops, and servers and how they connect. From IT, ServiceNow expands department by department. When HR or legal needed software for their own workflows, IT recommended the platform it already had, i.e., NOW. Today, HR cases, customer service, and security incidents run on the same system as IT tickets, and that’s how NOW differentiated itself in the SaaS space. Business Overview: ServiceNow’s primary revenue source is its workflow software subscriptions, which account for ~97% of revenue ( $12.9B of $13.3B in FY25 ). The small remainder is professional services, used mainly to implement its workflow products. The subscription model gives NOW a business with low cyclicality. It starts each year with largely assured revenue unless a customer exits. Its gross revenue retention rate typically holds at ~98% , reflecting how sticky the workflows are once onboarded. This stable income also gives it enough bandwidth to reinvest in the business and expand its offerings, improving its chances of sustaining long-term growth. ServiceNow reinvests in two forms: S&M: Sales reps continuously engage with customers to understand their workflows and educate them on NOW’s capabilities, acting as a two-way channel between NOW’s product team and its customers. NOW spent 33% of revenue on GAAP S&M in FY25, down from 35% in FY24, and expects it to fall slightly again in FY26 as sales productivity improves. R&D: This covers product development and maintenance. The platform needs continuous upgrades to support its growing enterprise scale, and AI is driving further product innovation. NOW spent 22% of revenue on GAAP R&D in FY25. Business Segments: ServiceNow has expanded its workflows from core IT services into customer service, HR, legal, finance, etc. It categorizes its workflow business into three broad sections: Technology, Customer & Employee, and Creator workflows. Technology workflows: This is their core IT service desk workflow segment. They have expanded this to include adjacent workflows such as Security Operations and hardware asset management. This business typically accounts for ~50% of their net-new ACV (Annual Contract Value) and remains a major growth driver. Customer & Employee workflows: This business consists of workflows for sales teams, employee onboarding, legal, etc. Their customer workflows have crossed $2B in ACV, while their employee workflows have crossed $1B in ACV. This business typically accounts for ~30% of new business and goes head-to-head with other major software firms like Salesforce for customer solutions and Workday for HR solutions. Creator workflows: This is their low-code and no-code software toolkit, including App Engine and Automation Engine, which allows regular, non-technical employees to build custom business applications and automate tasks without needing to write complex code. This accounts for ~20% of their new business. Growth Strategy: ServiceNow’s growth has two parts: landing new customers and expanding by selling more to existing ones. Landing is close to tapped out. It already serves over 85% of the Fortune 500, and its customer base grew only ~5% in 2025, from 8.4k to 8.8k. There aren’t many large enterprises left to sign. That makes expansion the primary driver of the ~20% revenue growth it is achieving, and the platform is built for sustained expansion across workflows. This is reflected in its revenue per customer almost doubling from ~$700k in 2020 to ~$1.5M in 2025. It currently has 658 customers paying more than $5M per year, reflecting the scale to which NOW can grow within an enterprise. ServiceNow continues to grow across all its customer cohorts, including $5M+ customers, as it enables AI capabilities and captures more workflows. Product & Moat : ServiceNow’s edge is simple. It can run a single piece of work across many different software tools and track it from start to finish. What makes this possible is its Configuration Management Database, or CMDB. Think of the CMDB as a live map of a company’s technology. Every server, app, laptop, and service is on it. So is every connection between them and the teams responsible for each one. Here is how CMDB helps: say a bank’s payments app slows down. ServiceNow logs it as an incident. The CMDB immediately shows which server and services enable the payment app and notifies the owner. It also identifies which customer service depends on the payment app and notifies the customer and others impacted. The tickets go straight to every person responsible for the slowdown and impacted by it. Without that CMDB map, a problem like this means a dozen people on a call trying to work out what broke. With it, the work finds the right people automatically. In October 2024, ServiceNow added a second layer called workflow data fabric. While CMDB maps a company’s technology, workflow data fabric reaches into its data. It lets ServiceNow read and use information stored in other systems, like a Snowflake or Databricks data warehouse, without copying it over. This covers neat tables of numbers as well as messy data like emails and documents. Together, the CMDB and Workflow Data Fabric give ServiceNow’s AI agents what most AI tools lack: context. Let’s go back to the payments app. The CMDB tells the agent which server failed and who owns it. Workflow Data Fabric lets it pull the failed transactions from the bank’s data warehouse to see how many customers were affected and can notify them as the service comes back online. One tells the agent how things connect. The other gives it the data to act on. AI agents are only as useful as what they can see, and every new workflow a customer builds on ServiceNow adds to the picture of how enterprise workflows flow. This is a big reason NOW’s AI products crossed $1B in annual contract value ( what customers are signed up to pay each year ) in under 3 years. Most AI startups would envy that. None of this works out of the box. The CMDB and workflow data fabric only become valuable once they are wired into the hundreds of apps and data sources a company already uses. That takes time. NOW’s 10-K says each rollout depends on the customer’s integration, data migration, compliance, and security needs. This can take several months to quarters. That effort is what makes NOW hard to leave. Switching means rebuilding every one of those connections in a new product. And because NOW’s workflows run across departments like IT, HR, and customer service, a company cannot move one at a time to another product without breaking the links between them. The numbers back this up: NOW’s renewal rate has been 98% of contract value for years. Over time, NOW stops being just another tool and becomes the place where work gets done. This goes well beyond corporations. Nearly all US states use the platform. The City of Raleigh, North Carolina, used NOW’s AI agents to cut its IT help desk costs by 66% ( Q2 FY26 ). If I’m a CIO, I’m not ripping that out to save a bit on licensing. ServiceNow AI Control Tower: Earlier, we saw how CMDB and Workflow Data Fabric keep NOW at the center of enterprise AI agent adoption. Its pole position in enterprise workflows makes it a natural place to run those agents, and it has extended this capability into a new product called AI Control Tower ( launched in May 2025 ). AI Control Tower is a command center for every AI agent and workflow in an enterprise, whether it’s built on ServiceNow or elsewhere. It discovers them and applies standard policies, ensures compliance, and measures what each one delivers. This is CMDB adjacency, just like a CMDB registers servers/apps and their dependencies. AI Control Tower registers agents, what they can access, and who owns them. Because NOW already holds the map of the workflows these agents need to act on in CMDB, it can identify when agents sidetrack. With AI Control Tower, NOW positions itself above the AI vendors, entrenching itself as an enterprise AI platform rather than being absorbed by one. AI impact on NOW’s Business & Pricing: NOW has traditionally priced its product on a per-seat basis, licensed by fulfiller, i.e., employees who work on the platform auto-remediating tickets and building workflows. Some products, like IT asset management, are priced by devices managed, connectors, data transferred, etc. AI makes their seats more productive, so fewer of them clear the same work. Street is concerned about the impact of seat compression on NOW’s revenue and their ability to mark up pricing for any loss of seats. The company now trades at $137, ~40% below its last year high. So it all comes down to whether AI will be a headwind or a tailwind for the company. To find out, let’s dig into: How ServiceNow is pricing its AI products Which companies can realistically challenge them Current profitability and stock-based compensation concerns NOW DCF Model and Market Sentiment rating Read more

AMZNAmazonNeutralAmazonBKNGBookingBearishBooking

1w ago

Notes on Muse

Mainstream AI agents like Meta's Muse could disintermediate travel booking intermediaries such as Booking Holdings.

NeutralCRMSalesforceSalesforceIWMRussell 2000Russell 2000

2w ago

The next great AI trade is everything that isn’t AI

Grab a cup of coffee for this one. It’s one of our most in-depth reports (and it’s free!). Successful investing comes down to finding a trend early and knowing when the thesis has become consensus. It’s the second part that trips up so many investors. While riding the trend up, it’s easy to think that it’s going to run forever. All the data and analyst projections point that way. But that’s rarely the case. Right now, that is the AI infrastructure trade. Capital has crowded into a handful of winners, with valuations increasingly pricing in years of uninterrupted growth. While this has certainly helped our portfolio (+55.3% YTD), many of the obvious AI bottleneck trades have already become consensus. We are bullish on the ROI AI capex will generate, but skeptical about how much projections will rise next quarter, especially since the ones running the show are clamoring to slow development. Any blip, however minor, will erase years of gains from these stocks. Plus, the Fed hiking rates will not be great for semiconductor companies — the real worry is not the 25bps hike: it’s a combination of a new hawkish Fed chair, sticky inflation (due to tariffs and the Iran war) , and rising national debt. Rate hike sensitivity | Source: Market Sentiment Research Who makes the money with transformative technologies? It’s not the first time investors have bet on a transformative technology to change the world. We have two good historical precedents: railroads and the internet. In both cases, we invested a single-digit % of our GDP into building railroads and telecommunications infrastructure. While both these technologies ended up changing the world, neither rewarded the long-term investors who funded the buildout. The key being long-term: short-term traders would have made a killing trading the volatility, but the index was more or less in the same place after the hype was over. Source: sparklinecapital The problem with aggressive capital spending is that it’s almost always suboptimal. After the initial euphoria of rising capex driving up stock prices, you will realize you overpaid for everything from the land to the memory to even the fiber-optic cables . The industry that creates the technology rarely captures the value it creates. The railroad expansion saw hundreds of railroad companies go bankrupt, and the dot-com bust saw the telecom index collapse by 92%. Yet the infrastructure those failures left behind became the foundation for the winners that came after. Standard Oil and Sears built their empires on the railroads, and Amazon and Netflix were built on the cheap bandwidth created by the fiber glut. The lasting winners in AI will also be the companies building on top of the infrastructure, not the ones laying it down. While Cisco's example is beaten to death, it’s still worth comparing it to Amazon. Cisco built the internet's plumbing: the routers and switches every website ran on, and was the world’s most valuable company in 2000. Then the bubble burst, and it took the company ~23 years to regain its ATH. Yet, even after the recent AI run-up, the company is 1/6th the size of Amazon, which used the same network to become one of the most valuable companies ever! All this leads to one simple fact: Companies benefiting from AI have become the most underrated trade. Here’s something we can guarantee most of you missed during all the AI hype: In 2026, the Russell 2000 outperformed both the S&P 500 and the Nasdaq 100. The same trend holds over the trailing year. YTD Performance | As of 16th Sep 2026 So why is an index of 2,000 small-cap companies outperforming one built on the very names everyone expects to change the world? We think the answer sets up one of the largest capital rotations in years. Tracking the Token Next year, AI Capex is expected to be ~$1.2T+. Based on our capex sustainability model , this should drive ~$2T of incremental revenue. This will be a fraction of the overall incremental revenue generated using AI (as a business, you don’t spend $100 on something unless it makes you back 2-3x that amount). The question then is: who will be the beneficiaries who capture this value? The simplest way to find out is to track the value chain. Take the example of Oil. The driller sells it for ~$70, the refiner turns it into jet fuel worth ~$90, the distributor sells it to the airline for $100, and the airline turns that fuel into a $400 seat. As the commodity moves further downstream, each step adds value. Commodity conversion doesn’t happen automatically; it requires unique assets. In this case, those assets are refineries, pipelines, and airplanes. These are the companies that benefit from adopting innovations and new tools that enable new commodities. Apply the same logic to AI tokens, and one industry is massively benefiting: Software . But not just any software — the ones with moats that AI cannot erode: proprietary data, distribution, workflow lock-in, or system-of-record status. Everything thinner will get squeezed by falling token prices, just like what happened with Fiverr. AI now handles the translation, basic design, and entry-level code that its marketplace was built to broker, and 2026 revenue is set to fall 14 to 17 percent. So who are the beneficiaries? 1. Cybersecurity While there is a lot of hype related to “ agents escaping sandboxes ”, our take is simpler: Frontier models have handed even a mediocre hacker the coding ability of the top 1%. We expect the number of attacks, especially on weak systems, to rise sharply. The diagram above shows how companies think about this trade-off. Security spending is weighed against the cost of a breach, and total cost bottoms out at an optimal level of defense. More attacks raise the expected cost of failure and push that optimal point toward heavier security. The problem is that companies cannot respond by banning AI, because the productivity gain is too large to give up. So the balance shifts to a narrower question: give employees access to AI while placing strong guardrails on what data it can and cannot reach. Which brings us to: 2. Horizontal & Vertical Software Applications Horizontal platforms such as CRM, IT service management, and ERP run across the enterprise. They own the systems of record, the permission structures, and the workflows that agents must read from and act on. In an agentic environment, that ownership is the advantage. An agent cannot close a deal, resolve a ticket, or post a journal entry without touching these systems, which hands incumbents a durable role. ServiceNow shows the transition working. Its AI book crossed $1 billion in annual contract value in Q2 2026, with agentic AI in production up 9x in nine months. Vertical applications, built for a single industry, can hold an even stronger moat. Their workflows are specific, and building useful AI for them requires deep process knowledge that is hard to replicate. Guidewire in insurance, Procore in construction, and Autodesk in design are the type of businesses this favors. 3. Development Infrastructure As the cost of building software falls to zero, a lot more applications will get built. As more applications are built and deployed, the infrastructure and tooling needed to develop, connect, secure, and operate them should grow with them. MongoDB, for example, has expanded beyond its traditional database offering with AI capabilities such as vector search, allowing developers to build AI applications on top of existing data infrastructure. Similarly, companies such as Palantir and Snowflake provide platforms that enable enterprises to develop and deploy AI applications using their proprietary data and workflows. 4. Recommendation Engines An often overlooked beneficiary is ad targeting and recommendations. Platforms with large consumer bases gain the most, since AI improves with more user data. Reddit and Meta use it to match users with more relevant content and products, boosting both engagement and ad performance. Unity shows a related version of the same idea: AI lowers the cost of building a game and then improves how those games are discovered and monetized. PocketFM takes it further, using AI-generated content to expand its catalog and scale its audience across global markets at low marginal cost. 5. Low-cost Compute Not every task needs a frontier model. Writing an email response or updating a deck does not require Claude Fable 5. An open-source model can do the same work at a tenth of the cost or less, and as open models get smarter, the share of workloads running on them will keep expanding. Usage will keep exploding while the price per token collapses, so the pie grows even as every workload gets cost-optimized. That means serving intelligence becomes a race to the lowest cost per token, the same way Japanese automakers won the US market by delivering most of the performance at a much better price. If that is the race, hyperscalers have no option but ASICs . Cheap intelligence into durable margin Ultimately, it comes down to this — if intelligence becomes abundant and cheap, who stands to benefit? Our answer starts with proprietary data a model cannot reconstruct. But the bar is higher than owning data, and the losers look like winners until they don't. Chegg owned one of the largest databases of solved academic problems in existence, reportedly more than 79 million of them. It did not matter. A free general model answered the same questions well enough; Chegg had no workflow to hold the student. The result was inevitable: The second filter is trust but verify, because AI-washing is everywhere. Every management team now claims to be a winner, and the metrics are easy to dress up. Salesforce booked over $1.5 billion in Agentforce ARR this quarter, but only after folding Slackbot and other products into the definition. We will judge adoption by usage, pricing, and margin, not by the earnings call. This is why we are tilting toward companies that convert cheap intelligence into durable margin, mainly in software and service-heavy sectors, and we will rotate out of some capex-driven semiconductor holdings over time to fund it. Subscribers will get the full portfolio changes next week (before we execute them) . Please consider upgrading your subscription to support our work and to get access to all the reports. Subscribe now If you made it till here, I would love to hear what you think: Leave a comment Disclaimer: Market Sentiment work is provided for informational purposes only, is intended solely for readers in the United States, and should not be construed as legal, business, investment, or tax advice. You should always do your own research.

NVDANVIDIABullishNVIDIA

3w ago

Notes on Open Source

Open-source models pressure AI lab margins, not AI infrastructure value; author keeps holding infrastructure