& my current portfolio/performance.

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Guidance & Valuation:

Okta trades for 28x forward EPS. EPS is expected to grow by 19.8% this year and by 6.7% next year.

Demand:

Profits:

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Guidance & Valuation:

MRVL trades for 21x forward EPS. EPS is expected to grow by 78% Y/Y this year and by 22% Y/Y next year.

Demand:

Profits:

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Guidance & Valuation:

Affirm guided to at least $3.86B in revenue for next year. This led to estimates for the year rising by 2.8% to $3.99B. It also guided to 26%+ EBIT margin, which led to EBIT estimates for the year rising by about 20%. 6% GAAP EBIT margin guidance led to EPS estimates rising from $0.75 to $0.89 Y/Y.

Affirm trades for 35x forward EPS. EPS is expected to grow by 41% this year and by 37% next year.

Data Cloud Foundation:

Snowflake’s overarching platform is called the (AI-fueled) Data Cloud – a “single foundation to eliminate data silos” – with a relational database makeup. Relational uses structured query language (SQL) and means data is stored in rows and columns to make insight gleaning straightforward. This differs from document-oriented database vendors like MongoDB, which use Not Only SQL (NoSQL) and stores data in (as the name indicates) flexible documents. Snowflake is considered best suited for online analytical processing (OLAP), while document-oriented is considered best for online transactional processing (OLTP). Both would subjectively argue that their approaches are best suited for handling unstructured data and for AI. And both are trying to encroach on the other’s territory.

Snowflake’s infrastructure unlocks affordable data storage, organization, querying and learning at gigantic scale. It offers these services with elastic compute capabilities, allowing for flexible scaling up and down of usage. The architecture naturally separates the functions of data storage and consumption, unlike legacy data warehouse solutions. That’s why it’s perfect for OLAP, as it means data consumption capacity is untethered from public computing resources. In turn, this helps control costs and waste, handle diverse workloads and resolve potential scale bottlenecks.

Under this framework, I can store as much data as I want to without the requirement of immediate processing. In Snowflake’s case, the storage is done in a centralized data repository in the Data Cloud and processed only as needed. Data is utilized virtually, which removes the need for dedicated customer hardware. Snowflake does all of this for clients in a managed fashion, minimizing talent and infrastructure needs. All of this routinely delivers 50% total cost of ownership (TCO) reductions for its customers.

Snowflake splits its products into 4 categories:

Analytics Category:

This includes the Snowflake Data Warehouse, which is where structured data is stored and (on command) processed. Structured data is formatted data. It’s utilized for record keeping and report creation. Data can be easily fetched via structured query language (SQL). The Data Lake does what the warehouse does for unstructured data. Unstructured data is unformatted and used to uncover new insights and patterns. This also includes its business intelligence product, which turns mountains of data into optimal suggestions.

Data Engineering Category:

Key products here include Snowpark. This is its developer platform and data-equipped playground to build new things. It enables working with data in any source code language. With it, they can process and visualize data (through Snowpark functions) and build apps (through Snowpark Native Apps). GenAI models are voracious data consumers. Snowpark Container Services allow GenAI models to run closer to the data that they require. This enhances performance, expedites model training and diminishes costs. Movement of apps, workloads and developer attention from Apache Spark to Snowflake is a key source of growth here.

Dynamic Tables is a newer data streaming tool. It automates pieces of structured data querying within the data warehouse. It offers auto-updates to reports as new data is added to its cloud. 

Unistore is SNOW’s hybrid table product, which can ingest, store and organize both transactional and analytical workloads. Snowflake has long been an analytical workload specialist within the data warehouse part of the business, and this unlocks transactional workload demand.

Snowconvert is another product that helps with easier migrations, as it automates conversion and modernization of source code and data.

AI and Applications Category:

Cortex AI is another important new product. It’s what Snowflake calls its “AI layer” and offers a slew of GenAI-powered tools to (as Snowflake always says) bring AI, application-building and analytics “right to a customer’s data.” That conjoining routinely lowers data transfer and storage costs. Cortex AI offers unstructured text summary, sentiment analysis, helps beginners write SQL etc.

They see the overarching company theme of “bringing data to your work” as a key differentiator here, as agents are only as good as the data they’re trained on and Snowflake offers the destination to get whatever data a company needs.

Collaboration Category:

This is where the Snowflake app marketplace and its data connectors reside. Snowflake Data Sharing is its secure product for, as the name indicates, sharing data among the rest of Snowflake’s participating users. As more opt in, a compelling network effect of relevant data builds and the firm’s value proposition deepens. As Snowflake builds a larger customer base, inviting these customers to openly share information with each other creates a compelling edge vs. sub-scale players.

Model:

Snowflake’s revenue model is consumption-based in nature. Customers book and pay in advance, with billings and revenue recognition coming as credits are utilized. This means visibility compared to SaaS business models is not as strong. It also means customers can more easily scale down (or up) usage when times are bad (or good).

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Snowflake added 15 net new Global 2000 companies in this quarter alone, as it set a record for net new $1M+ customers and enjoyed a few notable large data migrations. Overall, customer count rose by 19% Y/Y, meaning success was broad-based across all customer sizes. Its core data analytics and engineering business was “strong,” and it gained considerable AI cross-selling momentum. That complementary traction was amplified by continued improvements in its go-to-market and yielded the large top-line outperformance.

One note on professional services revenue (non-product revenue). The company enjoyed “one large customer” reaching contract milestones that led to significant deferred revenue being realized. Because professional services is the lower-quality revenue bucket, it’s great to see product revenue outperform by a slightly larger margin. That says its best, most structural growth segment is contributing the most.

Finally, the company is not anticipating large optimization headwinds from its AI-native cohort like it did during the pandemic bubble. They’ve been far more proactive in making sure customers are using their software efficiently, so that risk has significantly eased.

The profit beats were powered by revenue outperformance.

AI Value Proposition:

AI is already a meaningful tailwind for SNOW’s business. 25% of “all deployed use cases” now include at least one AI tool, while it has 6,100 weekly active AI customers. All in all, 50% of new customer wins were influenced by AI. Why are they winning? For the same reasons they’ve been winning to date. They take highly complex models, algorithms and applications and make them easy to use. They give customers all they need to prepare their data for AI readiness and that they can tap into a massive roster of 3rd-party sources opted into sharing. That roster continues to grow into a larger and larger network effect, as 40% of its customers are now sharing data with other Snowflake customers.

They unleash that capability within their platform to allow companies to safely and effectively use this technology across analytics, productivity and application-building use cases. Governance is fully handled by SNOW to allow companies to focus on building and growing rather than tedious maintenance. This is where seamless onboarding and complex, multi-step data analytics meet conversation-based querying and wonderful simplicity. That, in turn, drives compelling ROI while many others struggle to deliver it. Superior ROI always resonates and is now leading to tangible positive momentum and awareness across customers. I think this makes it clear why this software vendor is leaping ahead of others in AI monetization.

AI Product Innovation:

Snowflake Intelligence is a new AI product in public preview. This is what allows companies to fully make sense of and derive insight from unstructured data. That matters a lot. Non-relational database vendors like MongoDB would argue that their “document-oriented” niche is better suited for unstructured data. Strong early momentum for this specific offering clearly shows Snowflake can retrofit its relational database architecture to seamlessly handle this very important data source. Snowflake Intelligence frees data analytics to happen through natural language, greatly lowering the talent barrier for conducting effective data science and analytics. Cambia Health Solutions is already using it for its 2.6M members to more deeply personalize their customer experiences, while Duck Creek Technologies is also using it for internal productivity gains.

💡

“The kind of questions that we can ask of a sales agent that we developed on Snowflake Intelligence has become pretty remarkable… I can get an update about a customer I’m about to meet so my account executive doesn’t need to write a brief for me.” – Snowflake CEO Sridhar Ramaswamy

Cortex AI also added structured query language (SQL) support so customers can use Snowflake’s AI layer directly within its database offerings. This, in turn, lowers data movement costs and drives vendor consolidation. Reuters is using this product to infuse high-powered AI agents into Cortex Search and observability, while BlackRock is using it to augment customer service quality.

More on Product Innovation:

Snowflake added a Snowflake Postgres product during the quarter. Postgres is a relational-based open-source SQL database framework that’s highly malleable and popular for both AI and data analytics use cases. Now, with native support and a slick integration, Snowflake can augment developer tool flexibility within its platform and more deeply connect data analytics, app building and its other core product categories.

Next, it launched OpenFlow to simplify movement of batched or real-time structured and unstructured data. This supports data ingestion, governance, observability and AI tool integrations and comes from Datavolo M&A. The product should bolster Snowflake’s ability to onboard information and let its customers plug into other vital data sources. It also unlocks a $17B data integration market for SNOW.

Generally speaking, SNOW’s innovation engine is in a far better spot than it was a year ago. I think that has everything to do with new CEO Sridhar Ramaswamy replacing Frank Slootman as a more technical, product-oriented leader. Ramaswamy sought to push SNOW to move a lot faster, and they are. They’ve launched 250 new tools year-to-date, and many more are on the way. A good example of impactful innovation is deepening SNOW’s competitive positioning with its Iceberg Table product support. As a reminder, Iceberg is an open-source product that provides data storage; Snowflake offers support for this safely from within its platform. Many feared this would greatly diminish demand for SNOW’s data storage products. Not only has that not happened, but SNOW freely offering access to this increasingly popular offering is juicing demand for everything else it offers. It’s creating new workload flexibility, which is driving more workload migration and more cross-selling. This is the company embracing change and ensuring it benefits from that change.

Crunchy Data:

Snowflake bought PostgreSQL vendor Crunchy to bolster its OLTP capabilities and better compete with players like MDB for that demand. This gives the company native support for Postgres within its platform and for these high-volume workloads. As the company puts it, this isn’t just a Postgres offering, it’s Postgres with a whole host of other supporting tools that help the product stand out. Crunchy is contributing to SNOW’s results ahead of schedule and drove a small portion of the beat.

Go-to-market (GTM):

As a reminder, Snowflake significantly shifted some GTM focus away from new customer generation and towards cross-selling. As it adds compelling features like Cortex AI, Snowpark, Snow Convert etc. and gains a larger customer base, there’s so much opportunity to sell these happy clients more products. Now it’s prioritizing this. They’ve worked hard to scale GTM (364/529 hires this Q were in sales) while keeping tight communication between product builders and sellers, and the work is paying off with rising platform-level adoption. They’ve expanded this change to Europe and are enjoying preliminary signs of the same eventual benefit.

The partner standout this quarter was Microsoft Azure. The two companies keep getting closer across infrastructure and app-layer products, despite Microsoft offering many similar products in the overall cloud suite. Snowflake credits 40% Y/Y growth with this partner to “better alignment between its sales team and Microsoft” and significant time investments to better orient the two companies for mutual success.

Great quarter. This company is in such a better place under Ramaswamy, as his changes strengthen this company’s future and bolster its value proposition. He deserves so much credit here. This transformation has happened while the company has gotten far more surgical in its go-to-market strategy and investments. And it has happened while they’ve also delivered a convincing growth reacceleration. Between their dominance in OLAP and their expansion into OLTP, AI products, and more established products like Snowpark, the runway is massive and this company is once again fully capable of capturing it. Bulls should be pleased.

This was shared in the Discord page during the week.

DraftKings will no longer accept credit cards as a funding method on its sportsbook. This was done for two reasons. First, it makes them less predatory and dangerous in the eyes of regulators, which could help with more state access or maybe even a prediction market green light down the road. Customers are fleeced on interest rates when they use credit cards to fund accounts, as these are treated as "cash advances" (almost like a pay day loan) with sky-high rates. Next, this will actually be positive for their cost structure. Debit/ACH/Venmo/etc. all come with lower fees, chargeback rates and overall better profit than card-funded customers. So DKNG can probably extract a bit more margin from this business as long as there's no material impact to market share. There's no public data on % of deposits DKNG gets from credit cards, but they would not be making this move (before FanDuel) if they expected a material impact on overall share. I think between modest profit tailwinds and modest demand headwinds (if other competitors don't follow suit like I think they will), the net impact from this change will be quite modest. I think it's the right decision to strengthen long-term relationships with regulators, which is so important for the growth engine. iGaming will drive a lot of revenue for this firm and state access is still extremely limited. It makes sense to cozy up to those who can make things less limited before they force you to do so.

More articles (Tech Crunch & Fortune) came out on the new Google Translate tools being an existential competitive threat for Duolingo. This week, it was about new personalization tools in Google's lessons. This is now firmly a battleground stock, as the stock sells off on competitive concerns (like a few other times in the past). We've been seeing product introductions like this for a few years now (from ChatGPT mainly) with no material impact on Duolingo's business.

Nothing has changed since the take I wrote following its strong earnings report (section 1h).

Duolingo’s users are highly engaged, highly loyal and create the largest language learning dataset on the planet. That, in turn, drives constant product iterating and improving to stay ahead of the competition with a superior product. Its magic is in turning that product into entertaining, competitive and productive fun. I do not think that's materially challenged by talking to a chat bot, live translation or Google Translate lessons.

I expect headlines like these to keep driving negative sentiment in the short-term. I also expect the company to keep effectively innovating and profitably growing. And for that reason, articles like we’ve seen over the last few weeks do not deter my bullishness. It would take an actual structural degradation in financials for that to happen… and it hasn’t. The next few quarters will be very telling. If the multiple contracts much more than it already has (approaching 30x forward FCF with 37% 2-year FCF compounding), I may be adding to my stake. We'll see.

There was a PayPal service disruption in Germany late last week. This delayed about $12B in payments. The issue has since been resolved. I don't expect this to have a material impact on their earnings.

The Information released a piece of news late Friday about Meta potentially using Google Gemini or OpenAI models to complement Llama for Meta AI.

Bradesco BBI upgraded Nu to outperform with a $17 price target. This follows recent upgrades from Citi and Itau.

Uber will add Dollar Tree to its delivery service in the USA.

Still waiting on the anti-trust decision involving Alphabet’s payments to Apple for default search engine rights and Chrome. They received another antitrust fine from the EU. It was called “light.”

Walmart will add next-day shipping in major cities for 3rd party goods.

Morgan Stanley thinks Amazon’s AWS data center square footage growth points to strong growth ahead.

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