Custody bank stocks reported strong Q2 results, with 16 tracked companies beating revenue consensus estimates by 3.2% on average. Share prices rose 4.7% since earnings announcements. T. Rowe Price reported revenues of $1.91 billion, up 8.6% year on year, but missed analyst expectations by 1%. The stock fell 7.1% following the results and currently trades at $110.87. Hamilton Lane delivered the strongest performance, reporting revenues of $275.3 million, up 56.5% year on year, exceeding expectations by 21%. The stock rose 9.8% post-earnings and trades at $104.26. The custody bank sector faces fee pressure from large clients and requires substantial technology investments, whilst opportunities exist in global asset growth and blockchain adoption for settlement efficiency.

Databricks closes $5 billion round at a $190 billion valuation. Ad powered by advergic.com Databricks has closed a $5 billion funding round at a $190 billion valuation, the company confirmed on August 13, 2026. The round was led by Coatue Management with participation from Blackstone, MGX and T. Rowe Price, and it arrives alongside Databricks saying it has crossed a $7 billion revenue run-rate. It is the second financing the data and AI platform company has completed this year, and it lifts its valuation well above the $134 billion mark it carried after its previous round. What Databricks announced. In its own newsroom statement, Databricks said revenue grew more than 80 percent year over year in the second quarter and that the business has now passed a $7 billion annualised run-rate. The company also pointed to growth in newer products including Lakebase, its operational database layer, Genie for natural language data queries, and the Unity AI Gateway that governs how enterprise models access company data. The $5 billion raise was larger than originally planned. Chief executive Ali Ghodsi has said publicly that the company set out to raise roughly $1 billion and that investor demand pushed the round several times higher after word of the fundraise circulated during the company conference in June. Full details of the round were published by Databricks and reported by CNBC. Why investors are paying this much. Databricks sits in a part of the AI stack that has turned out to be unusually durable. Companies building AI products still need somewhere to store, clean, govern and query their data before any model is useful to them. That layer, often called the data lakehouse, is where Databricks competes directly with Snowflake and with the data warehouse products of the large cloud providers. The pitch to investors is fairly simple. Model providers keep changing, and enterprises keep switching between them, but the underlying data platform tends to stay put once it is installed. That makes revenue from the data layer stickier than revenue tied to any single model vendor, which is part of why private market investors have been willing to pay a valuation of this size while the company remains private. Ad powered by advergic.com What it signals about the AI funding market. The size of the round is a useful marker for anyone trying to read where the AI money is going in 2026. A lot of the attention in the last two years went to model labs and consumer AI apps. Deals like this one show capital moving further down the stack, into the infrastructure and data plumbing that AI systems depend on. It also underlines how long companies of this size are now staying private. Databricks has raised repeatedly at rising valuations without going to public markets, which is a pattern several large private technology companies have followed. For employees and early investors, that means liquidity increasingly comes through secondary sales attached to rounds like this one rather than through an IPO. Why it matters for professionals and business owners. If you work in data, analytics or AI engineering, the practical takeaway is about skills. Demand at the data platform layer is not slowing down. Familiarity with lakehouse architecture, SQL at scale, data governance and the tooling around retrieval and model access continues to be some of the most transferable technical experience available right now, and it is not tied to whichever model happens to be leading the benchmarks this month. For business owners and freelancers serving clients, the signal is that enterprise AI budgets are still being approved, and a growing share of them is going to data readiness rather than to model subscriptions. Companies are discovering that their AI plans stall because their data is messy, not because their model is weak. Anyone who can help fix that gap, whether as a consultant, a contractor or an internal hire, is selling into a market that investors clearly believe is still expanding. The round is a private financing, not a public listing, so the valuation reflects what a small group of institutional investors were willing to pay rather than an open market price. That distinction is worth keeping in mind when comparing it to the market capitalisation of listed competitors. Sharing is caring!