There was a big move in Oracle shares this week — perhaps the market realizing what we’ve been saying all along: that not all software companies will be left behind by AI. Indeed, some — especially Oracle — will thrive. That belief was further reinforced by all I learned at the invitation-only Oracle Database Summit in Silicon Valley a few weeks ago. More on that below.

You can find prior notes and the full research archive at https://epistrophy.beehiiv.com. Check it out now — the site is going through a major revamp. You can say you knew it when!

As always, I’m focused on three things:
1) Technology-driven change;
2) the latest in innovation and startup trends, and;
3) stock fraud.

Companies Discussed

In This Note:

1. Companies Discussed

2. A ⚡️ Moment For Oracle’s Database 

   1. Is Oracle the most AI company of them all?

      1. Databases Built for AI

3. Tweet O’ The Week

4. Epistrophy In The News

5. 📆 of Epistrophy Events

6. Availability The Next Two Weeks — none!

A ⚡️ Moment For Oracle’s Database 

Is Oracle the most AI company of them all?

Bloom Energy’s (BE: NYSE)  latest press release managed a neat trick: in a single one sentence it suggested that Oracle’s (ORCL:NYSE) cloud operation is proceeding with competence, speed and price discipline.

The release quoted Oracle’s Mahesh Thiagarajan, executive vice president of Oracle Cloud Infrastructure. “By rapidly deploying Bloom’s reliable, efficient fuel cell energy, we are quickly meeting the demands of our customers across the United States,” he said. 

But it was the numbers that ignited Oracle’s stock, which would rise 27% after that release.  Bloom’s release said it had delivered a fully operational fuel cell system to Oracle in 55 days, more than a month ahead of the expected 90-day deployment schedule. That performance landed Bloom a new, bigger contract. 

At once it showed that Oracle could exceed Wall Street’s dim view of its potential to acquire power and on the cheap.

Bloom Energy’s onsite fuel cells and Oracle’s sprawling cloud network illustrate the reality of AI computing: power, cooling, networking and database architecture now determine how quickly hyperscalers can deploy capacity. The center of gravity therefore shifts back to the database systems that organize and serve the data.

Oracle’s answer has been to fuse infrastructure with software. The company’s cloud strategy revolves around the Oracle Database, a product that has anchored enterprise computing for four decades. What once served transaction processing and corporate recordkeeping is being redesigned as a system capable of feeding AI models with real-time business data.

That shift becomes clearer in the newest version of the company’s flagship database platform, Oracle AI Database 26ai. The software introduces a set of capabilities aimed at making enterprise databases resilient, AI-aware and integrated directly with agent-driven applications.

Recent upgrades to that database are among the most consequential in years. Among them: 

The first of these upgrades addresses a simple but expensive problem: downtime. At the recent Oracle AI Database Analyst Summit,  the company explained that the Platinum tier of its Maximum Availability Architecture reduces disaster failover times to under 30 seconds for complex deployments, roughly four times faster than earlier database versions. The improvement relies on enhancements to Oracle Active Data Guard and Real Application Clusters, technologies that replicate data across systems while keeping applications running.

For the most demanding applications the company introduced a second layer, Diamond-tier availability. This architecture uses active-active database replication across geographically distributed regions. Logical replication through Oracle GoldenGate or the company’s distributed AI database allows systems to recover in less than three seconds after a failure. In industries such as financial services, electricity markets and telecommunications, outages measured in minutes can cascade into operational chaos. The ability to restore service in seconds becomes a selling point.

The second major development focuses on vector search. Modern AI applications store embeddings—numerical representations of words, images or documents—inside specialized vector databases. Many enterprises built separate infrastructure to handle those workloads. Oracle’s Autonomous AI Vector Database attempts to fold vector search directly into the main database engine.

Enterprises running relational databases, document stores and vector engines simultaneously must maintain multiple data pipelines and synchronization systems. Oracle’s approach allows developers to run vector queries, relational analytics and transactional workloads within the same system. The architecture reduces data movement and simplifies security controls. For large organizations managing petabytes of corporate data, eliminating separate infrastructure layers can translate into meaningful cost savings.

A third capability addresses how AI systems interact with corporate data. Oracle calls the feature a “Unified Memory Core.” The concept resembles a converged data engine capable of storing relational records, graph relationships, vector embeddings and spatial data simultaneously. AI agents querying the database can retrieve information across these different formats without stitching together multiple systems.

The design targets a specific weakness in enterprise AI deployments. Many organizations built machine-learning pipelines that periodically copy operational data into analytic systems. Those pipelines introduce latency. AI systems therefore reason over stale information. Oracle’s unified architecture aims to let AI models operate directly on live enterprise datasets.

Security changes represent a fourth technical pillar. Oracle added a system called Deep Data Security that enforces fine-grained access policies inside the database. Each user or AI agent can view only the data permitted under its role. The approach attempts to prevent AI-driven data leaks by enforcing authorization rules at the storage layer rather than within application code.

The database also incorporates post-quantum cryptography mechanisms designed to defend against “harvest now, decrypt later” attacks. Security researchers warn that adversaries can collect encrypted traffic today and decrypt it once sufficiently powerful quantum computers emerge. Oracle’s platform now supports hybrid cryptographic protocols that combine conventional encryption with quantum-resistant algorithms.

These architectural changes become easier to understand through real-world deployments.

One example came from a Hollywood studio trying to manage a giant digital asset booking platform. The company operates roughly 30 applications and manages about 260 terabytes of data. Its migration strategy involved moving from Oracle 19c running on-premises to Oracle 26ai in the cloud while maintaining zero downtime using Active Data Guard.

The project highlights a common constraint in enterprise IT. The studio’s chief information officer imposed a cost-neutral requirement – yes, he wants more performance and won’t spend another penny to get it. A previous effort using PostgreSQL tools from EnterpriseDB failed after a year, partly because promised automated conversion capabilities proved to be bunk. Engineers reported several multi-hour outages during the trial. Oracle’s migration path ultimately offered fewer disruptions.

Databases Built for AI

Taken together these deployments illustrate a subtle transformation inside Oracle’s business model. The company historically sold software licenses attached to corporate databases. The new strategy positions the database as the center of AI infrastructure.

The economic stakes are significant. Oracle’s cloud segment already produces tens of billions of dollars in annual revenue and continues to grow faster than the broader company. AI workloads require enormous clusters of GPUs connected through high-speed networks. Those systems generate unprecedented volumes of data that must be stored, indexed and queried.

Databases therefore become the connective tissue between AI models and enterprise operations. If Oracle succeeds in embedding vector search, agent frameworks and security policies directly inside its core database engine, customers may prefer to consolidate infrastructure rather than assemble complex stacks of specialized tools.

Oracle therefore appears to be attacking the AI infrastructure problem from both ends. On one side it expands data center capacity through cloud investment and energy partnerships. On the other it upgrades the software layer that organizes and secures the resulting data.

Predicting the outcome requires caution. Database markets evolve slowly. Enterprise customers rarely abandon established platforms quickly. Yet the convergence of AI and operational data may strengthen Oracle’s competitive position. Few companies control both a widely deployed enterprise database and a rapidly expanding cloud infrastructure network.

Artificial intelligence ultimately depends on two scarce resources: compute power and reliable data. Oracle’s strategy is simple: dominate both.

Tweet O’ The Week

Epistrophy In The News

What an interesting conversation with Connell McShane on NewsNation, where we dug into the issues around Anthropic’s Claude Mythos, the super LLM that the company apparently doesn’t trust the government to have access to. These are some strange days in tech.

📆 of Epistrophy Events

Availability The Next Two Weeks — none!

It’s vacation time — I’m taking a disturbingly rare break as I gird up for a busy earnings season and some monster IPOs. I’m on the hunt for some Caravagios (I’ve only ever seen three). That means no weekly note for the next two weeks and I’ll be far from techland until May!

Written reports are available to clients, with video summaries on YouTube, and of course our popular summaries of the summaries on Instagram, TikTok, and YouTube Shorts.