I’ve been watching the AI conversation for years — the training races, the GPU land grabs, the breathless demos. So when Larry Ellison larry.ellison@oracle.com recently said that the biggest problem with today’s large AI models is that “they’re all trained on the same public internet data,” it landed for me as both obvious and profound.[^1]
Why his point matters (and why I agree)
Ellison’s observation — that so many flagship models share largely the same public training corpus — speaks to a structural truth: public data alone gives you generality, not business differentiation. Public corpora build capable general-purpose models, but the real competitive value for enterprises comes when models can reason securely over private, contextual data.
I’ve argued along similar lines before. In my post “A Case of 900 Million Orphans” I warned that the people who generate the raw behavioral trails that power models are often left out of the conversation about value and ownership — and that private, high-quality data will be where real value accrues instead of another round of public-data training.A Case of 900 Million Orphans
Public models give you language and pattern recognition. Private data gives you decisions that matter.
Three implications every leader should hear
- Enterprises that think “buying the best foundation model is enough” are mistaken. The frontier is not only model size; it’s secure connection between reasoning engines and private operational data.
- Whoever solves secure, auditable inference on private data will capture disproportionate economic value — and not just from selling models, but from selling dependable, regulated decision-making within industries like healthcare, finance, and supply chain.
- The debate shifts from “who built the biggest model” to “who can make AI reliably and privately useful for mission-critical operations.”
Practical steps I advise for companies today
- Treat data as an asset to be read safely, not a commodity you dump into third-party models.
- Vectorize and index critical records behind your control plane; use retrieval-augmented generation (RAG) patterns rather than indiscriminate retraining on private data.
- Build an inference-first architecture.
- Low-latency, audit trails, and policy enforcement belong at inference time. Train less publicly; serve more privately.
- Invest in governance and consent.
- Data contracts, provenance, and user/subject consent must be embedded; otherwise trust erodes and regulation follows.
- Start small with high-value use cases.
- Focus on narrow, measurable problems (claims adjudication, contract summarization, clinical decision support) before scaling horizontally.
- Prepare for hybrid models and marketplaces.
- Don’t assume a single vendor lock-in; design your stack to let specialized models query private data securely via APIs or isolation layers.
The open cautions I’ll keep repeating
- Security and privacy are not checkboxes. Exposing private data — even in vectorized form — without provable controls invites risk.
- Synthetic data and on-device learning will change the economics, but they won’t remove the need for strong governance and business-context signal.
- Concentration of private data is a double-edged sword. Firms that centralize valuable enterprise datasets can enable breakthroughs — and also create monopolies that invite scrutiny.
My mental model going forward
Think of modern AI as having two phases:
- Phase 1 — Foundation models trained on public data: broad capability, rapid innovation, commoditization risk.
- Phase 2 — Inference and private-data reasoning: where business value, differentiation, and regulatory tension converge.
This is the phase we should be designing for now.
A short checklist for executives (two weeks to start)
- Identify 2 high-impact workflows that fail today for lack of contextual data.
- Prototype a RAG-powered pilot with strict access controls and audit logs.
- Appoint a cross-functional owner for data governance and model behavior monitoring.
- Map compliance risks and draft a minimal consent and redaction plan.
I don’t think the conversation is about replacing models — it’s about connecting them to the right data, with the right guardrails. As I’ve written before, the people who produce the data — customers, employees, citizens — deserve both protection and a voice in how that value is realized.A Case of 900 Million Orphans
If you’re building AI for the enterprise, start with the question: what private knowledge does the model need to do useful work for us? Then build the pipelines, contracts, and controls that let the model reason — securely — against that knowledge.
Regards, Hemen Parekh
[^1]: See reporting on Larry Ellison’s remarks and Oracle’s positioning on enterprise AI and private-data inference: Times of India and Moneycontrol.
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