CFOs already understand concentration risk. We just usually apply it to banks, customers, and supply chains — not to the intelligence layer now writing board packs, cash forecasts, and diligence notes.
If half your finance workflow depends on a model you do not host, do not control, and cannot audit end to end, you have built a single point of failure into the operating system of the business. That is not an IT preference. It is a governance decision. And boards should treat it as one.
The Dependency Problem
Cloud AI is extraordinary. It is also leased. You rent capability by the token, subject to vendor policy, pricing power, outages, and political weather.
In June 2026 the US Commerce Department put export controls on Anthropic’s frontier models, and access was switched off globally overnight while the company worked out how to comply. The controls were later lifted after new safeguards. The point is not the politics. The point is the switch. One policy decision, and a production capability disappeared.
A fortnight later, OpenAI previewed GPT-5.6 to a limited set of “trusted partners” first, explicitly at government request under a voluntary pre-release review framework. OpenAI said it did not want that model of access to become the long-term default. Fair enough. But enterprises now have a live example of frontier intelligence arriving through a gate, not a pipe.
Then the market did what markets do: it routed around the constraint. Orchestration layers and open-weight alternatives appeared fast — Sakana’s Fugu among them — because capability that can be withheld will always attract substitutes.
None of this requires a conspiracy theory. It requires a CFO’s instinct: if a critical input can be gated, censored, repriced, or reversed by someone outside your control, you should not build the entire house on it.
Cost, Latency, Confidentiality
Run the ledger properly.
Cost first. Public API pricing looks cheap until usage compounds. Board packs, monthly closes, covenant models, contract review, management accounts commentary, buyer Q&A — token volume scales with ambition. Cloud spend is opex with a vendor’s hand on the dial. Local inference has capex and energy cost, but the marginal cost of the next confidential memo is near zero once the box is paid for. That changes the unit economics of “use AI everywhere.”
Latency second. Interactive finance work hates round trips. Cash models, scenario trees, and live diligence chats feel different when the model sits on your network rather than three jurisdictions away. Speed is not vanity. It is whether people actually use the tool under pressure.
Confidentiality third — and this is the one boards understand immediately. Sending draft SPAs, customer concentration analyses, working capital bridges, or management presentations to someone else’s model is a data-processing decision. Even with enterprise contracts, retention policies, and “we don’t train on your data” promises, you have expanded the attack surface and the counterparty list. For sensitive M&A work and PE portfolio reporting, that is not a technical footnote. It is risk acceptance.
Encryption, access control, and data residency are not lifestyle choices for finance teams. They are controls. Local or private deployment puts those controls back under your policy, not a vendor’s product roadmap.
What “Local” Actually Means
Local does not mean a dusty server under the FD’s desk and a vow of technological poverty.
It means a spectrum:
• On-prem or colo hardware running open-weight models for high-sensitivity workloads.
• Private cloud tenancy where you control the network boundary and keys.
• Hybrid: public frontier models for low-sensitivity drafting; local models for cash, people, contracts, and deal data.
• Open weights where the model parameters are yours to run, pin, and version — not a black-box endpoint that can change behaviour between Mondays.
The mature pattern is the same one we used for banking systems and ERP: classify the data, then choose the environment. Public web search and generic writing can stay in the cloud. Anything that would hurt if it leaked — or freeze the close if it vanished — should have a home you control.
You do not need the absolute best model for every task. You need a good enough model that is available, private, and accountable when the board pack is due at 7am.
The PE Angle
Private equity should care more than most.
Portfolio companies are already wiring AI into forecast packs, pricing tools, collections, and customer support. That creates three diligence questions buyers will eventually ask:
Where does the intelligence run? What happens if the vendor changes terms, price, or access? How much of the “AI-enabled” value creation is transferable at exit?
Vendor lock-in used to mean ERP and CRM. It now includes model dependency. If a company’s operating edge is a prompt library glued to one closed API, the exit story is thinner than it looks. If the same company can run core finance workflows on owned infrastructure with portable open weights, the capability survives a change of control.
There is also a portfolio resilience angle. One policy shock or regional outage should not simultaneously degrade reporting quality across twelve companies because they all rented the same brain. Concentration risk is concentration risk, whether the asset is a bank facility or an inference endpoint.
For GPs and operating partners, local AI is not a gadget budget line. It is part of exit readiness, cyber diligence, and operational independence.
Practical Starting Steps for a Finance Leader
Skip the manifesto. Do this:
1. Map the workflows, not the hype.
List where AI already touches finance: board packs, commentary, covenant testing, invoice capture, contract review, data-room Q&A, FP&A scenarios. Rank each by confidentiality and operational criticality.
2. Draw a hard line.
Anything involving deal data, payroll, customer-level margin, unpublished results, or lender packs defaults to private or local processing unless there is a documented exception.
3. Pilot one high-value, high-sensitivity use case.
Local review of contracts. Private drafting of management accounts narrative from internal numbers. On-network Q&A over a diligence folder. Prove cycle-time and control benefits on something the board cares about.
4. Measure like a CFO.
Track cost per pack, hours saved, rework rate, incidents, and whether the process still works when the public API is slow or unavailable. If it only works on a perfect internet day, it is not production.
5. Separate “assistant” from “system of record.”
Models draft. Ledgers, workpapers, and approvals remain controlled systems with audit trails. Do not confuse fluency with authority.
6. Demand architecture options from vendors and internal IT.
“We use ChatGPT” is not a strategy. Ask for data flow diagrams, retention, key custody, fallback models, and an exit plan. If nobody can answer, you already know the risk posture.
Control the Controllables
You cannot control Washington’s export calendar, a lab’s safety incident, a vendor’s price sheet, or the next voluntary “trusted partner” gate. You can control where your sensitive numbers go, which systems are load-bearing, and how badly a third-party outage damages the close.
Cloud AI will remain useful. Frontier models will remain impressive. Use them where the data is dull and the upside is speed. For the work that defines enterprise value — forecasts, cash, contracts, diligence, board decisions — ownership of the intelligence layer is becoming as important as ownership of the general ledger.
The finance function’s job has always been to keep the business solvent, informed, and free to act. Depending entirely on rented cognition works against all three.
Local AI is not nostalgia for servers. It is the boring, adult version of resilience: keep the critical path under your own keys.
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