Uber, T-Mobile, and Brex Are All Capping Employee AI Spending After Billing Shock. That Is Your Wedge for an AI Cost Optimization Business.

Most AI consulting pitches lead with capability.

The smarter pitch leads with a bill.

Ed Zitron's analysis of the AI infrastructure market surfaced a data point that deserves more attention than it got: Uber burned its quarterly AI token budget in weeks and capped employee spending at $1,500 per month. T-Mobile capped at $2,000 per week. Brex at $500 per week.

That is not an AI adoption story. That is a finance story.

And it is the same story playing out inside most growing companies right now — they added AI tools faster than anyone tracked them, costs scaled before anyone measured them, and now the CFO is staring at a line item that appeared out of nowhere.

Only 26% of companies have comprehensive visibility into their AI costs. 22% have none at all until the invoice arrives.

That gap is your product.

What Companies Are Actually Facing

The pattern repeats at nearly every company that added AI tools between 2023 and 2025:

Individual teams subscribed to Cursor, Claude Pro, ChatGPT Plus, Perplexity, and a handful of niche vertical tools without central coordination. Engineering used OpenAI or Anthropic APIs directly for internal tools and no one tracked token burn. Finance first saw the cost at month-end billing, often higher than any individual team expected. There is no single owner of the AI budget line, no standard way to report usage, and no way to measure ROI against scattered spend.

The problem is not that AI is expensive. The problem is that AI costs are invisible until they are not.

The Service

An AI Cost Audit is a structured review of a company's full AI spend across three layers.

Layer 1: Tool subscriptions. Every per-seat SaaS product with AI features. Identify duplication — companies routinely have three tools doing the same summarization task across different teams without anyone noticing.

Layer 2: API usage. Every direct API connection across OpenAI, Anthropic, Google Gemini, and any fine-tuned model endpoints. Map the prompts being run, their token count, and their actual business output.

Layer 3: Infrastructure. Compute costs for any self-hosted or fine-tuned models, vector databases, embedding pipelines, and retrieval-augmented generation systems.

The deliverable is a one-page spend map and a written report with three to five specific optimizations — prompt caching opportunities, model downgrade paths for low-stakes tasks, redundant subscriptions to cancel, and usage alerting to add before the next billing cycle.

Why This Is an "Easy" Entry Point

You do not need custom tooling to do this. The information already exists. It is just scattered.

OpenAI and Anthropic both have usage dashboards. Helicone and LangFuse provide observability on top of those APIs. Your client's accounting team already has the invoices — you need to aggregate and map them, not build new infrastructure.

Your value is not technical. It is attention. Most companies have never had anyone sit down and look across all their AI costs in one place. That is a one-time task that pays well because the cost of not doing it is visible on the next invoice.

The Conversion Pitch

The highest-converting version of this offer leads with a specific, verifiable problem:

Most companies with 20 or more employees actively using AI tools have at least one redundant subscription they are paying for twice, at least one API workflow running without caching that could reduce costs significantly, and at least one tool where a lower-tier model would handle the majority of use cases without a noticeable quality drop.

You are not claiming to fix every problem. You are claiming to find the ones that are already there. That claim holds up because it is almost always true.

The Retainer Path

After the audit, the natural follow-on is a monthly AI Spend Governance Retainer.

What it includes: a shared cost dashboard that updates weekly from API usage endpoints, a monthly 30-minute review with whoever owns the AI budget, model routing recommendations as new models launch and pricing shifts, and alerting before spend overruns instead of after.

Price at $1,000 to $2,500 per month for SMBs. The value proposition is simple: you are the person who makes sure they do not get surprised by another billing shock. In a market where companies are already implementing caps because they could not see costs coming, that is a straightforward sell.

The Week-One Path

Day 1: Write a one-page AI Cost Audit offer sheet. What you review, what the deliverable is, the price, and the two-week timeline from kickoff to report.

Day 2 to 3: Build a simple audit template in Airtable or a spreadsheet. Columns for tool name, monthly cost, team using it, use case, whether it duplicates another tool, and optimization notes.

Day 4: Identify three companies in your network with 15 or more employees actively using AI tools. Offer a free 45-minute AI Spend Health Check — a review of their tool stack and one specific recommendation. This becomes your discovery call and your proof of concept simultaneously.

Day 5 to 7: Write one short content piece based on those conversations: "What We Found in Every AI Stack We've Reviewed." Publish it on your domain. Submit to HN. Use it as your outbound asset.

The business is not glamorous. Companies capping their AI spending at $500 per week are not the ones making TechCrunch. But they are everywhere, they have an obvious pain, and they are actively looking for someone who can explain what they are paying for.

That is a very good starting position.

Source: https://www.wheresyoured.at/ai-is-slowing-down/