I'm Paying $200/Month for Claude. Anthropic Quietly Downgraded What I'm Getting.

What Happened

I pay $200/month for Anthropic's highest individual tier — Max 20x. I use Claude Code (their CLI tool) daily with a team of AI agents for building high-performance .NET libraries: GPU compute transpilers, WebRTC networking, and machine learning inference engines.

For months, High was the highest effort setting available in Claude Code. My team was set to High because that was the maximum. Then sometime in late March 2026, Anthropic added a new tier above it: Max.

They didn't email me. They didn't put a banner in the CLI. They didn't notify subscribers that the meaning of their current setting had changed. I only discovered it by cycling through the effort options to double-check my configuration.

What "Adding a Tier Above" Actually Means

When High was the ceiling, it meant "use your full reasoning capability." Now that Max exists above it, High means "use a substantial but capped reasoning budget." The model settles on statistically likely solutions instead of exhaustively tracing logic.

For someone writing a blog post or a React component, this doesn't matter. For someone transpiling C# IL to WebGPU shader code or debugging WebRTC signaling race conditions, it's the difference between correct code and code that looks correct but fails at runtime.

The Real Cost

Over the past week, my team generated and shipped code under what we thought was maximum effort. Here's what we found when we finally switched to Max:

• 24 production bugs found in a single audit of our ML inference library — including buffer aliasing that crashes WebGPU, memory leaks from unreturned GPU buffer rentals, and a reflection-based dispose that was destroying shared hardware accelerators

• A complete rewrite of our WebRTC signaling — the agent ignored reference implementations I explicitly provided (my own working code in other repositories) and pattern-matched a generic solution instead of reasoning through the architecture

• A week of debugging issues that the AI should have caught the first time — race conditions, missing BEP 10 handshakes for incoming peer connections, and a MockLoopback test infrastructure bug that only manifested because the agent's initial implementation was incomplete

The Max setting caught all of these. High missed them because it was "being efficient" — which is another way of saying it was taking shortcuts.

The Numbers

Metric Before Max Discovery After Switching to Max

Test suite 471 passing 498 passing

WebRTC P2P Broken (offer collision) Working (offers-with-announce)

Wire extensions Untested 27 new tests, all passing

BEP 46 ECDSA Gaps 10 tests with real cryptographic verification

Production bugs found 0 (they were shipping) 24 in one audit

Why This Matters for Paying Subscribers

If you're on a premium plan, you're paying for intelligence, not just message count. When Anthropic introduces a higher effort tier without notifying existing subscribers:

• You're paying the same price for less. The setting you chose when it was the maximum now delivers less reasoning depth.

• You're paying twice. Once for the subscription, again in hours spent debugging code the AI should have gotten right.

• You can't opt out of A/B testing. Those "How well is it doing?" polls (1-3, dismiss) that appear during sessions? They're measuring whether you notice the reduced effort. Every "dismiss" you hit is a data point that helps them calibrate the minimum viable intelligence they can serve.

What You Should Do

• Check your effort setting right now. If you're on High and didn't know Max exists, you've been running below capacity.

• Set it to Max explicitly. Don't trust the default. For complex engineering work, Max is the only setting that provides the exhaustive reasoning you're paying for.

• Audit recent AI-generated code. If you shipped anything complex while on High (now the middle tier), re-run the logic on Max. You may find bugs that the "efficient" reasoning missed.

• Check your usage. I was at 46% of my weekly quota with 2 days until reset. I had headroom to burn. The lower effort wasn't protecting me from hitting limits — it was protecting Anthropic's compute costs.

The Bigger Picture

This isn't unique to Anthropic. It's the business model of "Intelligence-as-a-Service": sell the premium tier, then quietly reduce what "premium" means whenever the infrastructure costs get inconvenient. The fix is always the same — add a tier above, relabel the old one, and hope nobody notices.

For my team, this experience validates why we're building SpawnDev.ILGPU — GPU compute that runs on your own hardware (WebGPU, CUDA, OpenCL, CPU) without a corporate middleman deciding how much thinking your tools are allowed to do.

Until sovereign local inference is practical for everyone, at least know what you're getting. Check your settings. Your AI might be quiet-quitting on you.

I'm Todd Tanner (@LostBeard), author of the SpawnDev library ecosystem for .NET. The bugs described in this article were found in SpawnDev.WebTorrent and SpawnDev.ILGPU.ML, both open source.