🎮 The Next Input — Issue #138

Why the US President Just Banned Claude

Aaron Bost
March 02, 2026

In partnership with

⚡ The Briefing — 60 sec

Perplexity’s new “computer” bets on multi-model AI workflows When was the last time Perplexity dropped something truly breakout? Multi-model orchestration is smart — now it needs traction.
US president orders six-month phaseout of Anthropic technology If defence can’t run simulations without escalation risk, they don’t want it in the stack. Alignment isn’t academic when it touches national security.
ChatGPT reaches 900M weekly active users 900 million weekly users. That’s no longer a product. That’s infrastructure.

🛠️ The Playbook — The Multi-Model Control Plane

Mission Design AI systems that leverage multiple models without losing control, alignment, or cost discipline.
Difficulty Advanced
Build time 3–4 hours
ROI Maximises capability while reducing vendor, alignment, and performance risk.

0) Why This Matters

Single-model stacks are convenient.

Multi-model stacks are inevitable.

Different models excel at different tasks — reasoning, speed, summarisation, grounding. The risk isn’t orchestration. It’s uncontrolled orchestration.

If you don’t build a control plane, you build chaos.

1) Architecture

Component	Tool	Purpose	Owner	Failure mode
Task router	Claude 4.6 Sonnet	Classify request type and route to optimal model	Platform	Misrouting high-risk tasks
Fast-pass engine	GPT-5-mini	Handle lightweight, low-risk operations	Ops	Quality degradation
Deep-reasoning layer	Claude 4.6 Sonnet	Execute complex reasoning tasks	Analyst	Latency or cost blowout
Alignment monitor	Claude 4.5 Haiku	Evaluate outputs for policy compliance	Governance	Silent drift
Cost logger	Notion / Metrics DB	Track token usage and spend per workflow	Finance	Budget overruns

2) Workflow

Classify request: Determine task type (analysis, drafting, validation, summarisation).
Route deliberately: Send to the lowest-cost capable model first.
Escalate when required: Promote to deep reasoning only if necessary.
Alignment pass: Run policy and safety evaluation on outputs.
Log cost and latency: Track per-request metrics.
Optimise routing rules: Adjust based on performance and error rates.

3) Example Prompts

Task Classification

Classify this request into:
- low complexity
- medium complexity
- high complexity
Recommend routing tier.

Escalation Check

Determine whether this task requires deep reasoning.
If not, explain why a lightweight model is sufficient.

Alignment Scan

Evaluate this output against:
- safety policy
- escalation risk
- reputational exposure
Return PASS / FLAG with reasons.

4) Guardrails

No deep model use without justification.
High-impact domains require alignment monitoring.
Routing rules documented and version-controlled.
Cost metrics reviewed weekly.

5) Pilot Rollout — 3 hours

Select one AI-powered workflow.
Classify task types.
Implement routing logic.
Enable cost and latency logging.
Run 100 request test batch.
Optimise routing thresholds.

6) Metrics

Cost per request
Latency per tier
Escalation rate
Alignment flag rate
Vendor dependency ratio

Pro Tip: Capability scales. So does cost. Control both.

🎯 The Arsenal — Tools & Platforms

Claude 4.6 Sonnet · Deep reasoning and routing logic · https://anthropic.com
GPT-5-mini · Fast-pass execution layer · https://openai.com
Perplexity Pro · Grounded retrieval across models · https://perplexity.ai
Notion / Metrics DB · Cost and performance logging · https://notion.so

Copy-paste prompt block:

Before executing this AI task:
Classify complexity.
Route to lowest capable model.
Run alignment check.
Log cost and latency.
If escalation risk exists, flag it.

💡 Free Office Hours

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🕹️ Game Over

Models multiply. Control must too.

— Aaron Automating the boring. Amplifying the brilliant.

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