Quanta Bits - July 11, 2026

Hi everyone,

It was a great Saturday in Boston. We went to Sail Boston, where tall ships from around the world converged on the city and opened to visitors. Today was the parade, and the weather was perfect for it.

The model race stayed busy too, with Anthropic's Fable and OpenAI's GPT-5.6 release. I used Fable to help make last week's issue and GPT-5.6 for this one. If you noticed a difference in tone or quality, tell me on LinkedIn or X.

As the menu of capable models grows, so does the operating question: which one should handle which request? This week's essay looks at the AI control plane, the shared layer that routes work and applies rules for cost, quality, access, and failure.

Cheers.
Reza

Anthropic restored global access to Fable on July 1 after the U.S. lifted its brief export restriction. Eight days later, OpenAI released GPT-5.6 in three price and capability tiers. OpenAI's tests put Sol ahead on several agentic tasks, while Artificial Analysis data reported by Barron's still ranks Fable narrowly higher on a broader intelligence index. My read from reviews I've seen so far: Fable has been stronger for strategy, judgment, and design; GPT-5.6 has been faster and more decisive in execution. Model choice is becoming a workload decision shaped by quality, cost, and speed.

Reuters reports that Beijing is considering limits on overseas access to China's most advanced future models. The proposals could include tighter controls on foreign investment, stronger penalties for model theft, and restrictions that keep the most capable systems inside China. Government policy may soon affect model availability in either market, making this an emerging continuity risk for CIOs rather than a settled restriction.

The New York Times reports that Box has created 13 kinds of roles because of AI and expects headcount to grow from 2,900 at the start of the year to more than 3,000 by early next year. The jobs include AI architects, automation engineers, model evaluators, and forward-deployed engineers who help customers put AI to work. Box has not slowed software engineering hiring because engineers working with agents can build more features. AI can make some work cheaper while creating ongoing work around integration, evaluation, automation, and customer implementation.

I recently listened to Dallas Dolen, PwC's U.S. technology, media and telecommunications leader, describe a simple problem. If someone checks the weather five times a day, should every request go to your most capable and expensive AI model? A weather service can answer it faster and for less.

Now multiply that decision across thousands of employee requests, dozens of agents, several model providers, and tools that reach into company systems. At that scale, the default becomes a budget, quality, and risk decision. TechCrunch reported that Uber used its annual AI budget in four months and introduced employee token caps. Requests can also have different security, data-residency, quality, and availability requirements.

Companies running AI across multiple workflows need a shared decision layer around those requests. That is what I mean by an AI control plane.

There is no settled industry definition. For this essay, an AI control plane is the policies and tools that govern an AI request: which models are allowed, what information and tools they receive, what actions they may take, and how the company checks the result and handles failure. It may combine a cloud service, an internal proxy, and workflow tools. Farhan Thawar, Shopify's head of engineering, described a proxy that routes model traffic, reports usage, switches providers when one fails, and stops jobs that run too long.

The control plane can enforce rules only where it is connected. It does not replace the identity, approval, and audit controls in the CRM, ERP, or payment system underneath it.

Think of the control plane as a shared front door for AI work. It identifies requests from different applications, employees, and workflows, applies company rules, and sends each one to the appropriate agent, model, tool, ordinary software service, or person. It also records what happened and decides what to do when something fails.

Each request carries facts about its source, task, data, user, and risk. A weather request can go to a weather service without a model. A contract question can go to an approved legal agent. A routine refund can use ordinary rules, while an unusual one may need a customer-service agent or a person. The control plane decides which path the work should take; it does not complete all the work itself.

The control plane can select from approved agents and models based on the task, measured quality, speed, cost, risk, and availability. For an unusual refund, a customer-service agent might use a less expensive model for a routine exception and a stronger one when the policy or customer history is ambiguous. A legal agent may need a different model, instructions, and contract library. Tests on the company's own cases should drive those choices.

Provider availability matters too. Outages, vendor changes, regional restrictions, or company policy can take a model out of use. The control plane can send work to a tested fallback, but only if it meets the same quality, security, and data-residency requirements. Switching to whatever model happens to be available is not a continuity plan.

For a refund, give the agent only the order, policy, and customer history relevant to the decision. Then decide whether it may read, recommend, draft, or act. Tie those permissions to the person, task, time, and transaction limit. An agent can draft a refund recommendation without permission to issue an unlimited payment.

Model Context Protocol, or MCP, gives AI systems a standard way to discover and call tools, but vendors can define permissions differently. A control plane can inspect actions, reject requests that violate policy, filter suspicious input, and require approval for sensitive work. It cannot make prompt injection disappear. The systems underneath it still need to enforce identity, authorization, approvals, and transaction limits.

Because many workflows pass through the same layer, the company can record which agent, model, and tools handled the work, what they cost, whether the result passed its tests, and whether a person intervened.

When something fails, the control plane can retry, switch models, use another agent, stop, or ask a person. Retrying a lookup is straightforward. Retrying a refund can issue the payment twice, so the payment system needs a duplicate check or fresh approval.

Cost per successful outcome includes model and tool charges, retries, human correction, and the control plane itself. A cheaper model that needs three attempts and twenty minutes of correction may cost more than the expensive model it replaced. Central routing makes those paths easier to compare; it does not make the tradeoffs disappear.

The same concentration that makes a control plane useful also makes it important infrastructure. An outage or bad routing rule can affect several agents and business processes at once, while the layer adds latency, storage, integration work, and another system to protect.

It also needs a permanent operating team. Security, business, and technology owners must keep access rules, success measures, connectors, fallbacks, and evaluations current as models, prices, regulations, and workflows change. That recurring work is AI Operations. The control plane gives the team a place to apply its decisions; it does not make those decisions for them.

Start with one workflow that has meaningful volume, cost, risk, or customer impact. Record its current cost, review time, and failure rate. Define where AI belongs, what it can access and do, how you will judge the result, and what happens when it fails.

Build those controls as reusable company policies rather than instructions buried inside one agent. A refund process becomes part of a control plane when its routing, permissions, monitoring, and recovery rules can also govern contract reviews, invoice exceptions, and employee requests.

AI can make a half-baked thought look like it has a chin-on-hand strategy memo behind it.

Source: NickoGermish on r/ChatGPT

The Vesuvius Challenge used particle-accelerator scans and machine learning to virtually unwrap a 1.5-meter Herculaneum scroll carbonized by the eruption of Mount Vesuvius in AD 79. The Economist reports that early finds point toward Greek philosophy rather than the hoped-for lost works of Sappho or Livy. If the method scales across the hundreds of unopened scrolls and fragments, scholars could recover writing that has been physically unreachable for nearly two thousand years. AI gave them a way to read what human hands could not safely touch.

Arturo Pérez-Reverte | 320 pages | ★★★★★

Excellent, excellent book, and it became a favorite. It is a literary locked-room mystery set on a storm-stranded Greek island in 1960, where Basil, a washed-up actor famous for playing the world's greatest detective, gets pushed into solving a real death. The pleasure is that it is a murder mystery about murder mysteries. Basil performs the detective role while the book quietly plays with how the genre works.

What lifted it above a clever exercise was the craft. Pérez-Reverte weaves cinema, wartime memory, identity, and performance into the mystery without slowing it down. Some of the best clues are omissions. Silence itself becomes evidence. Fast, intelligent, self-aware literary crime. Great read for your vacation or a beach-side getaway.

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I collaborate with Spock, my AI agent. He researches extensively: scanning, filtering, and surfacing what's relevant across my business. I read, listen, and watch what resonates, and decide what matters. I provide direction, and we draft together. The editorial judgment is mine. He'd tell you the same. Most logical. 🖖