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Modal CEO Erik Bernhardsson’s “new era of software” describes a shift from AI that only generates answers to AI agents that execute code, use tools, maintain state and pursue objectives. In an interview reported by Bloomberg Technology and MT Newswires on October 14, 2026, Bernhardsson argued that this change requires infrastructure combining model inference, training, reinforcement learning and isolated sandboxes—not simply more GPUs.

Modal says it has run more than 3 billion AI sandboxes for customers. That is a company-reported total, not an independently audited metric. The interview account also put the sandbox business at about $300 million in reported run-rate revenue, while noting that Modal had raised $750 million at a $15.75 billion post-money valuation.

What Bernhardsson means by a “new era of software”

Traditional software is mainly authored by people who specify the logic, files, permissions and operating environment. Generative AI initially changed the input: a user asked an LLM a question and received text, code or an image. Bernhardsson’s thesis is that the next change is operational. An agent can write and run code, call APIs, inspect files, retry failed steps and coordinate with other agents.

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Once software is produced and executed dynamically, the runtime becomes part of the product. The infrastructure must decide what the agent can access, how long it can run, what it can spend, which network destinations it may reach and how its actions are recorded. A secure sandbox or virtual machine supplies that controlled execution boundary.

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Microsoft CVP Tim Bozarth describes a similar progression: “the first wave is asking an LLM to answer questions. The second wave is deploying agents to perform tasks and automate workflows. We’ve now entered a third wave, where success comes from managing a network of agents aligned to complete an objective.” In that third wave, code is an output of the system rather than the primary input maintained by a human developer.

Why AI agents need sandboxes

Containment of generated code

Generated code can be incorrect, destructive or intentionally risky. Running it inside an isolated environment limits access to the host system and makes it possible to terminate a job without taking down a production service. Isolation is especially important when an agent installs packages, compiles untrusted repositories or processes user-supplied files.

Tools, files and networking under policy

An agent is useful only when it can interact with tools. A practical sandbox therefore needs controlled filesystems, credentials, outbound-network rules, process limits and timeouts. The policy layer should make each permission explicit and log the request, result and side effects.

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Repeatable state and auditability

Long-running tasks need more than a single prompt-response cycle. Agents may need to preserve files between steps, resume after a failure or reproduce an evaluation. Sandboxes provide a defined place to store that state while giving operators records of commands, resource use and network activity.

Scaling short-lived workloads

Agent tasks often arrive as bursts of many small jobs rather than as one continuously running application. A platform must start environments quickly, schedule them on available capacity and destroy them when a task ends. The number of environments created is therefore a useful demand signal, but it does not by itself prove that each environment is profitable.

What Modal is combining in one platform

Modal presents sandboxes as one part of an AI infrastructure stack. The components serve different stages of an agent product:

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Component Role in an agent system Questions a buyer should ask
Inference Runs a model to interpret instructions, choose tools and generate outputs. What are the latency, throughput, model-choice and per-token economics?
Training Updates or fine-tunes models for a domain or task. Which GPU types, data pipelines and distributed-training features are supported?
Reinforcement learning Uses evaluations or feedback to improve decisions and tool use. Can experiments, rewards and evaluation runs be reproduced and compared?
Sandboxes Executes agent-generated code and tool calls in isolated environments. Can permissions, networking, files, resource limits and shutdowns be enforced and audited?
Developer workflow Connects APIs, observability, deployment and repeatable environments. Are logs, traces, versioning and integrations sufficient for production debugging?

The architectural argument is that these services reinforce one another. Inference chooses an action; the sandbox executes it; the resulting data can feed evaluation or reinforcement learning; improved models then make more reliable decisions. Keeping those loops close together can reduce integration work, although it can also increase dependence on one provider.

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What the reported numbers do—and do not—show

Figure How it was presented How to interpret it
$750 million raised Reported by Bloomberg Technology/MT Newswires in its October 14, 2026 coverage. Indicates substantial investor financing; it is not evidence of profitability.
$15.75 billion post-money valuation Reported valuation associated with that financing. A financing valuation, not a public-market price or audited company value.
More than 3 billion AI sandboxes Modal-reported customer usage figure cited in the same coverage. Company-reported scale; no independent audit, customer concentration or margin data was provided.
About $300 million in run-rate revenue Reported in a secondary rendering of the interview. A run-rate estimate, not audited historical revenue; the period and calculation methodology were not supplied.
Sandbox launch in 2023 Bernhardsson’s account in the interview rendering. He linked faster demand growth to models becoming able to generate fuller applications.

These figures establish a financing and usage narrative, not a complete business case. The available accounts do not disclose audited revenue, gross margins, retention, customer concentration or workload-level economics. A high sandbox count could include very short jobs, development experiments or repeated runs with different cost profiles.

How this differs from a GPU-only story

GPUs remain essential for training and inference, but the agent runtime determines whether a model can safely turn a decision into an outcome. A GPU service can return tokens while leaving developers to build process isolation, filesystem controls, networking rules, persistence, scheduling and audit logs themselves. Modal’s proposition is that those execution requirements should be delivered alongside compute.

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That does not make every workload an agent workload. Batch inference, conventional model training and applications with fixed business logic may need little or no general-purpose code execution. The sandbox layer matters most when the model must inspect changing data, run arbitrary programs, use developer tools or coordinate multiple steps.

How to evaluate an agent-infrastructure platform

Teams comparing hosted AI clouds should assess the complete operating model rather than count GPUs alone:

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  1. Isolation and security: Verify the boundary between a sandbox and the host, credential handling, package installation controls, network egress policy, resource quotas and emergency termination. Ask how commands and file changes are logged.
  2. Inference economics: Compare supported models, accelerator availability, latency targets, throughput, batching and pricing under the workload’s actual request pattern.
  3. Agent runtime: Check startup time, persistent state, files, tool adapters, virtual-machine support, retries, timeouts and concurrency limits.
  4. Training and reinforcement learning: Confirm support for fine-tuning, distributed jobs, evaluation suites, reward pipelines and experiment tracking if the product depends on continuous improvement.
  5. Developer workflow: Look for stable APIs, local reproduction, versioned images or environments, observability, tracing and integrations with existing coding-agent systems.
  6. Capacity and portability: Assess geographic coverage, GPU supply, quota policies, data-residency options and how difficult it would be to move workloads to another cloud.
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Why the claim matters beyond Modal

Jensen Huang, NVIDIA’s CEO, calls AI’s effect on the computing stack “a platform shift,” comparing it with earlier internet and cloud transitions that created new application layers. If agents become a standard way to operate software, the valuable layer may include orchestration, execution policy, state and evaluation—not just the model endpoint.

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Purchasing responsibility is also moving toward technical executives. McKinsey reported in 2025 that CIOs, CTOs and chief data and analytics officers lead nearly 60 percent of AI platform buying decisions. Those buyers are likely to evaluate security controls, operating cost and portability together because an agent platform touches both infrastructure and business processes.

Open questions and practical limits

The central commercial question is whether sandbox activity converts into durable paid workloads with attractive margins. The reported funding, valuation and usage figures do not answer that question. Nor do they establish how many customers run production systems, how concentrated revenue is, or how much each sandbox costs to provision and operate.

Infrastructure details, pricing, availability and partner programs can change quickly. Modal’s reported totals should therefore be treated as time-specific statements from the October 2026 coverage, not permanent specifications. For a deployment decision, request current service limits, security documentation, regional availability and an estimate based on the exact agent workload.

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