AI Progress in the U.S.: Build Faster, Deploy Safer

AI is getting cheaper and more capable at the same time—and that combination is why U.S. digital services are changing so quickly. OpenAI recently put a concrete number on the cost curve: the cost per unit of “intelligence” has been falling by roughly 40x per year over the last few years. That’s not a small efficiency gain; it’s a structural shift that turns “nice-to-have” automation into default product behavior.

Here’s the part most companies still miss: the gap between what many teams use AI for (chat, search, copy) and what frontier systems can do (multi-step problem solving, complex analysis, early-stage discovery) is huge. If you run a SaaS platform, an agency, a marketplace, or a startup in the U.S., this is the moment to upgrade how you think about product strategy, risk, and accountability—before your competitors do.

This post is part of our series, How AI Is Powering Technology and Digital Services in the United States. I’ll translate the key recommendations from frontier AI labs into practical choices you can make in 2026 planning: what to build, how to deploy responsibly, and how to avoid the two failure modes I see most often—shipping too timidly or shipping too carelessly.

The real story: AI is moving from “chat” to “work”

AI’s most important shift isn’t that it can talk—it’s that it can complete longer, higher-stakes units of work. Frontier systems have already progressed from handling tasks that take a person seconds to tasks that can take more than an hour, with expectations of days-to-weeks task completion on the near horizon. For digital services, that means product experiences will be built around outcomes, not features.

What “longer work” looks like inside U.S. digital services

Think in terms of workflows that currently bounce between tools and people:

• Customer support: from “suggest a reply” to “diagnose the issue, reproduce it, draft the fix, and prepare a customer-ready explanation.”
• Marketing operations: from “write this email” to “plan a campaign, generate variants, predict lift, run experiments, and summarize results.”
• Sales engineering: from “answer an RFP question” to “assemble an evidence-backed response set, map requirements to security controls, and produce a tailored demo plan.”
• Product analytics: from “summarize dashboards” to “explain what changed, identify likely causes, propose tests, and draft a stakeholder update.”

The stance I recommend: treat AI as a teammate that can execute projects, not a plugin that produces text. When you design around project completion, you naturally start caring about memory, access control, audit logs, evaluation, and escalation paths—all the things that separate a serious digital service from a weekend prototype.

The “80% to an AI researcher” implication

OpenAI argues that some systems now look “more like 80% of the way to an AI researcher than 20%.” Whether you agree with the exact framing or not, the practical implication is clear: your product team will soon have access to tools that can generate hypotheses, propose experiments, and synthesize literature-level context.

For U.S. startups and SaaS providers, this matters because it shortens the loop from idea → prototype → market test. It also increases the risk of shipping behaviors you didn’t intend. Which brings us to governance.

Responsible AI in the U.S. can’t be a 50-state patchwork

A strong claim in the source material is that today’s AI should diffuse widely and that most deployments should not face a messy “50-state patchwork” of extra burdens. That’s a business reality, not just a policy preference.

If you sell a digital service nationally, fragmented rules create two predictable outcomes:

1. Compliance becomes product complexity, which slows releases and raises costs.
2. Smaller companies lose, because only the biggest players can afford bespoke implementations per jurisdiction.

My take: the U.S. needs consistency for baseline deployments (customer chat, internal copilots, content assistance), and capability-based oversight for more powerful systems and higher-risk use cases. That aligns with the idea of public oversight “commensurate with capabilities.”

What “capability-based accountability” looks like in practice

If you’re building or deploying AI features, here’s a straightforward way to think about it:

• Low-risk automation (drafting, summarization, internal productivity)
  • Focus on privacy, data retention, access controls, and user disclosure.
• Medium-risk decision support (recommendations that affect money, access, or safety)
  • Add evaluations, human review, and monitoring for bias and error patterns.
• High-risk or dual-use capabilities (biosecurity, security exploitation, autonomous action)
  • Add strict gating, red-teaming, incident response playbooks, and stronger identity verification.

This tiering is how you avoid two bad strategies: treating all AI like it’s harmless autocomplete, or treating all AI like it’s regulated medical equipment.

Build an “AI resilience ecosystem” like cybersecurity—because you’ll need it

The most useful analogy in the OpenAI piece is the comparison to the early internet. The internet didn’t become trustworthy because one company promised it was safe. It became trustworthy because an entire cybersecurity ecosystem emerged: standards, monitoring, incident response, tooling, and professional practice.

AI needs the same.

The AI resilience checklist for SaaS and digital service teams

If you want a concrete starting point for 2026 planning, use this checklist. It’s intentionally operational.

1. Evaluation before launch
   • Define “unacceptable outputs” for your domain (security advice, harassment, regulated content, etc.).
   • Test with representative prompts and edge cases.
2. Monitoring after launch
   • Track rates of refusals, escalations, user complaints, and correction loops.
   • Monitor drift when prompts, tools, or models change.
3. Access control and least privilege
   • Don’t give your AI agent broad permissions “because it’s easier.”
   • Scope tool access per role, tenant, and action type.
4. Audit logs and incident response
   • Log tool calls and key decision points.
   • Create a playbook for “model did something wrong” that’s as real as your security playbook.
5. Data boundaries users can understand
   • Clear rules for what’s used for training, what’s stored, and what’s ephemeral.
   • Separate customer data across tenants by default.

A simple line I use internally: If an AI feature can take an action, it deserves the same seriousness as an engineer with production access.

Why this matters for lead generation and growth

This series is about how AI powers U.S. digital services—content, marketing automation, customer communication, and scale. Reliability and safety aren’t “compliance extras”; they’re growth drivers:

• Fewer hallucination-driven support escalations
• Higher customer trust and retention
• Faster enterprise procurement cycles
• Less brand risk during high-traffic moments (think holiday promotions, tax season, open enrollment)

Late December is a perfect time to pressure-test your resilience posture because traffic spikes and staffing gaps expose weak processes. If your AI assistant becomes the default front door while your team is on reduced coverage, monitoring and guardrails stop being theoretical.

Shared safety standards: why competitors still need to cooperate

Frontier labs have advocated for shared safety principles and shared safety research. This can sound abstract, but it has a concrete benefit for everyday product builders: shared standards reduce uncertainty.

What shared standards enable for U.S. startups and service providers

When there are common expectations, you can build faster because you’re not guessing what “good” looks like. A mature standards environment typically creates:

• Comparable evaluations (so you can measure improvements honestly)
• Clear vendor requirements (so procurement isn’t a bespoke debate)
• Better incident coordination (so one company’s discovery helps others)

I’m strongly in favor of this direction, especially for U.S. SaaS ecosystems where vendors chain together (CRM → support desk → marketing automation → data warehouse). A weak link anywhere becomes everyone’s problem.

Ongoing measurement: you can’t steer what you don’t track

OpenAI highlights that predicting AI’s real-world impacts—especially on jobs—has been hard because AI strengths don’t mirror human strengths. That’s exactly why measurement beats speculation.

Metrics that actually help teams manage AI impact

If you’re deploying AI across customer communication, marketing automation, or internal ops, track metrics that connect model behavior to business outcomes:

• Deflection rate vs. resolution quality (support)
• Time-to-first-meaningful-draft (marketing and sales)
• Human edits per output (a proxy for trust and accuracy)
• Escalation rate and root causes (where the model fails)
• Customer sentiment on AI-assisted interactions (CSAT/NPS tagged)

The goal isn’t perfect prediction. The goal is building a feedback loop tight enough that you can correct course quickly.

Individual empowerment: the next “utility layer” for digital services

One of the most direct recommendations is that adults should be able to use AI on their own terms, within broad social bounds—and that access to advanced AI may become a foundational utility, comparable to electricity or clean water.

Whether or not you agree with the analogy, product teams should internalize the implication: users will expect AI to be available everywhere, and they’ll expect it to respect their preferences.

What “empowerment by design” means for your product

Practical features that signal empowerment (and reduce risk):

• User-controllable modes: “draft,” “suggest,” “auto-complete,” “take action.”
• Transparent citations to internal sources (where applicable): show what account record or doc was used.
• Easy off-switches: don’t bury AI disablement behind support tickets.
• Permission prompts that feel like banking apps: clear, specific, revocable.

If you want a north star for 2026: give users more control as capabilities increase, not less.

What to do next if you run a U.S. digital service

Most teams don’t need a moonshot AI roadmap. They need three disciplined moves.

1. Pick one workflow where time-to-outcome is measurable
   • Support resolution, campaign production, sales enablement, onboarding—choose one.
2. Build the resilience layer alongside the feature
   • Logging, evaluations, access control, and escalation aren’t “phase two.”
3. Write your accountability story before customers ask
   • What data is used? Who reviews? What happens when it’s wrong? Put it in plain language.

The next wave of AI in the United States won’t be won by companies that merely add chat. It’ll be won by companies that turn AI capability into dependable digital services—and can prove it.

If AI keeps getting 40x cheaper per year, what would you build if reliability and safety weren’t bottlenecks anymore—and what would you stop building because AI just made it obsolete?