AI Is Moving Faster Than Institutions
AI isn't a trend. It's the new baseline for how economies operate and how services get delivered. Many experts warn the next two years could bring shifts that outpace our policy cycles and procurement timelines.
- AI is permanent. It will not fade away.
- AI is learning faster than we are. Capability gain outstrips human upskilling.
- AI will replace many jobs. That process has started.
These facts should set the tone for government. If we delay, the benefits consolidate in private hands while the public sector inherits the social costs.
What's Different This Time
AI concentrates knowledge and decision power at a scale no previous tool has offered. It builds, invents, and advises with speed that breaks our normal review loops.
Some tech leaders even suggest money could lose meaning if production and services approach near-zero marginal cost. Whether you agree or not, policy needs to anticipate outsized disruption in jobs, markets, and governance.
The Profit Machine: Why Replacement Happens
Firms are incentivized to automate routine work. AI doesn't take breaks, ask for raises, or call in sick. Cost savings flow to owners unless policy redirects a share to the public.
Evidence is already visible. A McKinsey survey reports high adoption across large companies, including advanced generative systems that touch knowledge work at scale. Source
The Government Challenge
If AI concentrates gains and externalizes risk, it's the state's job to correct course. We need to protect communities, keep services reliable, and spread benefits beyond shareholders.
The question isn't whether to act. It's how to act quickly, with clear guardrails and measurable outcomes.
Policy Priorities You Can Move On Now
- Set a national risk baseline. Create an AI incident reporting system, a public registry of high-risk deployments, and standardized impact assessments for government use.
- License frontier models that cross risk thresholds. Require third-party evaluations, red-team testing, and safety disclosures before deployment in critical contexts.
- Track compute and model capability. Require reporting for large training runs and sensitive fine-tunes (security, bio, critical infrastructure).
- Procurement with protections. Mandate contractual guardrails: data residency, privacy by default, audit rights, security standards, content provenance, and clear liability for vendor failures.
- Algorithmic accountability. Publish model cards, data use statements, known limits, and appeal processes for automated decisions that affect citizens.
- Public benefit mechanisms. Consider windfall triggers, data dividends for public datasets, or shared savings schemes when automation removes roles in publicly funded services.
- Workforce transition at scale. Fund retraining, paid on-the-job upskilling, and redeployment pathways for affected roles. Tie training to real vacancies, not generic courses.
- Strengthen service delivery safely. Use AI for triage, summarization, and backlog reduction with human oversight. Ban fully automated eligibility or enforcement decisions without appeal.
- Secure infrastructure. Harden agencies against AI-enabled phishing, deepfakes, and data exfiltration. Adopt content authenticity standards and watermarking where feasible.
- Build public digital goods. Support privacy-preserving datasets, open evaluation suites, and shared tooling so smaller agencies and researchers aren't locked out.
- Measure and publish. Set KPIs for service quality, cost per case, time to resolution, error rates, and equity outcomes. Report quarterly.
For implementation frameworks, the NIST AI Risk Management Framework offers a useful starting point for controls and evaluation. NIST AI RMF
90-Day Action Plan for Any Department
- Week 1-2: Appoint an AI lead and form a small steering squad (policy, legal, cyber, data, ops). Freeze unsanctioned AI pilots until reviewed.
- Week 2-4: Inventory all AI use (official and shadow). Classify by risk: low (assistive), medium (advisory), high (rights-impacting). Document vendors, data flows, and human oversight.
- Week 4-6: Ship a department AI policy: approved tools, data handling, human-in-the-loop, red-teaming, procurement clauses, incident reporting, and records management.
- Week 6-8: Run two low-risk pilots with clear success metrics: email triage, form summarization, knowledge base Q&A. Track time saved, accuracy, and failure modes.
- Week 8-10: Train managers and frontline staff on safe use, privacy, and prompt hygiene. Set up office hours for support and escalation.
- Week 10-12: Publish results, retire what fails, scale what works, and add controls where gaps appear.
Workforce: Practical Moves
- Pair each at-risk role with a reskilling pathway and a target destination role.
- Guarantee paid learning time and recognized micro-credentials.
- Stand up an internal "AI help desk" to support daily use and reduce shadow tools.
- Negotiate fair automation clauses with unions: notice periods, redeployment rights, and shared savings.
If you need structured learning paths mapped to job families, explore current options and certifications. Courses by job and popular certifications can help teams skill up without wasting budget.
Guardrails for Fair Benefits
- Citizen focus. Prioritize AI that cuts wait times, reduces errors, and improves access for vulnerable groups. Test with real users.
- Equity checks. Run bias tests by subgroup and publish findings. Add human review for edge cases.
- Appeals and recourse. Make it simple to challenge automated outcomes, with clear timelines and human contact.
- Transparency. Label AI-assisted interactions, keep audit logs, and meet freedom-of-information obligations.
The Window Is Short
AI will keep advancing whether we act or not. If we don't set the rules, market incentives will do it for us.
This is the job: protect the public, direct the gains, and keep services trustworthy. Start now, measure everything, and course-correct in public.
Your membership also unlocks:
