Balance the Triangle Daily Brief — February 11, 2026
Technology is moving faster than society is adapting.
The tension: Your board approved AI budgets assuming 30% productivity gains. Only 8.6% of pilots reach production—down from 12% in 2024. You’re paying for capability you can’t deploy, and your CFO is asking why.
Why This Matters
Big Tech is betting $650B (roughly 40% of their combined free cash flow) on infrastructure for AI adoption that isn’t happening. States passed AI laws, but Trump’s December order contests who enforces them. Meanwhile, 95% of your GenAI pilots are failing because of a data permissions problem from 2018.
The imbalance: Capability investment is surging. Adoption is stalling. Governance is contested. Someone owns fixing this—and it’s probably you.
At a Glance
- Big Tech commits $650B to AI infrastructure (40% of FCF, 60%+ above 2025)
- Trump order vs state AI laws creates dual compliance requirement
- 8.6% production rate, down from 12%—adoption is slowing
Pattern: Infrastructure assumes explosive adoption. Adoption is hitting governance walls. Governance is in legal limbo.
Story 1: Big Tech’s $650B Bet—And Your Pricing Risk
What Happened
Alphabet, Amazon, Meta, Microsoft will spend ~$650B on AI infrastructure in 2026—representing roughly 40% of their combined free cash flow and 60%+ above 2025 levels. This is the largest corporate capex cycle in history, deployed before clear enterprise demand signals.
Why It Matters
Current AI pricing is subsidized by this infrastructure buildout. If enterprise adoption stalls (it’s already slowing—8.6% production rate vs 12% in 2024), providers will either raise prices to recover costs or write down stranded assets. Either way, your AI economics change.
Who’s Winning
Nvidia’s competitors (AMD, Google TPU, Amazon Trainium) are gaining share as buyers diversify to avoid single-vendor lock-in. Smart buyers are negotiating multi-year pricing locks and exit clauses now.
Do This Next (3-Step Framework)
Monday morning:
- Pull your current AI vendor contracts—identify pricing escalation clauses
- Model a scenario where AI API costs increase 2-3x in 2027
- Schedule vendor meeting to negotiate: (a) multi-year pricing lock, (b) volume discounts with no minimum commitment, (c) 90-day termination clause
What to say to vendors: “Your infrastructure spend is 60%+ above last year. Walk me through how that doesn’t flow into my pricing in 2027.”
One Key Risk
You lock into today’s pricing, vendor raises prices next year, competitors stay flat—you’re stuck in an overpriced contract while competitors gain cost advantage.
Bottom Line
Today’s AI pricing assumes explosive adoption. Adoption is slowing. Negotiate pricing protection now before vendors realize their bet isn’t paying off.
Story 2: Federal vs State AI Laws—Decision Tree
What Happened
Trump’s December 11 executive order signals federal intent to preempt state AI laws. Colorado delayed its AI Act from February to June. California and New York passed comprehensive laws that now face potential federal challenge. Legal clarity is 18-24 months away.
Why It Matters
Compliance teams are building dual frameworks (state + federal) at massive cost. But “comply with both” isn’t a strategy—it’s budget destruction. You need a prioritization framework.
Who’s Winning
Companies with revenue concentrated in 1-2 states are choosing a “lead market” approach—build for California (strictest), layer in federal later. Companies with federal contracts are waiting for federal clarity and accepting state risk.
Do This Next (Decision Tree)
If >60% revenue from CA/NY/CO:
- Build to California standard (strictest wins)
- Document why you chose this approach
- Budget for federal layer-in later
If >40% revenue from federal contracts:
- Document existing risk assessment process
- Classify AI systems conservatively (high-risk)
- Wait for federal clarity, accept state enforcement risk
If revenue is distributed:
- Choose California as baseline (highest litigation risk)
- Build modular compliance (swap frameworks if federal preempts)
What to tell your board: “We’re betting on [state/federal], accepting [X risk], budgeting $[Y] for pivot if we’re wrong. Decision point is Q3 2026 when litigation clarity emerges.”
One Key Risk
California sues over preemption, litigation drags to 2028, you’ve spent millions building federal compliance for a regime that doesn’t exist.
Bottom Line
You can’t afford both. Pick your regulatory bet, document it, budget for the pivot.
Story 3: The 8.6% Problem—Governance Debt Audit
What Happened
Only 8.6% of enterprises moved AI to production in 2025—down from 12% in 2024. Adoption is slowing, not accelerating. The blocker isn’t technology—it’s data governance debt. 95% of GenAI pilots fail to deliver measurable returns because organizations discover their permission structures expose the wrong data to the wrong people.
Why It Matters
Your AI licenses are sitting unused because your data team hasn’t solved a permissions problem from 2018. You’re paying SaaS fees for tools you can’t safely turn on.
Who’s Winning
One Fortune 500 financial services firm (per Gartner case study) ran a 90-day “governance sprint”: audited access to top 20 datasets, tightened permissions, deployed Copilot to 5,000 users in Q4 2025. Their approach: fix permissions first, then deploy AI.
Do This Next (Governance Debt Audit—Start Monday)
Week 1: Pull access logs
- Identify your 10 most sensitive datasets (PII, financials, IP)
- Export current access permissions
- Flag any user/role with access broader than their job function
Week 2: Run AI exposure test
- If you turned on Copilot/Claude/ChatGPT Enterprise today, what could each role query?
- Map “what AI could access” vs “what users should access”
- Quantify the gap (% of users over-permissioned)
Week 3: Triage plan
- High-risk roles (finance, HR, legal): tighten immediately
- Medium-risk: 90-day remediation plan
- Low-risk: accept risk, document why
What to tell your CIO: “We’re paying for AI tools we can’t deploy because [X%] of users have permission scope broader than their role. Fix this first, then we turn on AI.”
One Key Risk
You skip the audit, deploy AI anyway, someone queries data they shouldn’t, breach gets disclosed, board asks “why didn’t we know our permissions were broken?”
Bottom Line
Governance debt is a 2018 problem blocking a 2026 capability. Fix permissions, then deploy AI. Not the other way around.
The Decision You Own
This week, answer these three questions:
- Pricing protection: Do your AI vendor contracts have escalation clauses? What happens if pricing doubles in 2027?
- Regulatory bet: Which regime are you building for—state or federal? What’s your pivot budget if you’re wrong?
- Governance audit: Can you safely deploy the AI tools you’ve licensed? If not, what’s Week 1 of fixing permissions?
Forward this brief to one person who owns part of this answer. CC yourself. Put “Decision Required—AI Risk Exposure” in the subject line.
What’s Actually Changing
The gap is widening, not closing:
- Infrastructure spending assumes 30% adoption. Reality is 8.6%—and falling.
- Regulatory frameworks exist but enforcement authority is contested for 18+ months.
- Organizations are paying for AI they can’t deploy because they haven’t fixed 2018’s data mess.
The pattern: Everyone is building capability. No one is building governance. The orgs that fix governance this quarter will deploy AI in 2026. The ones that wait will still be running pilots in 2027—paying licenses for tools they can’t turn on.
Your decision: Build governance now and deploy later, or keep running pilots and hope governance solves itself.
It won’t.
Sources
https://www.techrepublic.com/article/ai-adoption-trends-enterprise/
BALANCE THE TRIANGLE LABS 