The Arithmetic of Divergence

When acceleration meets inequality, the transition reveals its true mathematics.

The Transition Monitor l November 13, 2025

Reporting from the edge of the algorithmic frontier.

Opening Reflection

The transition is performing a calculation we're only beginning to read. On one side of the ledger: $192.7 billion flowing into AI startups this year alone, Microsoft pursuing superintelligence independently of OpenAI, venture capitalists placing bets that exceed entire national economies. On the other: 1.09 million American jobs eliminated by October, the wealth from AI stock gains concentrating in the top 1% who hold half of all corporate equity, and public concern about AI's societal impact climbing to 47%—up from 34% just eleven months ago.

The mathematics here aren't abstract. They're accumulating in real time, compounding daily in both directions. While AI companies achieve unprecedented revenue-per-employee ratios and reach $100 million in annual recurring revenue faster than any technology in history, the bottom 50% of American households collectively hold just 1% of stock wealth—effectively locked out of the very boom reshaping their economic reality. This isn't a contradiction in the transition. It's the transition showing us its structure.

What November 13th reveals is not chaos but divergence—the systematic widening of gaps between those positioned to capture AI's upside and those experiencing its displacement. The technology compounds advantages and accelerations on one trajectory while compounding precarity and dislocation on another. Both are real. Both are intensifying. And the distance between them is becoming the defining feature of this historical moment.

Today's Signals

The displacement mathematics reached a grim milestone that economists are calling a "jobless profit boom." Tech sector layoffs attributed to AI are occurring alongside exceptionally strong profit growth in these companies, representing a significant departure from historical patterns where job cuts typically followed declining profitability. October 2025 recorded 153,074 announced job cuts, the highest for any October in over twenty years, with AI cited as the second-most common reason after cost-cutting. The pattern suggests something beyond cyclical adjustment—a fundamental recalibration of labor's relationship to capital as algorithmic systems demonstrate their capacity to maintain and even increase productivity with dramatically reduced headcounts.

The AI stock boom has disproportionately benefited the wealthiest households, with the top 1% owning half of the $51.2 trillion in corporate stock and mutual fund shares as of Q2 2025, while the bottom 50% collectively held just 1%. This wealth concentration isn't incidental to the AI transition—it's structural. As AI companies that barely existed three years ago achieve valuations exceeding small nations' GDPs, the economic gains flow through equity markets to those already positioned at the top of the wealth distribution. The result is what economists describe as a widening gulf that could create "political fracturing," making consensus increasingly difficult to achieve.

On the investment front, the acceleration shows no signs of moderating. Venture capitalists have poured $192.7 billion into AI startups so far in 2025, setting new global records and putting the year on track to become the first where more than half of total VC dollars went into the industry. The capital isn't distributed evenly—it's concentrating in late-stage rounds to proven players, with Anthropic's $13 billion Series F and OpenAI's restructured Microsoft partnership exemplifying the "bigger bets" strategy dominating the landscape. Meanwhile, more than 30% of all venture investment in Q3 went to just eighteen companies that raised funding rounds of $500 million or more each, well above historical proportions.

The policy landscape reflects this divergence through fragmentation. President Trump's January 2025 executive order "Removing Barriers to American Leadership in AI" rescinded Biden's comprehensive AI oversight order and shifted explicitly toward deregulation, prioritizing innovation and U.S. competitiveness while instructing agencies to eliminate policies that might hinder American AI dominance. At the state level, lawmakers accelerated AI regulation in 2025, with 210 bills tracked across 42 states, though only around 9% were enacted, as legislatures moved away from sweeping frameworks toward narrower, transparency-driven approaches. The result is a patchwork that mirrors the economic divergence—federal acceleration meeting state-level caution, with no coherent center holding the competing forces together.

Culturally, the gap between users and skeptics is widening. The share of Americans believing AI will negatively affect society has increased from 34% in December 2024 to 47% in June 2025, with 50% now saying they're more concerned than excited about AI's increased use in daily life. Yet among those who use AI tools weekly—about one-third of Americans—the sentiment flips: 51% see positive impact. The transition is creating two lived experiences of the same technology, sorted largely by proximity to its benefits versus its disruptions.

Reflection

November 13th offers us a clear image: the transition isn't unfolding—it's bifurcating. Every acceleration on one path generates a corresponding dislocation on another. Every billion-dollar funding round coexists with thousands of jobs attributed to AI efficiency gains. Every stock market surge widens the gap between equity holders and wage earners. The transition's velocity isn't the same for everyone experiencing it.

This divergence isn't a bug to be fixed through better policy or fairer distribution, though both matter enormously. It's embedded in the technology's mathematical structure—its tendency to concentrate advantages, to reward those already positioned to capture returns, to amplify existing asymmetries rather than level them. The question facing us isn't whether AI will transform society. It's whether that transformation will compound inequality to breaking points, or whether we'll find mechanisms to redistribute both the gains and the disruptions more equitably.

What today's signals suggest is that we're not yet building those mechanisms. Instead, we're watching the divergence accelerate—capital flowing upward, displacement flowing downward, and the distance between growing daily. The transition is performing its calculation. We're still deciding whether we're comfortable with the sum.

Mood of the Transition

Calculated divergence—wealth accelerating upward while disruption compounds below, each trajectory mathematically precise.

Category Analysis

Displacement (Transition Strength: 5/5)

US layoffs surpassed 1 million in 2025, with October delivering 153,074 job cuts—the worst single-month spike in over two decades, as AI was cited as the second-most-common reason for cuts after cost-cutting. The "jobless profit boom" represents a structural shift: tech giants eliminating roles while posting record earnings, productivity surging without corresponding employment gains, and a permanent loss of 5% of private-sector payrolls compared to pre-pandemic trends.

Deployment (Transition Strength: 4/5)

McKinsey's State of AI 2025 survey found that 88% of companies now use AI somewhere, but most remain in experimentation or pilot phases, with just 33% actually scaling it and only 6% achieving EBIT impact of 5% or more. The gap between adoption and impact signals that while deployment is widespread, effective integration remains elusive for most organizations. European and Israeli AI application companies are gaining ground, raising 66 cents for every dollar their American counterparts raise in 2025.

Performance (Transition Strength: 4/5)

Microsoft formed the MAI Superintelligence Team to pursue AI systems capable of surpassing human-level performance far beyond AGI thresholds, with the revised OpenAI partnership allowing Microsoft to independently research artificial general intelligence and use OpenAI's models to accelerate that research. Google's DeepMind unveiled robotics models claiming leadership across 15 academic benchmarks, while the focus on "world models" and physical-world understanding represents the next major algorithmic leap toward AGI.

Investment (Transition Strength: 5/5)

AI investments in 2025 have reached $192.7 billion, surpassing any previous full year including the $168.1 billion raised during the AI boom of 2021, with AI funding now accounting for 52.5% of global VC deal value. SoftBank's $40 billion investment in OpenAI set a record for the largest private company investment, while Anthropic's $13 billion Series F marked Q3's largest funding round. Capital concentration is intensifying—fewer deals, larger bets, with investor focus on proven enterprise applications and critical infrastructure.

Policy (Transition Strength: 3/5)

The Trump administration's "Removing Barriers to American Leadership in AI" executive order replaced Biden's comprehensive oversight framework, shifting toward deregulation and explicitly prioritizing innovation over safety standards. Meanwhile, state legislatures proposed 210 AI-related bills across 42 states, with only 20 bills (around 9%) enacted, reflecting a move toward use-specific regulations in healthcare, employment, and consumer protection rather than comprehensive frameworks. The federal-state divergence creates compliance complexity but no unified governance approach.

Culture (Transition Strength: 4/5)

American concern about AI's societal impact rose from 34% in December 2024 to 47% in June 2025, with specific worries increasing about human dependency (50%), creativity diminishment (49%), deepfakes (63%), and decreased face-to-face interaction (46%). The cultural response is splitting along usage lines: weekly AI users see benefits (51% positive), while the broader population grows increasingly wary. One in three employees reports AI has created tension or conflict between teams, though organizations with leadership-driven AI strategies report 62% full engagement compared to 50% in other settings.

The Transition Monitor tracks the six dimensions of AI's integration into human systems: Displacement, Deployment, Performance, Investment, Policy, and Culture. Each daily report synthesizes global developments into narrative form, capturing not just what happened, but what the transition felt like on that particular day.