2026-03-20 08:00:00
In 2025, we predicted that 2026 would be the year agents would earn as much as a person.
It’s already happening.
In markets where there’s a labor shortage and an urgent need to hire people, we are seeing agents command 75%, 85%, even 100% of a human equivalent salary. This is faster than we were anticipating.
The first-order benefit is completing the work.
But there are second-order benefits that are now starting to appear. Training agents is significantly faster since all materials can be presented at once & in parallel to the AI.
Agents typically require less management burden. They can work 24 hours faster or slower as the team needs. Capacity scales as a function of willingness to spend on inference.
Then, a third-order benefit : significantly lower tax burden. Robotic workers are not taxed to the same extent as humans. No FICA. No state unemployment insurance. No benefits. At least a 25-30% cost reduction for the same salary.1 Plus agent software cost is tax-deductible up to $2.56m.2
In other categories where AI is augmenting existing workers, the sale is different. Here, the sale captures the marginal hire rather than a big swath of the team.3
In both conversations, usage tends to surge because of the effectiveness of the systems, much faster than both the vendor and the buyer anticipate.
At that point, the business often pauses because a strategic review of organizational design needs to take place.
The market rewards this shift. Goldman Sachs found that low-labor-cost stocks outperformed high-labor-cost stocks by 8 percentage points in 2025.4 Labor’s share of GDP hit a record low of 53.8% in Q3 2025.5 The implication : every dollar shifted from labor to software improves margins & stock performance.
Across the S&P 500, labor costs represent about 12% of revenues on average.6 Software costs sit around 1-3%. As agents absorb labor, that ratio inverts. Labor shrinks. Software expands. The total addressable market for software grows at labor’s expense, while profitability grows.
In the short term, this means no pricing competition on a per-agent basis. Vendors aren’t racing to the bottom ; they can price at par to a person.
Sources
California employer costs : 7.65% FICA + 3.4% SUTA + 0.1% ETT + ~25% benefits = ~36% on top of base salary. CA EDD Payroll Tax Rates, BLS Employer Costs ↩︎
Goldman Sachs / Futunn : S&P 500 Surges 32% - Cooling Labor Costs as Hidden Driver ↩︎
Fortune : U.S. Workers Took Home Smallest Share of Capital Since 1947, FRED Labor Share Data ↩︎
Goldman Sachs : How Labor Costs Have Affected Corporate Margins ↩︎
2026-03-18 08:00:00
I set up a race today between two robots.
My Mac on the left vs Claude Code on the right. Both tasked with building a payment app on Stripe’s new Tempo blockchain. Same prompts, same task, side by side.
Opus 4.5 is about 20% smarter than Qwen 35B on benchmarks. And it’s likely 50x larger. The hare should have won. It didn’t.
The local model finished in 2 minutes. Claude took over 6. I asked Claude to score both outputs : local model 6.5, Claude 4.5.1
Video plays at 2x speed.
With 3x faster responses, I could add an extra cycle : “critique the plan and address the critiques.” In the time the hare was still thinking, the tortoise ran another lap.
| Prompt | Local (Qwen 35B) | Claude (Opus 4.5) |
|---|---|---|
| Research Tempo & create plan | 20.9s | 55s |
| Critique the plan | 16.5s | 1m 35s |
| Which language is best? | 16.5s | 1m 35s |
| Research feedback online | 48.9s | 2m 35s |
| Save implementation plan | 15.4s | 44s |
| Total | ~2 min | ~6 min 24s |
Faster responses mean more rounds of revision before a meeting ends or attention drifts. It’s different for agentic coding workflows & complex codebases, where slower work may lead to better outcomes. But for everyday tasks, faster models can enable tighter feedback loops. Tighter loops can produce better outcomes.
We don’t always need the smartest AI to get the job done.
2026-03-15 08:00:00
“What happens when a new employee brings their agent to work?”
An executive asked this recently. Imagine a few years from now : a student graduates, having trained their own agent through university. It knows everything they’ve learned, every paper, every problem solved. Day one, they bring it to work.
It’s like bring your own device circa 2009. The iPhone launched & nobody wanted corporate Blackberries1 anymore. IT scrambled to adapt.
But a rogue phone couldn’t sign contracts. A rogue agent can.
Amazon just learned this at scale. $6.3 million in lost orders. 99% order volume drop across North America. Four severity one incidents in one week.23
Amazon’s AI coding assistant contributed to at least one major production incident. The response : a 90-day safety reset with mandatory two-person review for all code changes.
An internal memo admitted what everyone implicitly knows :
“Best practices and safeguards around generative AI usage haven’t been fully established yet.”3
Companies can’t hide behind hallucinations. Utah’s AI Policy Act4 eliminates the hallucination defense :
“It is not an affirmative defense to assert that the GenAI tool made the violative statement or undertook the violative act.”
Newer and larger models are smarter and more reliable.5 But they fail unexpectedly. There is no relationship between size and how failures change over time. AI-generated code creates 70% more issues than human code.6
The TRUMP AMERICA AI Act would create explicit liability pathways - allowing the US Attorney General, state AGs, & private plaintiffs to sue AI developers for defective design & unreasonably dangerous products.7
That new hire’s personal agent? The company bears liability for its mistakes. The contracts it signs, the code it deploys, all of it lands on the company.
Like a dog or a device, you are responsible for your agent.
2026-03-13 08:00:00
What happens when your AI doesn’t answer?
Everything is in short supply. It’s no longer just GPUs. It’s power. Data centers. Memory. CPUs.
If there’s no relief for six more quarters, perhaps it’s time to plan for a world where inference isn’t freely available on-demand.
Inference prices, which have been static, will rise. Subsidies will be harder to justify.
Enterprises will need to rationalize workloads, deciding which teams receive state-of-the-art models & which don’t. Not every CRM update requires a trillion-parameter frontier model.
Inference rationing normalizes. Marketing receives this much, sales receives that much, software engineers probably receive a lot more.
Constraint will be the mother of invention. Companies will optimize what they have, adopt open source where they can, and likely move to smaller models for many workloads.
2026-03-12 08:00:00
In September 2024, Hurricane Helene flooded Baxter International’s plant in Marion, North Carolina, which produced 60% of the nation’s IV fluids. Within a week, more than 80% of U.S. healthcare organizations reported shortages. One plant, one flood, one week.
That disruption made headlines. Most don’t. Eighty-five million packages arrived damaged in the U.S. in 2024, up 30% from the prior year, costing businesses $4 billion.
Sean McCarthy saw those failures accumulate during his years at Amazon Shipping, where he was one of the early hires. The investigation process never varied. Query the warehouse management system, often two decades old. Cross-reference the carrier portal. Call the driver, who doesn’t pick up. File a claim: seventeen fields. Four hours pass. Sometimes the problem gets solved.
The obstacle was fragmentation. A single shipment can touch 40 to 60 processes across multiple vendors. Connecting them would mean hundreds of bespoke integrations. The project never got funded.
Sean partnered with Henry Ou, who led ML teams at Apple and built ranking systems at ByteDance. Together they founded BackOps, which deploys AI agents that read emails, click through portals, call drivers, and file claims. When a customer reports a problem, BackOps traces it across every system involved, escalating to a human only when a judgment call is required.
We’re leading BackOps’s $26 million Series A.
The product works in two stages. Employees record their screens while solving problems; BackOps converts those recordings into automated workflows. Then Relay, the automation engine, runs continuously: filing claims, initiating reshipments, responding to customers.
Customers report 93% faster response times and 60% time savings. BackOps files 100% of eligible carrier claims automatically. The platform serves a top global automaker, a leading retailer, major grocery chains, and industrial suppliers.
Sean and Henry are targeting a $3.5 billion market growing 13% annually. The bet: AI agents can connect systems that were never designed to talk to each other. So far, the connections hold.
If you’d like to learn more, reach out to Sean.
2026-03-11 08:00:00
AI eliminates the marginal hire.
Tech job openings are down 45% from the 2022 peak, but up 16% since the start of 2026 - from 227k to 264k. Why the narrative violation?
Companies are hiring again, just fewer people than before. A reset to a lower baseline.
A team that would have added two engineers to hit next year’s roadmap now ships with the headcount they have. Cursor, Claude Code, Copilot close the gap. The job postings never go live. The offers never extend.
Inside most organizations, headcount stays flat. No layoffs. No restructuring announcements. Just fewer new hires than planned.
Block slashing 40% of its workforce showed what happens when a company acts on this logic all at once. Jack Dorsey explained : “Intelligence tools we’re creating & using, paired with smaller & flatter teams, are enabling a new way of working which fundamentally changes what it means to build & run a company.”
Most companies won’t restructure so dramatically. Until an economic shock, a missed quarter, or pressure from the board forces the question. What AI made possible, AI makes necessary. The restructuring that might have happened gradually over five years happens in one quarter.
The seismic shock isn’t coming out of nowhere. It’s building invisibly, one unposted job at a time.
