In the wake of the week…

AI may be early in most companies, but not in tech, where leaders are racing to reshape their org charts around agents instead of employees. MIT and Stanford expose the state of play for AI in businesses right now, while California lawmakers are drawing new lines around algorithmic authority in the workplace. The shift from experiments to systems is underway.

And in Extra Credit, AI-native firms are coming for the $20 trillion professional services market, trading reputation for results and rewriting the rules on pricing and scale. Rising chatbot use is creating new cybersecurity blind spots that companies are scrambling to contain. And in Washington, lawmakers are turning up the heat on Big Tech’s AI investments, raising fresh questions about influence, access, and competition in the next phase of the platform economy.

Let’s dive in. 👇

ONE // Strategy in the Shallows: Stanford and MIT Provide AI Reality Check

You’ve probably seen the headlines: If you haven’t done X with AI, you’re already behind. MIT and Stanford say: don’t believe the hype. Two years into the generative AI boom, most companies are still waiting for big returns. MIT Sloan urges leaders to trade grand plans for practical execution. Stanford’s AI Index confirms the pattern: usage is up, investment is heavy, but financial results remain limited. The gap between adoption and impact tells the real story of where AI stands in 2025.

What the Data Shows

• Adoption is surging because GenAI is easy to experiment with.

• Returns are shallow because most companies are early, disorganized, or lack foundational capabilities like clean data, governance, and training.

• Incremental gains today reflect fragmentation, not failure.

• Strategic alignment and infrastructure maturity will unlock broader and more consistent impact.

What to Expect

As companies get more focused by standardizing use cases, managing risk, and embedding AI into core workflows rather than bolting it on as an afterthought, we should expect stronger financial returns. These results may not come from cost cuts alone. Faster execution, better decisions, and new business models will also drive impact.

MIT calls this the “small t” before the big transformation. Stanford’s data backs it up. Companies that report impact today are using AI in targeted, functional ways, especially in marketing, supply chain, and service operations. When more firms shift from pilots to platform-level integration, the payoff will grow.

Leadership Insight

Start by picking one business function where AI can create measurable impact, then build the data access, workflows, and team routines around that use case. Treat it like a product launch, not an IT rollout. Once it works, scale it across similar functions.

The Bottom Line: The fastest way to real ROI is to stop chasing use cases and start delivering one. Momentum comes from making one part of the business better, then repeating what works. Full-blown transformation can wait. Traction today is the foundation for everything that comes next.

Sources: Stanford: The 2025 AI Index [report], MIT Sloan Management Review

TWO // Humans Optional: Inside Tech’s Not-So-Quiet Pivot to the Agent Workforce

In our lead story, we highlighted that most companies are still early in the AI adoption cycle. The exception, as you might expect, is Big Tech, where leaders are in a race to automate work before hiring for it. Shopify now requires managers to prove AI cannot do the job before opening a role. Salesforce has paused engineering hires and is scaling its Agentforce platform to serve as a full-service digital workforce.

Driving the Trend

• Shopify teams must justify headcount requests by showing AI cannot do the job.

• Salesforce AI agents resolved 84% of 380,000 customer cases with minimal human involvement.

• Google reports that AI now writes more than one in four new lines of code.

• Meta is reassigning work from mid-level engineers to AI systems that handle routine tasks.

What to Expect

Headcount is no longer the primary lever for growth in the tech sector. As agent-based systems become more capable, companies will focus on structuring teams around capability gaps, not job titles. Management will shift toward coordinating human and digital contributors side by side.

Leadership Insight

The tech sector often moves first, but not always with a map. Leaders outside of Big Tech should treat these shifts as early signals, not marching orders. The rise of AI agents is a reminder to look closely at where work is getting done, how teams are structured, and where new capabilities are emerging inside your organization. In most cases, the right prescription will be augmentation, not complete automation.

The Bottom Line: AI agents are changing how tech companies think about work. For everyone else, the opportunity is to observe, experiment, and prepare for a future where teams include both people and machines.

Sources: Business Insider, The San Francisco Standard, The Pragmatic Engineer [Substack], Digital Native [Substack]

THREE // The Algorithm Will See You Now: Meet the AI Boss, Not the Same as the Old Boss

California lawmakers have proposed the “No Robo Bosses” Act, which would require human oversight for AI systems making decisions about hiring, promotions, and firings. Behind the bill is a broader trend: generative AI is reshaping management across industries. Algorithms now assign tasks, evaluate performance, and influence career paths.

By the Numbers

• $500 — Civil penalty per violation under California’s SB 7

• 21 — Days employers would have to reverse a decision after a successful appeal

• 0 — Federal laws requiring human review of algorithmic employment decisions

Why It Matters

AI management tools are spreading across workplaces, often without clear rules or human oversight. Researchers warn these systems can erode job quality, reduce autonomy, and push workers out of key decisions. The shift also blurs lines between support tools and control systems, creating accountability gaps with real consequences.

Leadership Insight

If you're deploying AI or algorithmic tools to make people decisions, think of it like changing your company’s operating system. These systems shape how work gets assigned, how performance gets judged, and how decisions get made. Build in oversight from the start, and make sure managers stay responsible for the outcomes.

The Bottom Line: By legislation or backlash, algorithmic oversight is coming. Smart companies will move proactively to embed transparency, consent, and accountability into every AI-powered decision that touches their people.

Sources: SHRM, Built In, Data & Society [podcast]

Extra Credit

For the overachievers: These are the stories that didn’t crack the top three but are too important to ignore—quick hits on what’s happening and why it matters.

Anthropic Provides Inside Look at the Real-World Use of Generative AI in Higher Ed

Key Takeaway: Computer Science students account for a staggering 36.8% of Claude conversations, far outpacing their 5.4% share of U.S. degrees, as students across disciplines increasingly turn to AI to create, analyze, and solve academic tasks. The findings suggest that STEM students are not just early adopters but also power users, signaling a broader shift in how AI tools are reshaping technical education.

Why It Matters: This usage pattern reveals both the transformative promise and challenges of AI in education, as students increasingly delegate higher-order thinking to AI systems, prompting urgent questions about assessment, skill development, and academic integrity. Institutions must now confront the reality that traditional learning and evaluation models may no longer be equipped to measure what students know, how they learn, or where the work truly comes from.

Source: Anthropic Education Report

AI’s $20 Trillion (Yes, Trillion) Disruption of the Professional Services Industry

Key Takeaway: A new generation of AI-native firms is emerging to challenge the dominance of traditional professional service giants. Built on transparency, performance, and scale, these upstarts are reshaping client expectations and redefining the economics of expertise.

Why It Matters: This shift unlocks high-value services for a broader market while threatening to make brand prestige and billable hours relics of the past. AI-native firms are not just competing on cost, they’re competing on outcomes.

Source: Ethan Batraski (Partner at Venrock)

AI at Work Brings Speed and Risk in Equal Measure

Key Takeaway: As workplace chatbot use surges, cybersecurity experts warn that employees may expose sensitive data without realizing it. Companies like Samsung and Apple are already tightening controls, while others are building secure, internal alternatives.

Why It Matters: Generative AI is easy to adopt but hard to govern. Without training and oversight, well-meaning employees can create new vulnerabilities faster than security teams can close them.

Source: WSJ: Tech News Briefing [podcast]

Senators Target Big Tech’s AI Deals in New Antitrust Push

Key Takeaway: Democratic senators are pressing Microsoft and Google over whether their investments in OpenAI and Anthropic give them undue influence over the startups’ operations and unfair advantages in the cloud market.

Why It Matters: The scrutiny signals rising political momentum around reining in the concentration of power in AI, especially where investment ties could shape market access and innovation. The FTC and DOJ are watching closely, and structural remedies may follow.

Source: The Information [paywall]

Want to chat about AI, work, and where it’s all headed? Let’s connect. Find me on LinkedIn and drop me a message.