The Technology Executive

Teams across the market are smaller than they were two years ago and getting more done. Almost all of them have the same hole: nobody junior. That gap didn't start with AI. Long before generative AI, a lot of companies were already avoiding junior hires, because junior people are difficult and expensive to develop : years of mentoring, tolerance for mistakes, a willingness to pay someone to learn on the job. The youth unemployment numbers have been ugly for a while because of it; recent college graduates were already unemployed at roughly 5.6%, well above the 4.2% national rate , before you even get to AI. AI isn't creating that problem. It's making it worse, because the junior work, drafting the first pass, pulling the data, writing the routine ticket response, is exactly the work AI now does before a human touches it. The debate about AI and jobs keeps getting framed as one question: does AI take jobs, yes or no. That framing is already wrong, and the data coming out thi...

Open your feed this week and you'll find two competing panics: Today, its AI is about to take everyone's job and previously it was that companies suddenly need everyone back in the office. We had that second argument for three straight years before AI showed up. I want to set the headlines aside and look at this the way an economist would: what is a salary actually buying, and does AI change the answer. That's a more useful question than either panic, and it's the one that actually determines what happens to anyone's pay. You're Paying for Time, Outcomes, or Units. Pick One. Hourly pay is honest about what it is: you're buying a block of a person's time at a given skill level. There's no ambiguity to resolve when a tool makes that person faster. Fewer hours, less pay, full stop, assuming the work still gets done to standard. A salary is a different contract, whether anyone wrote it down this way or not. It's closer to fixed-price consultin...

Nobody Has Priced This Yet In my day-to-day, I speak to leaders at lots of consulting firms and we keep ending up in the same conversation: what do we do about AI and pricing? The problem is not that there is no answer. The problem is that there are too many, and every one of them assumes something that may not be true: that the consulting firm can actually manage the cost of AI in the first place. That is a big assumption for an industry where, by micro-segment, plenty of firms are a few bad months from closing their doors. Layer on the fact that clients want projects faster and cheaper while firms are trying to protect or grow margin, and you have a genuine standoff. AI does not resolve that tension. It just gives both sides a new variable to fight over. The Fee Nobody Wants to Admit Is a Rate Cut The first instinct I see is a technology fee, a separate line item for the AI tooling. I dislike it the most. It has the same feel as a baggage fee or a credit card surcharge: I am b...

What Soren Kaplan Gets Right, and Where I'd Push Back I read Soren Kaplan's recent Inc. column arguing that consulting disruption has finally arrived, that once AI can generate a strategic plan or synthesize market trends in seconds, the rules of what clients pay for change completely. He's right about the direction. After nearly three decades in this industry, and the last few years watching AI move through dozens of engagements alongside consulting partners of every size, I'd push back on the timeline, and on what's actually being disrupted. Buyer Expectations vs. Consulting Realities Procurement has been told, by vendors, by analysts, by their own boards, that AI should be cutting consulting costs dramatically by now. Consulting firms have been told to adopt AI or get disrupted. The savings clients budgeted for still haven't materialized at the scale they were promised, and that's the gap: procurement asking where the savings are, delivery explainin...

I get some version of this question at least once a quarter, usually from a CIO who just sat through a brutal renewal negotiation or is frustrated by the cost of an upgrade: "Was moving to the cloud a mistake?" The honest answer is that most of the people asking it aren't actually asking about the cloud. They're asking about something else entirely, and until they separate the two, they're going to keep making the same expensive decision over and over. The Cloud Is a Landlord, Not a Product Let's start with what "moving to the cloud" actually means, because the term has been stretched past usefulness. At its core, it's moving your workloads into data centers that aren't yours, typically a hyperscaler's. You're renting infrastructure: compute, storage, networking, physical security, and a deeper bench of specialized expertise than almost any single enterprise IT shop can staff internally. That's a legitimate, often excellent, ...

I've sat in enough boardrooms this year to notice a pattern: every CIO has an opinion on whether SaaS is dying, and none of them agree on where the line is. The loudest voices on social media have already written the obituary. I think they're half right, and the half they're wrong about is the half that matters for what you do next quarter. The Pundits Have the Diagnosis Right, the Prognosis Wrong The claim isn't crazy. Gartner projects that 40% of enterprise applications will carry task-specific AI agents by the end of 2026 , up from under 5% a year earlier. That is a real, fast shift, and it is already showing up in how vendors price and package their products. But "pressure" and "death" are different diagnoses, and the pundits keep reaching for the second one because it's the more shareable headline. IDC put it more carefully : SaaS isn't dying, it's metamorphosing into outcome-based and usage-based economics. That's the disti...

  Why employees are bypassing IT, vendors are missing the point, and the integration problem is costing you more than just money I manage five email accounts across personal and professional domains, yet my AI assistant can connect to exactly one and only when enterprise security policies permit it. Calendar integration works intermittently, memory disappears between sessions, and token consumption burns through budgets faster than I can track. Every few weeks, a new tool launches with precisely the capability I've been waiting for, forcing another cycle of integration rebuilding and workflow retraining. This is the reality of AI agents in 2026, not the autonomous assistants orchestrating our workloads, not the context-aware systems maintaining our preferences, not the seamless integration promised in vendor demonstrations. The technology isn't the problem. Organizations are trying to integrate intelligent agents into architectures designed for simpler systems, governance mo...