Enterprises pouring money into GenAI and CEOs treating AI agents like cheap labor - yet only 25% see ROI right now. Vibes: “Europe’s long holiday from history is over.” Also: IBM does RTO, predictions about DOGE layoffs, the term “platform” remains a favorite excuse for overcomplicated tech, and “autonomous killer robots.”

AI comes for HR

What to make of using AI to automate HR processes? Melody Brue and Patrick Moorhead look at Oracle’s work there:

The agents are designed to support several key facets of the employee experience, including hiring, onboarding, career planning, performance reviews and the management of compensation and benefits.

Yes, and…

(1) If it’s bullshit work (“busy work”), eliminate it, don’t automate it. The thinking here promises to automate bullshit work like manually formatting performance reviews, copy/pasting boilerplate onboarding checklists, clicking through timecard approvals, writing job descriptions from scratch, and filling out endless HR forms. Yes, and…are these tasks that should probably just be eliminated or drastically simplified rather than lovingly preserved in AI amber. I’ve written job descriptions several times and there is something wrong-feeling about the process and the results. The same with performance reviews from both sides of the review. If you feel like you’re doing bullshit work and you get excited about automating it with AI, why not eliminate it instead? Or, you know, fix it.

(2) How could workers use similar AI stuff to maximize their advantage versus management? In a heavily bureaucratic HR system, reports and analysis are important: you need to prove that you deserve a promotion, more money, whatever. You’re often weighed against relative metrics: how much do people get paid in a region, how did you perform versus other people on a bell curve (or ranking), etc. Putting together those reports is tedious and your managers may not put in the effort. Have the AI do it for you. You could also look at those wordy job descriptions to extract what your role is responsible for doing. And when you need to come up with annual MBO/KPI/OKR/whatever the next TLA is for “goals,” have the AI look at the goals-trickle down and come up with yours. Then have it track what you should be doing. Negotiating salary could be useful to: how much should you even be asking for, what is your BATNA? What is their BATNA?

(3) Could you run the robot on, say, the last 5 years of reviews and then compare it to what the human evaluators did? Is the robot better (less bias, giving feedback that improves worker performance, finds low performers, etc.), or is it worse (wrong analysis leads to less performant workforce)? As a worker, thought you might not actually have access to full reports, you could try to find out what the real performance measures are. Load in job descriptions, give an overview of what highly rewarded people did, and then see what attributes and actions get rewarded. Never mind what the official metrics are, target those.

There’s a general theory for all AI use here as well: if what your AI produces is something that can just be consumed and used by another AI, it’s probably bullshit work that you can reduce to a quick email or can be eliminated entirely.

***

For him, of course, it was a business opportunity. He was part of what I would come to see as a savvy minority of people and companies capitalizing on AI fatigue.

Meanwhile, this is a fantastic piece on the state of HR tech from the worker’s perspective. There’s plenty of AI talk in it. It’s also fun to see what tech conferences and marketing looks like to (I presume) outside eyes. We are such dorks and, often, tasteless:

While the word people was plastered everywhere as both a noun and an adjective, the workers of the exhibit hall's collective imagination were not real, three-dimensional people. They were shadows without substantive interests or worries beyond the success of their companies. That was the only way these products could be pitched as win-wins. But, come on. We were in Las Vegas - everyone here knew the real money comes from making sure enough people are losing.

Fresh Podcasts

There are new episodes of two of my podcasts, listen to ‘em!

• Software Defined Interviews #94: Adding more condiments to the 7 layer networking burrito, with Marino Wijay - Why do we keep adding new layers and frameworks instead of just fixing the ones we have? They also talk about the challenges of platform engineering, the importance of empathy in tech, the difficulties of integrating multiple layers in tech stacks, the essential role of effective communication and prioritization, and EmpathyOps.

• Software Defined Talk #507: Battery of Potential - This week, we discuss how banks beat PayPal with Zelle, what the Wiz survey says about AI usage, and whether you can really “disagree and commit.” Plus, are multitools actually useful?

Relative to your interests

• AI Agents: Why Workflows Are the LLM Use Case to Watch - The agentic app revolution isn’t a transformation story. It’s a modernization story; a chance to solve small problems with the team you already have.

• AI Agents and the CEOs - “At the risk of saying the quiet part out loud, the way CEOs are talking about agents sure sounds like how they talk about employees–only cheaper!” // “Companies are dedicating significant spend to AI–approximately 5% of the revenue of large enterprises (revenues over $500 million) according to one survey by Boston Consulting Group, and yet only 25% claim they are seeing value from their AI investment.”

• To avoid being replaced by LLMs, do what they can’t.

• Learning from examples: AI assistance can enhance rather than hinder skill development - Could be that AI use makes you better. // “Decades before the advent of generative AI, the legendary UCLA baseball coach John Wooden declared that the four laws of learning are explanation, demonstration, imitation, and repetition (31). Few learners have access to the best human teachers, coaches, and mentors, but generative AI now makes it possible to learn from personalized, just-in-time demonstrations tailored to any domain. In doing so, AI has the potential not only to boost productivity but also to democratize opportunities to build human capital at scale.” // Also, some prompts used to evaluate writing quality. The one rating “easy responding” is interesting: how easy is it to (know how to) respond? Maybe good for CTAs.

• Gartner Survey Reveals Over a Quarter of Marketing Organizations Have Limited or No Adoption of GenAI for Marketing Campaigns - ”Nearly half (47%) report a large benefit from adopting GenAI for evaluation and reporting in their campaigns.” // The number is reverse is more interesting: 77% of surveys marketing people say they’re using generative AI for marketing stuff. Related:

• OpenAI reaches 400M weekly active users, doubles enterprise customer base - “The ChatGPT developer currently has 2 million paying enterprise users, twice as many as in September.” With “400 million active weekly users, a 33% increase from December.” And: “The New York Times reported in September that the company was expecting to end 2024 with a $5 billion loss on sales of $3.7 billion.”

• 2025 is the breakthrough year for Generative Enterprise — and partnering with a capable services partner is critical - “[S]pending on GenAI is rising (HFS data suggests enterprise investment is rising by more than 25% on average into 2025), we start from a low base. We estimate enterprise spending on GenAI in 2024 accounted for less than 1% of global IT services spending. This is just one illustration of how far we still have to go.” // Plus, a whole bunch of commentary in enterprise AI.

• Data is very valuable, just don’t ask leaders to measure it - AI ROI is difficult: “in a survey of chief data and analytics (D&A) officers, only 22 percent had defined, tracked, and communicated business impact metrics for the bulk of their data and analytics use cases… It is difficult, though: 30 percent of respondents say their top challenge is the inability to measure data, analytics and AI impact on business outcomes”

• A Simple Definition Of “Platform” - “a product that supports the creation and/or delivery of other products.”

• IBM co-location program described as worker attrition plan - From the RTO-as-not-so-stealthy-layoff files.

• YouTube (GOOGL) Plans Lower-Priced, Ad-Free Version of Paid Video Tier.

• On European Defence, Energy and Growth - Imagining big changes in European priorities: changing policy to get more energy, more emphasis on militaries.

• No Rules Are Implicit Rules - The European view on enlightened American management policy: “Greg, I hate to bring it to you, but working for ten fucking hours a day is not the normal hour. I don’t care if you live in America or not. The section continues with other “grand” examples of managers taking “up to” 14 days a year off to show their employees they should to so too. Let’s assume the best here: 14 workdays are almost three weeks. A year. The statutory minimum for full-time employees working a forty-hour week is 20 (thus 4 weeks) in Belgium. Oops.”

• Rage Against the Machine - Perceptive: “They’re going to try two or three things they think will solve everything, which will be thrown out in court. I assume the first thing they’ll do is some kind of hiring freeze, and then, after three months, they’ll realize agencies have started to figure out ways to get around it. And then they’ll try to stop that, and they won’t be able to do that. Then they’ll try to make people come to work five days a week, and that’s going to be difficult because a lot of these agencies don’t have offices for these people anymore. I think it’s going to be one thing after another, and maybe after four years the number of employees will be down 2 percent—maybe.” // The layoff playbook DOGE is working comes from the tech world, and it sort of works there. But that’s because tech companies can die, be acquired, or be reborn. In a tech company, you rarely starve the beast (or amputate parts of it) and have it survive. Do we want the same outcomes with government?

Read by the robot

I don’t read everything, sometimes I have the robot read it for me. Beware that the robot sometimes makes things up. Summaries are for entertainment purposes only.

Kelsey Hightower declined to join the AI gold rush, advocating instead for a glossary of tech jargon to remind everyone that AI is not new, just rebranded.

Platform engineering teetered between breakthrough and bust, with some heralding it as the savior of DevOps while others braced for its descent into Gartner’s “trough of disillusionment.” Several years ago (February, 2023) Sam Newman insisted that calling something a “platform” is often just an excuse to overcomplicate things, suggesting “Delivery Enablement” as a rebrand.

Meanwhile, IBM Consulting offered enterprises a guided tour of “Agentic AI,” a term that likely needs its own entry in Hightower’s proposed glossary.

Wastebook

• “effortful,” AI study.

• “Topological qubits,” MSFT.

• “Deliberately they don’t give a shit,” Emily, Political Gabfest, February 20th, 2025.

• And: “chaos entrepreneur,” John.

• “Europe’s long holiday from history is over,” John Naughton.

• "This [Trump] administration cares about weapon systems and business systems and not ‘technologies. We're not going to be investing in ‘artificial intelligence’ because I don’t know what that means. We're going to invest in autonomous killer robots." Fund the outcomes, not the tech.

Conferences

Events I’ll either be speaking at or just attending.

VMUG NL, Den Bosch, March 12th, speaking. SREday London, March 27th to 28th, speaking. Monki Gras, London, March 27th to 28th, speaking. CF Day US, Palo Alto, CA, May 14th. NDC Oslo, May 21st to 23rd, speaking.

Discounts: 10% off SREDay London with the code LDN10.

Logoff

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