Robert Charette Leaves IEEE Spectrum After 20 Years of Risk Writing

Robert N. Charette, an IEEE Life Senior Member and self-described risk ecologist, has ended his two-decade run as a contributing editor at IEEE Spectrum. Known for his Risk Factor blog, which ran for more than 10 years and topped 1,750 posts, Charette chronicled software failures, EV adoption challenges, automation dangers, and the mythology behind the so-called STEM crisis. He is now stepping back to focus on nature photography and a set of fictional trilogies, leaving behind a body of work still taught in university engineering courses.

What happened

Detail	Fact
Years at IEEE Spectrum	20+
Risk Factor blog posts	1,750+
Blog lifespan	More than 10 years
Award won	Jesse H. Neal Award for Best Infographics, 2016
Award-winning piece	“Lessons From a Decade of IT Failures”
Most impactful article (per Charette)	“The STEM Crisis Is a Myth” (2013)
Next chapter	Nature photography and fictional trilogies

Robert N. Charette joined IEEE Spectrum as a contributor in 2005 when the publication invited him to write about why enterprise software projects fail. His first article, “Why Software Fails,” is still assigned in university engineering courses. That piece launched a 20-year working relationship with the site’s editorial team, eventually anchoring the Risk Factor blog, which became one of the publication’s longest-running regular features.

The Risk Factor blog ran for more than a decade, producing over 1,750 posts. Its culminating project, “Lessons From a Decade of IT Failures,” won a Jesse H. Neal Award for Best Infographics in 2016. In an ironic footnote, the infographics themselves were built in a software package that is no longer supported and have since been lost.

What did Charette consider his most important work?

When asked which article had the biggest impact, Charette pointed to a 2013 feature titled “The STEM Crisis Is a Myth.” According to Charette, IEEE Spectrum gave him a platform to challenge the widely held assumption that the economy needed more STEM graduates. He argued that much of the so-called crisis was a narrative perpetuated by employers and academic institutions, and that it had been uncritically accepted by the engineering community.

Beyond the blog, Charette produced several long-form projects, including a 12-part series and accompanying e-book called “The EV Transition Explained,” a piece called “The Doctor Will See Your Electronic Health Record Now,” and an article titled “Automated to Death,” which examined the automation paradox, the risk that humans become so reliant on automated systems that they lose the skills and situational awareness to intervene when those systems fail. That piece covered cyberphysical systems in aviation, rail, and automotive contexts.

Why it matters

Charette’s career was built on a single idea: software is everywhere, but almost no one can see it clearly. “Software is all around us, but we don’t recognize it at all,” he told IEEE Spectrum editors during a Friday conversation in July. “You can’t see software, you can’t touch it, you can’t taste it. You may feel the consequences of software failure, but you never see the reason itself.”

That framing is as relevant now as it was in 2005. The wave of AI integrations hitting businesses today carries exactly the same visibility problem. Teams adopt AI tools without fully understanding the assumptions baked into the models or the failure modes those assumptions create. Charette’s risk maxim applies directly: “Assumptions made are risks accepted.”

For anyone thinking about adding AI to their business workflows, that line is worth writing on the wall. Most AI failures are not dramatic crashes. They are quiet, assumption-shaped gaps between what a system was built to do and what a business actually needs.

Our take

Charette spent 20 years making a simple argument: name the risk before it names you. That is harder than it sounds when everyone around you is excited about a new system, a new platform, or a new model. His “STEM Crisis Is a Myth” piece is a good reminder that even expert communities can run on unchecked assumptions for years before someone does the uncomfortable work of checking the numbers.

The automation paradox he described in “Automated to Death” is now playing out across AI-assisted work. When a tool handles enough of a task, the human operator loses the fluency to catch the tool’s mistakes. That is not a reason to avoid automation, but it is a reason to keep humans genuinely in the loop rather than just nominally responsible. We think about this when we scope workflow automation projects for clients: the goal is augmentation with visibility, not delegation into a black box.

Charette also ran two management consultancies in parallel with his prolific writing output. The combination of practitioner and communicator is rare, and it showed in the quality of his reporting. Spectrum will be harder to replace than they may expect.

What to do about it

1. Read “Why Software Fails” if you are evaluating any significant technology project. It surfaces assumption categories that most project checklists skip entirely.
2. Before signing off on any new software or AI integration, write down your three biggest assumptions and assign each one a named owner who will track whether it holds.
3. Review Charette’s coverage of the automation paradox if your team is adopting AI tools for high-stakes tasks such as customer communication, financial reporting, or operations scheduling.
4. Follow the Lumien news feed for ongoing coverage of AI failures, risk patterns, and practical implementation lessons for business operators.

The most durable thing Charette leaves behind is a habit of thinking: before you accept a system’s output, ask what assumptions it was built on.

Source: IEEE Spectrum · AI