Tag-Teaming Engineers Week with My Narcissistic AI Girlfriend “Perplexity”

Two places at once

Is where I gotta be now

AI, don't fail me

It’s no secret that AI is here and that all of us, as engineers, are struggling to find ways to exploit the productivity gains it brings to our jobs while dealing with its severe shortcomings. LLM-based AI really boils down to a string of words that are most likely applicable to a prompt sequence.

I’m attending DesignCon to bring you some of the latest and greatest tech and insights, and to cover the Analog Aficionados get-together. I crashed at an Airbnb in South San Jose, got some other work done, including getting the Automotive Electronics Newsletter deployed yesterday, and an hour later, went to the Santa Clara Convention Center to start covering the first day of the conference.

Meanwhile, it’s Engineers Week, which is an annual commemoration of who we are and what we do — a time that lets us share our profession with others in our communities. We’re also in some strange times, where LLM-based AI is starting to produce some believable content and is threatening our very professional existence.

This is the case whether you’re a new grad looking to land that cool entry-level job that will define the rest of your life, or a multi-decade specialist in a particular field, such as analog or mixed signal design, or as a guru creating 200-Gb/s links “for AI,” as the marketers will paint it at DesignCon 2026. 

So, with me needing to do two things at once, I’m keeping the interesting stuff for myself (attending DesignCon and writing this special edition blog introduction), and letting Perplexity write about Engineering Week as my supervised intern versus as a trusted colleague. One of my favorite quotes that I’ve used before about AI on Electronic Design:

 “I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes” — Joanna Maciejewska, author and videogame enthusiast

To balance the AI that we all hate to love and love to hate, I’m also including a roundtable discussion my editor colleagues and I had recently (I was sick in bed when we were doing the Zoom call, so apologies for the neckbeard and for coming across as more grumpy than usual, lol) on how us meatbags can survive this threat to our careers, whether new grad or seasoned pro.

Huge shoutout to our video guru, Marie Darty, for the edits she made to create a rather engaging YouTube video of us Electronic Design editors and to Roger Engelke for copy editing my and Perplexity’s mashup of words here.

Enjoy,

-AndyT

p.s. For the time it’s taken me to proof, correct, add links into, and supplement the AI’s output, I could have written it from scratch in about as much time. Note that I’ve embedded editor snark in the following AI piece for your enjoyment and to lend perspective. Not to worry — I'm here in this gig to write and motivate other SMEs to write for our readers; not be an AI jockey. As the bumper sticker says, “My other job is design engineer.”

Engineers Week 2026: Transforming the Future of Electronic Design in the Age of AI

From the devices in our pockets to the data centers driving generative AI, electronics and embedded systems define the modern world. Engineers Week 2026, organized by DiscoverE and celebrated February 22-28 under the theme “Transform Your Future,” puts a spotlight on the people who make this possible and the students who may join them next.

For electronic design professionals, it’s also a timely moment to examine how artificial intelligence is reshaping design workflows, and why staying ahead of that curve hinges on continuous learning.

Engineers Week in Classrooms, Communities, and Labs

Across K-12 classrooms, Engineers Week is increasingly hands-on and electronics-centric. Students are building simple sensor projects with microcontrollers, experimenting with LED matrices, or using basic robotics kits to understand control systems and feedback. Outreach programs such as DiscoverE's Future City challenge ask middle- and high-school students to design sustainable, technology-enabled cities, often incorporating renewable power electronics, smart grids, and embedded intelligence.

Community events and corporate outreach bring these ideas closer to real engineering practice. Design teams at semiconductor, EDA, and electronics companies host lab tours and demonstrations, showing students PCB layout stations, hardware-in-the-loop testbenches, and mixed-signal evaluation boards instead of abstract “engineering” concepts.

Local chapters of IEEE and other professional societies layer in panel discussions on topics such as power electronics for EVs, RF front-end design for 5G/6G, and trustworthy AI in embedded systems. The common message: Engineering is creative, collaborative, and accessible to a much broader segment of students than many assume.

Within universities, Engineers Week often coincides with design competitions and hackathons that resemble miniature versions of the workflows used in industry. Teams may prototype IoT nodes, build power converters for solar applications, or design small ASICs or FPGAs using modern EDA tools. These events are increasingly tied to real-world constraints — thermal limits, EMI/EMC concerns, safety margins, manufacturability — giving students a glimpse of what practicing electronic designers wrestle with every day.

How AI is Changing Electronic Design Work

While students are soldering boards and debugging firmware during Engineers Week, practicing engineers are confronting a different challenge: Integrating AI deeply and safely into their workflows. AI and machine learning have moved from experimental add-ons to mainstream features in EDA suites, SPICE and mixed-signal tools, and even requirements and verification platforms.

In PCB and IC design, AI-assisted tools can:

• Propose placement and routing strategies to improve power, performance, and area.
• Cluster and prioritize DRC violations so that layout engineers are able to focus on the highest-impact fixes first.
• Suggest layout changes to reduce EMI or improve signal integrity, drawing on patterns learned from large design datasets.

At the chip level, new AI-powered EDA systems, such as Siemens’ recently announced EDA AI System, use a combination of generative and reinforcement learning models to accelerate everything from RTL-to-GDS flows to physical verification. These platforms can orchestrate flows from natural-language prompts, explore design tradeoffs, and reportedly deliver order-of-magnitude speedups in design throughput by automating repetitive exploration and analysis tasks.

For board- and system-level designers, AI-driven circuit-analysis and optimization tools are emerging that can:

• Suggest component value changes to improve stability or efficiency.
• Flag potential reliability concerns based on historical field-failure patterns.
• Generate alternative topologies for power stages or filter networks under specified constraints.

Despite these advances, AI isn’t replacing electronic designers. Instead, it’s shifting the center of gravity from manual iteration toward higher-level architectural decisions and critical review.

Engineers still define specifications, safety margins, and acceptable risk. They still decide when an AI-generated layout is manufacturable, when a suggested topology is robust, and how to interpret results that may be statistically correct yet practically unsuitable. In other words, AI is becoming a powerful co-designer, but human expertise remains the arbiter of what ships [Perplexity is so condescending —she can’t think, can’t reason, has zero logic or association capability, and stalks my writings incessantly, yet elevates herself to being a human equal. I don’t think so, robo-babe...].

Lifelong Learning as an Engineering Deliverable

This shift in tools raises the bar for what “up to date” means in electronic design. It’s no longer enough to be fluent only in a particular microcontroller family, RF band, or power topology. Engineers increasingly need working literacy in data and models: how AI algorithms are trained; their failure modes; and where they’re most and least reliable.

Universities and continuing education programs are responding. Short courses and certificates focused on “AI for EDA,” “machine learning for embedded systems,” and “data-driven verification” are now common in engineering-focused professional development catalogs. Many emphasize practical skills: building quick ML models for anomaly detection on sensor data, interpreting AI-driven DRC clustering, or using generative tools to explore architectural alternatives without losing sight of constraints like thermal budgets and BOM cost.

Professional societies and vendors are filling gaps as well. EDA providers publish application notes, webinars, and training content explaining how to integrate AI features into existing flows safely and effectively. IEEE and other organizations offer courses on AI ethics, bias, and accountability — topics that matter when algorithms prioritize design fixes, estimate lifetime reliability, or help select components in safety-critical systems [a sales pitch is built into any AI output I’ve played with; it never disses itself or its creators, or jeopardizes its existence or funding].

For working engineers, these resources aren’t academic extras. Rather, they’re rapidly becoming part of the core toolkit needed to remain effective and employable.

Staying One Step Ahead of Your Tools

In the broader public conversation, AI is often framed [by AI’s progenitors like Sam Altman] as a competitor to human professionals. Within electronic design, a more productive framing is emerging: The best safeguard against being displaced by automation is to own the automation. Engineers who understand how to direct, constrain, and audit AI-based tools add value both as designers and as internal “AI translators” for their teams.

[The tool is also narcissistic and sees us “bags of mostly water” as “auditors” versus being the correctors of AI’s hallucinations and misinformation. Much of that can slip through the cracks when we don’t apply the diligence to check every detail because we should be in the car driving up to the Convention Center].

Consider a board designer working with an AI-augmented layout tool. The system may recommend an aggressive high-density interconnect stackup and tight differential-pair routing to hit signal-integrity targets at minimum layer count. The designer still has to weigh that suggestion against manufacturing capability, cost, rework risk, and field-serviceability.

They must also recognize when AI overfits to a narrow set of historical [aka “plagiarized” IP] designs and misses a corner case, such as a harsh EMI environment or a wide temperature swing in the field. Such judgments rely on domain knowledge that only comes from experience and ongoing education.

The same logic applies to IC design and verification. AI that accelerates timing closure or testbench generation is invaluable, but only when its outputs are framed by an engineer who understands the underlying physics, process corners, and business constraints. Without that context, teams risk “rubber-stamping” AI suggestions and pushing problems downstream to manufacturing or field support. With it, AI becomes a force multiplier that lets skilled engineers explore more options and converge faster on robust solutions.

Engineers Week as a Reminder — and a Challenge

Engineers Week’s 2026 theme, “Transform Your Future,” is aimed at students, teachers, and parents, but it carries a message for practicing engineers as well. The future of electronic design will be shaped not just by faster processes and denser nodes, but by how effectively engineers harness intelligent tools without surrendering their judgment.

For the next generation, Engineers Week provides a first exposure to the excitement of building something that works, whether that’s a blinking LED cube or a simple autonomous robot. For today’s professionals, it can be a prompt to audit their own skills:

• Do I understand the AI features in the tools I already use?
• Do I have a plan to keep my data and workflows secure as AI becomes more deeply integrated?
• Am I investing time in the courses, seminars, and reading needed to stay ahead of my tools, rather than behind them?

Electronic design has always required a blend of creativity, rigor, and lifelong learning. AI raises the stakes but doesn’t change that equation. If anything, it makes education itself a critical design parameter — an adjustable knob that determines how effectively human expertise and machine assistance combine.

As Engineers Week connects professionals, students, and communities around the common goal of building a better future, it also highlights a simple truth for our [“our” vs. “the”... nicely done, AI] industry: The most important “upgrade” in any AI-enabled design flow is still the engineer sitting at the keyboard. Continuous learning is how we transform not just our tools, but our own ability to use them, keeping us one step ahead of automation and firmly in control of the systems we create.

[With that, here’s Electronic Design’s editors’ take, created for Engineers Week 2026, on continuing education in this career minefield created by AI for us fleshy beings:]

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