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IEEE Spectrum publishes a sponsored article by Emerson Test & Measurement president Ritu Favre arguing that modern engineering's defining challenge is verifying complex semiconductor and software-defined systems, not just designing them.
Intelligence analysis by Llama
Emerson's Ritu Favre argues engineering has entered a new era of test, where AI-augmented verification of complex systems, from chiplet-based semiconductors to sensor-rich vehicles, matters more than the initial design itself.
Imagine building a robot with parts from different toy makers. Each part works great on its own, but when you snap them together, something weird might happen. Engineers now spend more time checking that all the parts play nicely than building them, and AI helps speed up that checking.
Analysis
Chiplet-based designs
The article uses chiplet-based semiconductor architectures as its central example of rising engineering complexity. A chiplet from one supplier, an interposer from another, and a packaging process from a third may all perform perfectly on their own, yet the assembly can produce unexpected behavior, according to Favre. This shift from monolithic to multi-vendor heterogeneous integration is reshaping the test challenge from a per-component question into a system-level verification problem. For robotics and other complex automated systems built on similar heterogeneous hardware stacks, the implication is direct: the most consequential failures often emerge at the seams between subsystems, not within any single chip.
Emerson
The piece is published as a sponsored article brought to readers by Emerson, where Favre serves as president of the Test & Measurement business group. The framing positions Emerson's commercial interest in selling test instruments and platforms, but the editorial argument is broader: the entire engineering community must reorient around verification. By spotlighting chiplets, sensor-rich vehicles, and turbulent-aircraft flight control as proof points, Favre ties Emerson's product strategy to a sector-wide claim about where engineering investment should flow next. Readers following robotics should note that test-and-measurement vendors are watching the same complexity curve that autonomous-system developers are trying to climb.
Ritu Favre
Favre's quoted formulation, "The future of engineering will be defined by who can verify, understand, and improve complex systems fast enough to safely keep innovation moving forward," is the article's load-bearing thesis. She argues that test can no longer be a final checkpoint before release but must become a continuous activity threaded through design. The shift she describes, from validating finished products to continuously verifying software-defined systems, parallels the move in robotics from pre-deployment testing toward online monitoring and post-deployment validation. The article does not cite specific benchmarks, timelines, or named customer deployments, so the claims should be read as an industry framing rather than a measured technical report.
Key points
- Sponsored article authored by Emerson Test & Measurement president Ritu Favre, published on IEEE Spectrum
- Argues engineering has entered a 'new era of test' where verification, not design, is the primary bottleneck
- Uses chiplet-based semiconductor designs as the central illustration of system-level complexity from heterogeneous suppliers
- Frames modern cars, aircraft, and other software-defined products as test challenges because of thousands of sensor interactions per second
- Positions AI as the tool that will supercharge test and measurement workflows in this new era
If AI-augmented test platforms deliver as Favre frames them, engineering teams could verify complex multi-vendor systems faster, reducing the time and cost of validating new chips, vehicles, and other safety-critical products. The article envisions a continuous, integrated verification loop that keeps pace with innovation rather than gating it.
The article offers no quantitative evidence of measurable improvements, and the "new era of test" framing is driven by a test-instrument vendor with a commercial stake in selling that narrative. If verification complexity outpaces AI's ability to model real-world interactions, the gap between test-passed and field-robust systems could widen, especially in safety-critical domains like autonomous driving and flight control.
