“I’m seeing interesting developments in optical computing: Implementations of neural networks, neuromorphic computing, nonlinear models using optical systems, etc. Another interesting trend is in the area of integrated photonics: Chip-based optics platforms are becoming increasingly reliable, which moves them closer to applications. And there is movement in the area of optical detectors: Event-based cameras that effectively do calculations and transmit data for individual pixels rather than full frames. These are useful for LiDAR, fast imaging, and will enable all sorts of new imaging modalities.”—Stefan Witte
“AI has been in all the news, and photonics AI is a hot topic. Recently, we started to see quantum optical computing take off, blending quantum with photonic AI. This promises fast, energy-efficient machines for solving complex problems. Another is the use of topology to overcome the problem of noise in quantum systems. From solid-state topological qubits to photonics topological qubits and audits (high dimensional states), this field is exploding in activity and looks very exciting.—Andrew Forbes
“While optimization has long been the ‘holy grail’ for quantum computing and other emerging high-performance computing (HPC) platforms, we’re now seeing the market shift toward solving partial differential equations (PDEs). PDEs sit at the core of many scientific and engineering simulations, and they’re notoriously time- and energy-intensive. This is a multibillion-dollar field that largely depends on digital software to model complex physical behavior. A new wave of physics-based processors is changing this. By using natural media such as light or even chemistry to mirror how physical processes unfold, they can run these simulations natively and far more efficiently. A growing research community is rallying around these approaches, contributing to what we see as the ‘heterogeneous future of HPC,’ where specialized processors tackle the tasks they’re best suited for rather than relying solely on general-purpose hardware.”—Ruti Ben-Shlomi
“One emerging trend is the growing use of AI to enhance both data collection and analysis. From adaptive measurement strategies to real-time pattern recognition, AI is accelerating discovery across photonics and quantum research.”—Jean-Michel Ménard
“An interesting trend we’re observing, particularly in the defense optics industry, is the shift in R&D funding. For the past two decades, significant U.S. government funding from the DoD, NSF, NIST, and DOE has supported steady, incremental, but sometimes slow development in optics and photonics. Optics continue to be critically important for defense and four of the Pentagon’s six critical technology areas rely on optics or photonics technologies. The U.S. government is now shifting from funding incremental development toward procuring already-developed products with accelerated delivery schedules. This change requires optical systems companies to adapt by increasing their own innovations through internal R&D. We may see an increase in industry-funded applied research creating opportunities for agile manufacturers to break into and disrupt the defense market, but there is concern what this change might mean for fundamental research.”—Justin Sigley
“One trend I’ve noticed is the strengthening connection between academia and industry, with real momentum behind turning research prototypes into scalable, manufacturable products. Many papers this year emphasize miniaturization, disruptive approaches, scalability, and cost reduction. We’re also in a highly exploratory phase, with multiple competing technologies developing in parallel—and this diversity is part of what makes the field so exciting.”—Mohan Wang
“We’ve heard from a number of sources that defense spending and the need for dual-use photonics technology (civilian and military) are key areas of growth in the industry, and these dual-purpose technologies often enable faster innovation cycles and lower political barriers. We’re also hopeful for Q4 tailwinds in photonics applications for pharma/biotech and precision medicine that will encourage recovery to prior levels. One particular area of interest for Hamamatsu is spatial omics.”—James Butler
“AI is becoming increasingly powerful, especially in solving mathematical and theoretical problems. I’m a theorist, and derivations that previously took me more than a week can now be completed within an hour with AI’s assistance—as long as I guide it well. I believe AI will significantly enhance productivity, but it will also intensify competition. Even those who may not be naturally strong in theory can now produce high-quality theoretical work using AI.”—Xuchen Wang
“I’m always amazed by the breadth and depth of applications of lasers and photonics more broadly. I think the most interesting development during the past year was the explosion of interest in hardware for AI, and photonics is set to feature very prominently in this—particularly photonic integration and PICs.”—Stephen Sweeney
Is there anything you’re concerned about for the industry as we head into 2026?
“My main concern is a simple one: We’re rapidly developing incredibly powerful tools—from artificial intelligence to quantum computing—but are we fully prepared to use them responsibly? Are industrial regulations, ethical frameworks, and safety standards keeping pace with the speed of innovation?—Mohan Wang
“My biggest concern is the uncertainty in the world economy, and the increasing protectionism and trade blockades. Even though the need for optics is only growing, I see industry becoming hesitant to take risks and invest, which has a negative effect on innovation and job opportunities for recent tech graduates.”—Stefan Witte
“Like many of my peers, I’m increasingly concerned about the widening gap between rapidly growing AI workloads and the energy needed to support them. As an industry, we need to develop hardware that’s fundamentally better suited to these demands. But bringing new hardware from concept to commercial reality requires significant investment and close collaboration across innovators, academia, and established industry partners. We also need strong national and regional initiatives to ensure hardware progress keeps pace with advances in AI—because without that foundation, the entire ecosystem will struggle to scale sustainably.”—Ruti Ben-Shlomi
“AI offers incredible opportunities, but it’s also creating uncertainty for those entering the engineering workforce. I frequently hear questions like ‘What should I major in?’ or ‘Will AI take my job?’ If this uncertainty discourages students from pursuing STEM, it could slow the progress the industry has made in recent years.”—Andrea Martin Armani
“The balance between the rate at which we graduate quantum students and the rate at which they are absorbed into the workforce is not yet in a steady state, so it’s hard to gauge if we are producing too many or too few. The rapid rise in startups has created huge demand on talent, but one does worry that this could be a bubble that is soon to burst. Big investments usually demand big returns, and it looks like the payoff may be some years away still.”—Andrew Forbes
“I’m worried about there being a decline in foreign national students. These students make up a large share of graduate enrollments in the U.S. and contribute significantly to laboratory productivity, publication output, and the talent pipeline that feeds academia, national labs, and industry. With fewer international researchers, many programs may face difficulty maintaining sustaining experimental efforts. Over time, this trend may reduce the United States’ capacity to remain competitive in emerging STEM fields, unless offset by expanded domestic recruitment or policy changes that support international participation.”—Tara Fortier
“Boom and bust…having witnessed this in the telecom bubble of the early 2000s, I would say there is caution about the latest boom in AI and hopefully we won’t see a repeat of the telecom bubble bursting.”—Stephen Sweeney
“If the current AI surge turns out to be a bubble, its collapse could trigger broader economic consequences—potentially affecting investment and momentum in adjacent fields like photonics and quantum technologies.”—Jean-Michel Ménard