Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I am Peter Wang, based in Shanghai, with dual master’s degrees in engineering and business management from Fudan University and the National University of Singapore.

Over the years, I have worked across sales and marketing roles with companies like Keyence, Datalogic, Teledyne E2V Semiconductors, and Spectris Servomex. That exposure has given me a hands-on understanding of multiple industrial segments, including machine vision, industrial automation, semiconductors, spectroscopy, and gas analysis. My perspective is shaped largely by working at the intersection of technology and market adoption across these industries.

 

Q2. Are you seeing a pivot in ASU demand toward Hydrogen purification? How much of the 2026 pipeline is driven by 'Green Hydrogen' mandates versus traditional semiconductor demand?

I do see ASUs increasingly being integrated into green hydrogen projects, particularly across Asia-Pacific. But I would not call this a fundamental shift in demand yet.

In my experience, the more reliable way to read this market is by tracking how the large global gas players—Linde, Air Liquide, and Air Products—are allocating capital. They still anchor the market, both for semiconductor specialty gases and broader industrial gases.

Semiconductors continue to drive steady demand for gases like nitrogen, helium, hydrogen, and argon. Nitrogen remains non-negotiable across processes because of its purity requirements and continuous usage profile.

At the same time, traditional industrial demand—especially oxygen consumption in steel and chemicals—continues to form the base load for ASUs. That demand hasn’t gone anywhere. So, what we’re seeing is incremental growth from hydrogen, not a replacement cycle.

 

Q3. In industrial machine vision, is the shift from 2D to 3D (LiDAR-integrated) sensors happening fast enough to justify the 2026–2027 revenue forecasts, or is it still a 'pilot-phase' technology?

From what I see on the ground, this is still very much in a pilot phase.

LiDAR is proving useful, but only in specific scenarios—AGV and AMR navigation being the most obvious ones. It’s not replacing 2D vision systems across the board.

3D vision itself is evolving through multiple approaches—structured light, time-of-flight, and laser triangulation. Among these, laser triangulation is where I currently see the strongest commercial traction.

So the shift is real, but it’s selective. I would be cautious about assuming broad-based adoption in the near term.

 

Q4. Are you seeing a demand surge for non-ITAR imaging solutions specifically for high-altitude Earth observation? If not, which other segment is picking up?

I wouldn’t describe it as a surge. What I see is steady, structural growth rather than any sharp spike.

The more interesting shift, in my view, is happening elsewhere. Demand is moving toward:

• Commercial communication payloads
• AI-driven Earth observation data services
• Small satellite constellations

These segments are scaling faster and attracting more consistent investment compared to high-altitude imaging alone.

 

Q5. In the 2026 drone market, is the high-value 'moat' in the sensor resolution (MP) or in the on-chip AI processing for real-time object recognition?

This has clearly evolved over time. Earlier, the focus was heavily on sensor resolution. Today, that’s just the baseline.

What I see now is that the real differentiation sits at a system level.

• Sensor resolution is the entry ticket
• AI edge computing is where the differentiation happens

But even that is not sufficient in isolation. The companies that are pulling ahead are the ones that combine:

• High-resolution sensors
• Edge AI capabilities
• Application-specific domain understanding

That combination allows them to build what I would call software-defined payloads—where the same hardware can adapt across use cases like inspection, mapping, or surveillance.

By 2026, competition is no longer about image clarity alone. It’s about how intelligently the system can interpret and act on that data in real time.

 

Q6. As we move to more advanced chips in 2026, what is the most critical 'Ground-Level' purity bottleneck you're seeing?

At the ground level, purity is becoming a much tighter constraint than most people outside the industry realise.

From what I’ve seen, the biggest challenge is maintaining ultra-high purity in gases like hydrogen, nitrogen, and argon. At advanced nodes, even trace levels of moisture or hydrocarbons can directly impact yield.

Beyond gases, process chemicals are becoming equally critical. Photoresists and cleaning agents now need far tighter control because device architectures are far more sensitive than before.

What this means in practice is:

• More advanced filtration systems
• Real-time monitoring, not just batch validation
• Much tighter process control overall

The margin for error is shrinking, and the tolerance levels are far less forgiving than they used to be.

 

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

I would focus on three areas, based on what I’ve seen create long-term differentiation:

Sustainability of the technology moat

I would want to understand the physical limits of their purification or filtration capabilities, and whether they have real-time, in-situ monitoring or are still dependent on offline validation.

Supply chain resilience

Given how sensitive these industries are, I would look closely at how they ensure continuity of high-purity materials without over-reliance on a single region or supplier.

Shift in business model

This is becoming increasingly important. Are they still just supplying products, or are they moving toward data-driven services?

For example, are they using real-time purity data to offer predictive maintenance or process optimisation?

In my experience, the companies that think beyond product delivery and start building data-linked value layers are the ones that tend to stay ahead.