$LSCC (+7,9 %) The semiconductor with software margins.

As AI moves from the data center to end devices, there is a need for power-efficient chips that can also perform precise actions in milliseconds. Lattice solves yet another problem: the security layer in the data center.

Lattice Sentry protects server firmware and the boot process. It checks whether software has been tampered with, can detect compromised firmware, and automatically restores a secure version. In addition, it uses modern post-quantum cryptography to ensure that systems remain protected against new types of attacks over the long term.

SCMP (Secure Control & Management Plane) goes one step further: It is intended to become an open industry standard that can automatically identify, authenticate, and manage thousands of servers, GPUs, and racks. Instead of configuring each piece of hardware individually and programming security features separately, operators will be able to roll out many processes in a standardized and automated manner in the future.

The investment thesis is therefore:

Lattice not only sells FPGAs but is positioning itself as a security, control, and management layer for future AI infrastructure.

✅ Pros

• Record revenue: $291 million, +118% YoY. Significantly above the expected ~$256 million. 🚀
• EPS skyrockets: Non-GAAP EPS of $0.72 vs. $0.28 in the prior year (+160%).
• AI data centers are clearly gaining momentum: FPGA revenue up 90% YoY; management explicitly cites accelerated AI server demand as the driver.
• Strong outlook: Q4 revenue of $325–345 million implies continued very high growth rates.
• Margins are rising despite growth: Non-GAAP gross margin of 70.9%, operating margin of 41.1%.
• Strong cash generation: $77.6 million in free cash flow for the quarter.
• Design wins, order backlog, and bookings are strong: Management expresses confidence through 2027 and beyond.
• The AMI acquisition appears to be paying off strategically: Lattice now has not only FPGA hardware, but also:
• rack management
• Firmware
• Security
• Data center control
• SCMP Initiative In collaboration with AMD, Arm, Intel, Microsoft, and NXP, the company is strengthening its position as a control and security layer for AI infrastructure.
• Post-quantum cryptography and Root-of-Trust products precisely address a future need.

❌ Cons

• AI Focus: Management itself points out that a portion of the demand comes from a few large customers.
• AMI integration still needs to be proven: So far, everything looks good, but major acquisitions always carry risks.
• Nearly $900 million in debt following the AMI acquisition.
• Assessment: Given these figures, hardly anyone will be able to claim that Lattice is a bargain.
• Cyclicality remains: Even though AI is currently dominating the headlines, Lattice remains, at the end of the day, part of the semiconductor industry.
• Strong Competition: In many areas, the company faces indirect competition from heavyweights like AMD, Intel, NXP, or specialized security providers.

Here $LSCC (+7,9 %) I had to take another bite 🐊 So now I have 49 shares in my portfolio.

In terms of forward revenue and forward EBITDA, Lattice ranks second in my portfolio. However, it’s priced accordingly at a premium. Despite being in the semiconductor sector, it’s projected to have software-like gross margins of 69%.

So far, my two posts have been about future AI bottlenecks on the topics of test & metrology and photonics. Both areas revolve around the question of how increasingly powerful AI infrastructure can still be controlled, connected and scaled.

However, another possible future infrastructure layer could be directly outside the data centers: Edge AI.

Today, the majority of modern AI still takes place centrally in large data centers. At the same time, more and more systems are being created to perform AI locally: Cameras, vehicles, drones, robots, sensors or industrial machines. Data often has to be processed there in real time. Not every request can be sent to the cloud first. As a result, AI is gradually moving closer to the physical world.

Edge AI describes precisely this development: AI inference runs locally on devices, machines or sensors instead of exclusively in the central data center. This creates a new infrastructure stack between cloud AI and the real world.

Level 1: Edge Compute & Embedded Control

Computing power also remains the foundation in the edge area. NVIDIA is increasingly addressing this market with Jetson platforms for robotics, industrial AI and autonomous systems.

I also think it is worth mentioning $LSCC (+7,9 %) (Lattice Semiconductor). While many AI systems prioritize maximum performance, Lattice focuses more on low-power embedded control, sensor fusion and flexible local data processing. It is precisely such efficient control systems that could become more important in the edge area than pure maximum performance.

Level 2: Power management & energy efficiency

As local AI grows, the importance of energy efficiency also increases. Edge systems often operate under tight energy and temperature limits. Many systems run permanently, mobile or autonomously.

This is precisely why power management could become even more critical in the long term than in traditional data centers. Companies such as $MPWR (+5,53 %) (Monolithic Power Systems) address this level with highly efficient power solutions for AI, automotive and embedded systems.

Level 3: Vision AI, sensor technology & perception

Many future edge systems will need to understand their environment locally. Cameras, machines, vehicles and autonomous systems increasingly require computer vision directly on the device itself.

$AMBA (+6,38 %) (Ambarella) is one of the most interesting pure plays in this area for me. Its strength lies primarily in energy-efficient local image processing and vision AI. This is exactly where the connection between AI and physical reality arises. I have also added Ambarella to my wikifolio "NextLimits" today.

Level 4: Storage, navigation & optionality

As autonomy increases, so do the requirements for memory, sensors and navigation. $MRAM (+9,81 %) (Everspin Technologies) is working with non-volatile memory architectures, which could become particularly interesting for robust and energy-efficient edge systems in the long term.

$INFQ (Infleqtion) is also expanding the topic to include quantum sensing and high-precision navigation technologies, for example for autonomous or GPS-independent systems. For me, these are not yet certain future winners. But possibly early infrastructure options for a world in which AI no longer only exists in data centers.

The exciting thing is that the next big AI shift could not just mean more compute. But the shift of AI from central cloud systems into machines, vehicles, sensors and robotics.

AKUT/ACTIVE:

HBM + Power & Cooling + Advanced Packaging + Energy/Grid

FUTURE/EMERGING:

Test & Metrology + Photonics + Edge AI

AI will not only need more computing power in the future. AI must increasingly function directly in the real world "out in the field".

On the graphic you can see Edge AI in context. At the bottom you can also see all the acute (4) and future (3) AI bottlenecks from my last posts.

Next, I will point out potential bottlenecks in other emerging fields independent of AI.

#wikifolio
#scalelimits

RBC Capital Markets rates nine semiconductor stocks as "Outperform", including Nvidia $NVDA (+3,8 %) Micron Technology $MU (+4,29 %) , Marvell Technology $MRVL (+5,79 %) Arm Holdings $ARM (+5,1 %) , Astera Labs $ALAB (+7,82 %) , ASML Holding $ASML (+5,23 %) , Applied Materials $AMAT (+5,41 %) , Lam Research $LRCX (+5,12 %) and Lattice Semiconductor $LSCC (+7,9 %) .

The bank justifies this with different exposure to AI infrastructure, memory markets and advanced semiconductor manufacturing.