AI Insights
Pixel 9a review: Google’s cut-price Android winner | Pixel
Google’s latest cut-price Pixel offers the best bang for your buck in Android phones and is arguably better in many areas than some models costing twice the price.
The Pixel 9a starts at the same £499 (€549/$499/A$849) as last year’s equally good value model. That makes it £300 or so less than Google’s regular Pixel 9 and places it up against mid-rangers such as Nothing’s Phone 3a Pro and Samsung’s Galaxy A56.
Google has stuck with its tried and tested A-series formula, packing the 9a with top-level specs, chips and cameras, cutting a few corners to bring the price down. The result is an extremely compelling package for the money.
The 6.3in OLED screen is crisp, smooth and bright, looking better than many rivals costing far more. It has slightly thicker bezels around the edge than the Pixel 9, but keeps the premium-feeling aluminium band around the outside. The back is high-quality plastic rather than glass, but it’s difficult to tell unless you know. The 9a has full IP68 water resistance, too, matching the best in the business.
The big change is that the standout camera bar on the back is gone. Instead, the twin cameras sit almost flush poking through a small black oval. It means the phone sits flat on a desk and is a little sleeker, but it has also lost some of its charm looking far more generic than the rest of Google’s phone designs.
Specifications
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Screen: 6.3in 120Hz FHD+ OLED (422ppi)
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Processor: Google Tensor G4
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RAM: 8GB
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Storage: 128 or 256GB
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Operating system: Android 15
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Camera: 48MP + 13MP ultrawide, 13MP selfie
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Connectivity: 5G, Sim/eSim, wifi 6E, NFC, Bluetooth 5.3 and GNSS
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Water resistance: IP68 (1m for 30 minutes)
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Dimensions: 154.7 x 73.3 x 8.9mm
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Weight: 185.9g
Tensor G4 and most of Google’s AI
Inside, the 9a has the same top-tier Google Tensor G4 chip as the rest of the Pixel 9 line but with only 8GB of RAM rather than 12GB on the more expensive models. The chip is fast and performance is snappy. It won’t win any raw performance awards but is markedly faster than most mid-range chips and played games just fine.
The battery also lasts a long time: up to 57 hours between charges with general light use, including actively using the screen for nine hours and spending five hours on 5G. That is the longest of all the Google Pixels and means it should see out two days, rivalling some of the longest-lasting handsets on the market.
You likely won’t notice the smaller amount of RAM compared with the Pixel 9 in daily use, but it limits some of the potential for running Google’s AI systems locally on the phone. The 9a has to make do with a smaller version of Google’s Gemini AI tools that can only process text on-device, precluding some of the fancy audio or image-based tools such as the popular Pixel Screenshots and Call Notes apps from the Pixel 9.
All the Gemini features that use the web to process things, such as Gemini Live, work great. As do the various photo editing and image generation tools. The 9a can also produce summaries of voice recordings, but only those under about 15 minutes as there is a maximum number of words it can process in one go.
The rest of the Android 15 experience matches the other Pixel phones, which makes it one of the best in the business. Even better at this price is Google’s seven years of software support for its Pixels, which means you can safely use the 9a for far longer than most in the mid-range market.
Camera
Google’s Pixels have some of the very best cameras, which includes the 9a. It has a new main 48-megapixel camera twinned with a 13MP ultrawide, which matches that from last year’s model.
The main camera is capable of shooting better photos than many full-price flagship phones, and is only a smidgen behind the regular Pixel 9 in low-light scenarios, taking longer to get the shot. Photos across a range of lighting conditions are full of detail and well balanced, while the ultrawide continues to be one of the better options available. The main camera offers a solid 2x zoom that can stretch up to 8x with more obvious digital artefacts.
New for the 9a is a macrophotography mode that uses the main camera and can produce some great shots, though sometimes it struggles to focus and is only sharp in the centre of the image. The selfie camera is solid on the front, while video capture is equally good.
The 9a has popular Best Take and Add Me AI features from the main Pixel 9, as well as various AI editing tools in Google Photos including Magic Editor, unblur and audio magic eraser.
Sustainability
Google says the battery should last about 1,000 full charge cycles with at least 80% of its original capacity. The phone is repairable by Google and third-party shops with genuine replacement parts available direct from iFixit.
The Pixel 9a is made with recycled aluminium, glass, plastic and tin, accounting for at least 23% of the phone by weight. The company publishes an environmental impact report for the phone and will recycle old devices free of charge.
Price
The Google Pixel 9a costs £499 (€549/$499/A$849).
For comparison, the Pixel 9 costs £799, the Samsung Galaxy A56 costs £499, the Nothing Phone 3a Pro costs £449 and the Apple iPhone 16e costs £599.
Verdict
The Pixel 9a shows Google knows how to make a cut-price flagship phone at a mid-range price better than any other.
The combination of top-tier chip, long battery life, great screen and a class-leading camera beats phones costing a lot more. Google’s quality software and long seven years of support only sweeten the deal.
A few corners have been cut to bring the price down, but they aren’t noticeable in day-to-day usage. Missing things such as a lack of wifi 7, satellite messaging or spatial audio, or the use of a plastic back can all be forgiven at this price.
While more expensive Pixel phones offer a few more bells and whistles, it’s hard to overlook the Pixel 9a. It is the best mid-range phone by a wide margin and is the handset to buy for anyone looking for a quality experience that goes the distance for less.
Pros: seven years of software updates, class-leading camera, great screen, top-tier chip, very long battery life, recycled materials, good AI features, undercuts high-end phones on price.
Cons: design quite generic, no optical zoom for camera, raw performance short of best, plastic back, no built-in spatial audio, no wifi 7 or satellite messaging, older Gorilla Glass 3.
Equipped with a starter robot hardware kit and cutting-edge lessons in artificial intelligence, students in CS 123: A Hands-On Introduction to Building AI-Enabled Robots are mastering the full spectrum of robotics – from motor control to machine learning. Now in its third year, the course has students build and enhance an adorable quadruped robot, Pupper, programming it to walk, navigate, respond to human commands, and perform a specialized task that they showcase in their final presentations.
The course, which evolved from an independent study project led by Stanford’s robotics club, is now taught by Karen Liu, professor of computer science in the School of Engineering, in addition to Jie Tan from Google DeepMind and Stuart Bowers from Apple and Hands-On Robotics. Throughout the 10-week course, students delve into core robotics concepts, such as movement and motor control, while connecting them to advanced AI topics.
“We believe that the best way to help and inspire students to become robotics experts is to have them build a robot from scratch,” Liu said. “That’s why we use this specific quadruped design. It’s the perfect introductory platform for beginners to dive into robotics, yet powerful enough to support the development of cutting-edge AI algorithms.”
What makes the course especially approachable is its low barrier to entry – students need only basic programming skills to get started. From there, the students build up the knowledge and confidence to tackle complex robotics and AI challenges.
Robot creation goes mainstream
Pupper evolved from Doggo, built by the Stanford Student Robotics club to offer people a way to create and design a four-legged robot on a budget. When the team saw the cute quadruped’s potential to make robotics both approachable and fun, they pitched the idea to Bowers, hoping to turn their passion project into a hands-on course for future roboticists.
“We wanted students who were still early enough in their education to explore and experience what we felt like the future of AI robotics was going to be,” Bowers said.
This current version of Pupper is more powerful and refined than its predecessors. It’s also irresistibly adorable and easier than ever for students to build and interact with.
“We’ve come a long way in making the hardware better and more capable,” said Ankush Kundan Dhawan, one of the first students to take the Pupper course in the fall of 2021 before becoming its head teaching assistant. “What really stuck with me was the passion that instructors had to help students get hands-on with real robots. That kind of dedication is very powerful.”
Code come to life
Building a Pupper from a starter hardware kit blends different types of engineering, including electrical work, hardware construction, coding, and machine learning. Some students even produced custom parts for their final Pupper projects. The course pairs weekly lectures with hands-on labs. Lab titles like Wiggle Your Big Toe and Do What I Say keep things playful while building real skills.
CS 123 students ready to show off their Pupper’s tricks. | Harry Gregory
Over the initial five weeks, students are taught the basics of robotics, including how motors work and how robots can move. In the next phase of the course, students add a layer of sophistication with AI. Using neural networks to improve how the robot walks, sees, and responds to the environment, they get a glimpse of state-of-the-art robotics in action. Many students also use AI in other ways for their final projects.
“We want them to actually train a neural network and control it,” Bowers said. “We want to see this code come to life.”
By the end of the quarter this spring, students were ready for their capstone project, called the “Dog and Pony Show,” where guests from NVIDIA and Google were present. Six teams had Pupper perform creative tasks – including navigating a maze and fighting a (pretend) fire with a water pick – surrounded by the best minds in the industry.
“At this point, students know all the essential foundations – locomotion, computer vision, language – and they can start combining them and developing state-of-the-art physical intelligence on Pupper,” Liu said.
“This course gives them an overview of all the key pieces,” said Tan. “By the end of the quarter, the Pupper that each student team builds and programs from scratch mirrors the technology used by cutting-edge research labs and industry teams today.”
All ready for the robotics boom
The instructors believe the field of AI robotics is still gaining momentum, and they’ve made sure the course stays current by integrating new lessons and technology advances nearly every quarter.
This Pupper was mounted with a small water jet to put out a pretend fire. | Harry Gregory
Students have responded to the course with resounding enthusiasm and the instructors expect interest in robotics – at Stanford and in general – will continue to grow. They hope to be able to expand the course, and that the community they’ve fostered through CS 123 can contribute to this engaging and important discipline.
“The hope is that many CS 123 students will be inspired to become future innovators and leaders in this exciting, ever-changing field,” said Tan.
“We strongly believe that now is the time to make the integration of AI and robotics accessible to more students,” Bowers said. “And that effort starts here at Stanford and we hope to see it grow beyond campus, too.”
AI Insights
Why Infuse Asset Management’s Q2 2025 Letter Signals a Shift to Artificial Intelligence and Cybersecurity Plays
The rapid evolution of artificial intelligence (AI) and the escalating complexity of cybersecurity threats have positioned these sectors as the next frontier of investment opportunity. Infuse Asset Management’s Q2 2025 letter underscores this shift, emphasizing AI’s transformative potential and the urgent need for robust cybersecurity infrastructure to mitigate risks. Below, we dissect the macroeconomic forces, sector-specific tailwinds, and portfolio reallocation strategies investors should consider in this new paradigm.
The AI Uprising: Macro Drivers of a Paradigm Shift
The AI revolution is accelerating at a pace that dwarfs historical technological booms. Take ChatGPT, which reached 800 million weekly active users by April 2025—a milestone achieved in just two years. This breakneck adoption is straining existing cybersecurity frameworks, creating a critical gap between innovation and defense.
Meanwhile, the U.S.-China AI rivalry is fueling a global arms race. China’s industrial robot installations surged from 50,000 in 2014 to 290,000 in 2023, outpacing U.S. adoption. This competition isn’t just about economic dominance—it’s a geopolitical chess match where data sovereignty, espionage, and AI-driven cyberattacks now loom large. The concept of “Mutually Assured AI Malfunction (MAIM)” highlights how even a single vulnerability could destabilize critical systems, much like nuclear deterrence but with far less predictability.
Cybersecurity: The New Infrastructure for an AI World
As AI systems expand into physical domains—think autonomous taxis or industrial robots—so do their vulnerabilities. In San Francisco, autonomous taxi providers now command 27% market share, yet their software is a prime target for cyberattacks. The decline in AI inference costs (outpacing historical declines in electricity and memory) has made it cheaper to deploy AI, but it also lowers the barrier for malicious actors to weaponize it.
Tech giants are pouring capital into AI infrastructure—NVIDIA and Microsoft alone increased CapEx from $33 billion to $212 billion between 2014 and 2024. This influx creates a vast, interconnected attack surface. Investors should prioritize cybersecurity firms that specialize in quantum-resistant encryption, AI-driven threat detection, and real-time infrastructure protection.
The Human Element: Skills Gaps and Strategic Shifts
The demand for AI expertise is soaring, but the workforce is struggling to keep pace. U.S. AI-related IT job postings have surged 448% since 2018, while non-AI IT roles have declined by 9%. This bifurcation signals two realities:
1. Cybersecurity skills are now mission-critical for safeguarding AI systems.
2. Ethical AI development and governance are emerging as compliance priorities, particularly in regulated industries.
The data will likely show a stark divergence, reinforcing the need for investors to back training platforms and cybersecurity firms bridging this skills gap.
Portfolio Reallocation: Where to Deploy Capital
Infuse’s insights suggest three actionable strategies:
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Core Holdings in Cybersecurity Leaders:
Target firms like CrowdStrike (CRWD) and Palo Alto Networks (PANW), which excel in AI-powered threat detection and endpoint security. -
Geopolitical Plays:
Invest in companies addressing data sovereignty and cross-border compliance, such as Palantir (PLTR) or Cloudflare (NET), which offer hybrid cloud solutions. -
Emerging Sectors:
Look to quantum computing security (e.g., Rigetti Computing (RGTI)) and AI governance platforms like DataRobot (NASDAQ: MGNI), which help enterprises audit and validate AI models.
The Bottom Line: AI’s Growth Requires a Security Foundation
The “productivity paradox” of AI—where speculative valuations outstrip tangible ROI—is real. Yet, cybersecurity is one area where returns are measurable: breaches cost companies millions, and defenses reduce risk. Investors should treat cybersecurity as the bedrock of their AI investments.
As Infuse’s letter implies, the next decade will belong to those who balance AI’s promise with ironclad security. Position portfolios accordingly.
JR Research
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