Emerging Tech Archives - Page 2 of 5

Custom Silicon and New Interconnect Opportunities To Drive Growth at Marvell

Marvell reported Q1 FY25 financial results on May 30^th with total revenues of $1.16 billion, down 12% Year-on-Year (YoY) and 9% sequentially. As with the previous quarter, the standout business segment was the data centre business, with revenues up 87% YoY and 7% sequentially. However, Marvell posted a $216 million net loss, an increase of 28% YoY.

Data Centre Segment

Data centre revenue reached $814.4 million in Q1 FY25 driven by strong demand for its AI electro-optical products (PAM4 DSPs, TIAs and drivers) as well for its ZR Data Centre Interconnect (DCI) products. The double-digit revenue growth was driven by cloud AI as well as standard cloud infrastructure, which offset a higher than seasonal decline in on-premises enterprise data centre revenues.

In addition, Marvell booked revenues from initial shipments of its custom AI compute programs and announced three new data centre interconnect opportunities: PCIe Gen 6 retimers, AEC PAM4 DSPs and extended range coherent DSP-based DCI modules for use inside and outside data centres.

Optical Interconnects:

100G-lane 800G PAM4 DSPs – are the primary interconnect product for state-of-the-art AI deployments and are shipping in volumes today. Marvell announced that it has started qualifying next-generation 200G/lane 1.6T PAM4 DSP solutions, which will enable next-generation AI accelerators. Volume adoption is expected to start later this year and accelerate during CY 2025.
PCIe Gen 6 retimers – Marvell recently announced its new PCIe Gen 6 retimer product range. PCIe Gen 6 is the first PCI standard to use PAM4 DSPs and these products are designed to help data centre compute fabrics continue to scale inside AI servers. The company is currently sampling its 8-lane and 16-lane PAM4-based PCIe Gen 6 retimer products.
Active Electrical Copper (AEC) PAM4 DSPs – AI demands higher speeds, which is driving the need for active interconnects inside racks. Marvell has started shipping its AEC PAM4 DSPs and has secured design wins with multiple Tier-1 cloud customers.
DCI ZR Modules – Marvell is shipping its 400G ZR products in high volumes and is seeing strong interest in its next-generation 800G ZR/ZR+ pluggable module DCI products. During the quarter, it also demonstrated the industry’s first 3D photonics engine. Marvell’s DCI customer base is expanding with design wins at multiple new data centre customers. However, revenue contribution from the 800G ZR/ZR+ DCI products is not expected to ramp up until next year.

AI clusters today are made up of thousands of GPUs within a single building. As future LLMs increase in size, clusters are expected to comprise hundreds of thousands of GPUs and will be accommodated in multiple buildings on campuses. These buildings will need to be connected so as to look like a single data centre. Marvell recently announced a new coherent DSP for use on campuses, extending the current range from less than two kilometres to 20 kilometres.

Marvell is further expanding its DCI market opportunities by introducing another coherent DSP design based on a new technology called Probabilistic Constellation Shaping (PCS), extending the reach of pluggable DCI modules from 120 kilometres to 1,000 kilometres.

Switches:

In data centre switching, Marvell expects to start production and shipments of its next-generation 51.2T Teralynx 10 switch this summer for lead customer Nvidia.

Custom compute:

Marvell’s custom compute AI programs started to ship in Q1 FY25 with a very substantial ramp-up expected in H2 FY25 followed by a full year of high-volume shipments in FY26. At its recent AI Analyst Day, Marvell revealed that it has custom compute projects with three of the four biggest US hyperscaler operators:

Amazon – AI Trainium training accelerator (ramping up now) and AI Inferentia inference accelerator (expected CY2025 ramp)
Google – ARM Axion custom CPU (ramping up now)
Microsoft – AI Maia accelerator with expected ramp-up in CY2026

Marvell has also developed a custom silicon for chip start-up Groq’s first PetaOPs AI accelerator, a 700mm2 custom ASIC which is in volume production.

Marvell expects its custom silicon business to generate around $200 million by the end of 2024 and the company claims has strong visibility over its 5nm programs over the next two years. In addition, it claims that its 3nm design pipeline and wins have been very strong and is also already engaged on 2nm design work.

Exhibit 1 shows a breakdown of Marvell Q1 FY25 revenues by market segment.

Exhibit 1: Data Centre vs Enterprise, Carrier and Automotive Revenues

5G Infrastructure and Enterprise Networking

As predicted by the company last quarter, the weakness in the 5G infrastructure and enterprise network markets continued with revenues down at both business units:

5G Networks – revenues were $71.8 million in Q1 FY25, down 75% YoY and 58% sequentially. In the previous quarter, Marvell was expecting Q1 FY25 to be the low point with growth resuming in Q2. However, the company now expects Q2 to be flat sequentially with recovery beginning in H2 FY25 driven by the adoption of the next-generation Marvell DPUs at a Tier-1 customer, probably Nokia. Nevertheless, the company expects its 5G market share to increase as customers transition to 5nm Octeon 10 DPUs and baseband processors in H2 FY25. It claims to have won an additional socket with Nokia.
Enterprise Networks – revenue was down 58% YoY and 42% sequentially to $153 million. Although Marvell reported that customers’ excess inventory absorption is progressing, it still expects Q2 revenue to be flat sequentially with recovery starting in H2 FY25.

Automotive/Industrial

The automotive/industrial market is another market experiencing inventory correction. Reported revenues were $78 million, down 13% YoY and 6% sequentially. However, Marvell expects growth to resume in H2 FY25 driven by an increase in Marvell Ethernet content in 2025 model year vehicles.

Analyst Viewpoint

Counterpoint Research believes that Marvell is increasingly well positioned to benefit from the burgeoning growth in AI data centre infrastructure over the next few years. However, many of its new revenues streams will only start towards the end of FY25 but will set up a solid foundation for FY26.

With two hyperscaler customer wins already ramping up, most of the growth in data centre revenue will come from custom silicon over the next two to three years. Marvell had previously pencilled in around $1.5 billion for AI revenue for FY25 (split two-thirds electro-optics, one-third custom compute) – up from $500 million in FY24. However, it now expects to exceed that number and has a target of $2.5 billion in FY26 as the custom silicon programs reach the first full year of volume shipments.

Custom silicon is a business with high barriers to entry and could be very lucrative for Marvell in the medium and long-term. To succeed, vendors need to have R&D scale, technical excellence, cutting edge IP as well as being able to operate on the latest leading-edge process node – attributes which Marvell possesses. In addition, by working closely with the hyperscalers, Marvell will gain a significant advantage as it will acquire unique insights into their next-generation architectural requirements, not only for custom silicon, but also for the connectivity, switching and other products. However, Marvell competes against Broadcom – the 800-pound gorilla of the custom silicon market – and others, including Nvidia, a recent new entrant to the custom silicon market. Over time, there is also a risk that the hyperscalers will develop their own expertise and follow the examples of Apple and Huawei and bring their custom silicon design work in-house.

The other new growth market is data centre interconnects with three new opportunities – AEC interconnects, PCIe Gen 6 retimers and pluggable DCI coherent DSPs – each of which is estimated to be a $1 billion opportunity over the long term. However, in the non-data centre businesses, thinks are not looking quite as rosy, although there are high hopes that both the 5G infrastructure and the enterprise networking businesses have reached the bottom of their respective cycles. 5G capital spending still looks weak and growth might be delayed beyond Q4 FY25. In addition, Marvell is heavily exposed to one vendor, Nokia. Last year, AT&T, one of Nokia’s largest base station customers, switched to rival Ericsson. It will probably take quite some time for these two business units to return to their previous $1 billion per year run rates.

New Tech to Combat Rising Wildfires in Canada

Canada’s 2023 wildfire season surpassed the country’s previous record in terms of the affected area.
Innovative technologies are being deployed to combat this rising trend of wildfires.
AI-equipped sensors have been developed to monitor critical environmental variables.
The Canadian Space Agency is developing the world’s first satellite dedicated to monitoring wildfires.
A less ‘advanced’ technology, but still very important, is practical tools like interactive dashboards and websites.

In 2023, Canada saw an ultimate high of wildfires after a decade of increasing frequency and intensity. The 2023 wildfire season had over 6,500 reported incidents that affected over 18 million hectares of land, surpassing the country’s previous record of 7.6 million hectares. Drivers for this spike were climate change and evolving environmental conditions. To combat this rising trend of wildfires, which devastate air quality and land, innovative technologies are being deployed. They can not only predict such devastating natural events but also prevent and manage them more effectively.

Technologies in development

1. Artificial Intelligence (AI) and sensor technology

Companies like SensaioTech have developed AI-equipped sensors to monitor critical environmental variables, including soil temperature, humidity and luminosity. These sensors provide real-time data, enabling more accurate predictions and quicker responses to potential wildfire threats. This real-time monitoring is a significant improvement over traditional satellite imagery, which often has a delay of several days.

Rogers Communications, in partnership with Pano AI and SpaceX, is in the process of deploying AI-powered cameras that can detect smoke up to 20 km away. Using a 5G network, the cameras provide real-time alerts to first responders to check the spread. This technology focuses more on remote locations where early detection is crucial.

2. Satellite advancements

The Canadian Space Agency is developing WildfireSat, the world’s first satellite dedicated to monitoring wildfires. Unlikely to be launched before 2029, the satellite is being programmed to provide detailed data during peak wildfire instances, which will help emergency services study the trends and triggers for fire activity to better predict and manage wildfires. This initiative aims to bridge the gap in current satellite data, which often misses critical moments of fire activity.

3. Predictive modeling

The province of British Columbia has faced the brunt of wildfire occurrences in Canada, causing it to give wildfire technologies a higher priority than other provinces.

The British Columbia Wildfire Service has been using advanced predictive modeling to forecast wildfire growth and intensity. Much like the AI sensor technology, these models consider various factors, including weather conditions and terrain, to predict the potential spread and impact of wildfires. This allows for more strategic deployment of firefighting resources and better preparation for wildfire seasons.

4. Interactive planning tools for residents

A less ‘advanced’ technology, but still very important, is practical tools like interactive dashboards and websites that provide tools and guidance to residents to prepare comprehensive emergency and evacuation plans, enhancing community resilience against wildfires.

Technology use compared to US

Compared to other countries, Canada experiences a significant number of wildfires annually, but it ranks behind countries like the US and Australia in terms of the total area burned and the number of fires. For instance, the US sees an average of 70,000 wildfires per year, burning over 2.5 million hectares, whereas Canada typically records around 7,300 wildfires, affecting about the same area. This area is considerably lower if we take into account the larger uninhabited land mass that Canada has when compared to other countries.

When it comes to technological integration, Canada is making notable progress but still lags behind some countries. The US, for example, has been quicker to adopt advanced wildfire management technologies, leveraging AI, satellite imagery and drones extensively. However, Canada is catching up with initiatives like WildfireSat and partnerships involving AI-powered detection systems. The slower integration is also due to a restraint on available resources in the more barren landscape, which makes it difficult to get these technologies to cover a larger area in remote places.

Impact of wildfires on Canada

These technological advancements are crucial as Canada faces increasingly severe wildfire seasons. Rising global temperatures and decreasing humidity levels have created conditions that foster more frequent and intense wildfires. The past decade alone has seen some of the worst wildfire seasons on record, with significant economic and environmental impacts.

While wildfires in Canada pose a growing threat, the integration of advanced technologies offers hope for better management and mitigation. From AI and satellite technology to predictive modeling and community planning tools, these innovations are essential in the ongoing battle against wildfires. As traditional wildfire management methods are becoming less dependable, embracing these new technologies will be critical in protecting communities and preserving Canada’s vast forested landscapes. Further, providing effective connectivity across Canada will aid in the quick development of these technologies. This is why we are seeing a rise in telecom player interventions over this issue, like Rogers deploying AI-powered cameras on its already existing towers to detect smoke.

Apple Kickstarts AI Journey with Apple Intelligence Riding on CAPEX (CApable Personalized EXperiences) Push

Apple has unveiled a well-rounded strategy to bring Generative AI capabilities to its ecosystem.
Given the high level of external focus on its AI plans, Apple strategically placed AI at the end of its WWDC24 keynote, branding it ‘Apple Intelligence’, a clever move to gain a greater mind share to start with.
The fact that Apple is positioning it as a capable personalized experience that is based on great architecture, the name Apple intelligence is likely to resonate with users a lot going forward.
The initial use cases announced range from new features for writing, focusing and communication to image creation.

The era of artificial intelligence (or ‘Apple Intelligence’ as the company would like to describe it) beckons Apple users. At its WWDC24 event on Monday, the company unveiled a well-rounded strategy to bring Generative AI capabilities to its ecosystem. Given the high level of external focus on its AI plans, Apple strategically placed AI at the end of its keynote, branding it ‘Apple Intelligence’, a clever move to gain a greater mind share to start with. The fact that Apple is positioning it as a capable personalized experience that is based on great architecture, the name Apple intelligence is likely to resonate with users a lot going forward.

Image Source: Apple

Unlike Google, Apple avoided creating an overly grandiose narrative for AI. Instead, it focused on specific, practical applications. A consumer-friendly approach will truly encourage the mass adoption of AI on its devices. Therefore, Apple has taken a holistic and highly integrated approach to bringing GenAI experiences to its ecosystem.
The initial use cases announced range from new features for writing, focusing and communication to image creation. Apple orchestrated hundreds of actions for end users that make the device experience more personalized. We believe it is an elegant weaving of ‘typical’ AI features like text summary, image creation and contextual info retrieval.

WATCH: Will Apple Intelligence Drive Ecosystem-wide “Super Cycle” for Apple?

Key AI features:

Summarize text
Create original images
Retrieve relevant data when needed
Siri-ChatGPT free access
Transcribe phone calls and voice memos
Solve advanced math equations
Sort through emails
Prioritize notifications

Image Source: Apple

In Siri 2.0, Siri becomes more useful in communicating in a natural conversational and contextual language, the capability being built on top of the App Intent framework, now powered by LLMs. This allows Siri to be more intelligent and take new actions across the apps. Future scenarios for how GenAI, and AImore broadly, can deliver real value to consumers often focus on the evolving role of digital assistants based on software agents.
Despite collaborating with OpenAI, Apple ensured that its capabilities were not overshadowed. For instance, Siri asks for user consent, thus maintaining the strength of its own AI.
At the foundation is Apple Silicon, one of the most powerful and advanced computes available in the market, with the capability to unlock those advanced AI features natively on Apple hardware. Apple also shared the list of devices compatible with Apple Intelligence, starting with the iPhone 15 Pro series in iPhones and iPads.

Image Source: Apple

Built on this are Apple’s ‘Foundation models and Adapters’ trained with underlying privacy-focused “Responsible AI” principles. Adapters are the small neural network modules that fine-tune the models for specific tasks.

Image Source: Apple

Layered on the FM is the ‘App intents’ framework, which is the linchpin here in Apple‘s overall AI strategy. The framework exposes the AI capabilities and drives deeper integration into Apple’s prized application ecosystem. This is the holy grail of Apple’s AI strategy.
OpenAI-ChatGPT-4o partnership adds an extra layer of intelligence for complex queries with universal data across apps.
We believe users and developers will trust Apple’s “privacy-led” narrative both at the edge and in the cloud (Apple’s Private Cloud Compute) to “opt in” with their data to boost the Apple Intelligence strategy.
While it is free for now, at some point Apple will either raise the hardware prices or go with the “hardware-as-a-service” model and start charging for AI.
It can also be a double-sided business model where developers can also earn some (if Apple is generous enough) incentive to integrate App Intents.

Overall, Counterpoint believes that Apple will have more than 250 million fully Apple Intelligence-capable devices by the end of 2025 for developers, Apple and users to start enjoying the scale of Apple Intelligence.

WWDC 2024 – OS Updates

iOS 18 overshadowed, not overlooked

iOS 18 is about customization, productivity and communication. Updates include:

Upgraded home screen, control center
Upgraded messaging:
Finally supporting RCS – green bubbles remain
iOS-Android messaging to support higher-quality images, videos, read receipts, E2E encryption
Scheduling
New text effects
Enhanced Face ID capabilities

Image Source: Apple

iPadOS 18: Redesigned App Experiences

iPadOS 18 brings redesigned experiences across apps, unlocking more capabilities for Apple Pencil.
Customized control center, home screen and an updated photo app from iOS18 coming to iPadOS18 as well.
A floating tab bar makes it easier to navigate between different parts of the app.
Apple brings a native calculator app for iPad.
Math notes and smart script unlock the capabilities of Apple Pencil, leveraging powerful on-device machine learning capabilities.
Messages in iPadOS18 get an update in the form of text formatting, redesigned Tapbacks and the ability to schedule messages at a later time.

Image Source: Apple

macOS Sequoia: Focus on Continuity and Productivity

iPhone Mirroring makes Continuity “More Magical”
Interact with and control iPhone directly from Mac
Safari becomes more productive
‘Highlights’ automatically detects relevant information
from a webpage by using machine learning
Distraction-free video experience
Unified gaming platform
Easier to bring Mac games to iPad and iOS using
Game Porting Toolkit 2
More game titles
Presenter preview and backgrounds for video calls

Image Source: Apple

watchOS18 – Fitness Remains Central to Watch Experience

watchOS18 gets an update on fitness and deeper personalization

Vitals app helps users make more informed decisions about their health on a day-to-day basis.
Watch becomes more personalized with an update to the fitness app, activity rings and Photos watch face.
More intelligent Smart Stack, new widgets, and even suggestions for widgets. The double-tap gesture can now be used to scroll through any app.
Translate app comes to the watch with 20 supported languages.
New APIs for developers to take advantage of the capabilities of Smart Stack and double-tap gesture.

Image Source: Apple

Vision Pro Expands to New Geographies; visionOS2 Uninspiring, but a Step Forward

Apple Vision Pro, exclusively available in the US since February, will now be launched in nine new markets.
visionOS 2 was also previewed with some interesting features:
- Can create spatial photos from existing libraries
- Navigate using new hand gestures
- Watch videos in environments
- Developers will have access to new APIs and frameworks to create more immersive experiences

Overall, we believe the WWDC 24 lived up to its expectations and Apple made it look very smooth. But, like watching a duck on the water, we suspect there is a lot of frantic paddling going on inside. At the same time, Apple is best positioned to make AI and GenAI work well for consumers thanks to its Hardware-as-a-Service business model.

Counterpoint Research Joins DTW24 Ignite as Official Media Partner

Counterpoint Research is pleased to announce its participation as a Media Partner in DTW24 Ignite.

When: 18^th – 20^th June, 2024

Where: Copenhagen

About the Event:

DTW24-Ignite will return to Copenhagen from June 18-20, offering a comprehensive exploration into future-proofing one’s AI-native journey from design to delivery. This event will provide invaluable insights into harnessing the transformative power of AI for innovation and leveraging the capabilities of composable IT. Attendees will have the opportunity to learn how to enhance intelligent networks and implement AI and automation at scale, while also exploring strategic initiatives and adjacent opportunities that unlock new revenue streams.

GPT-4o: OpenAI’s New Frontier in User Experience

OpenAI marked a significant leap forward with its much-anticipated spring update – not by launching a new model like GPT-5 but by introducing GPT-4o, a cutting-edge model that integrates audio, visual and text processing in real time. GPT-4o (“o” for omni) is all about enhancing user experience, and it comes packed with new features and improvements that are set to revolutionize human-machine interaction. Here are some key highlights from OpenAI’s announcement:

Real-time Multimodal Integration: GPT-4o combines audio, visual and text processing, enabling it to interact with users more naturally and intuitively. In a way, GPT-4o integrates three models – text, vision and audio.
Free Access with Improved Speed: OpenAI claims GPT-4o is 2x faster than GPT-4. Users can enjoy the intelligence of GPT-4 with even faster performance, all at no cost.
Enhanced Memory and Analytics: The addition of memory and advanced analytics allows for more sophisticated and personalized interactions. GPT-4o can interpret complex visuals like charts and memes alongside text inputs. Files can be directly uploaded from Google Drive and Microsoft One Drive.
Multilingual Support: Available in 50 languages, GPT-4o caters to a global audience, breaking down language barriers.
Developer-Friendly APIs: Developers can leverage GPT-4o’s capabilities through newly available APIs, fostering innovation across applications.
User-centric Design: The new interface emphasizes a highly integrated and intuitive user experience.
Desktop App: OpenAI will also release a desktop application in addition to the mobile application to cater to a wider range of user needs.
Pricing: GPT-4o’s API pricing is half that of GPT-4 Turbo. In GPT-4o input cost $5 per million tokens while output costs $15 per million tokens. Considering that GPT-4o’s token throughput (tokens per second) is almost 3x that of GPT-4 Turbo, the value proposition is much better for GPT-4o.

Image Source: AI Supremacy

Implications of GPT-4o

Improved Human-Machine Interaction

During the model demonstration, GPT-4o showcased its ability to create more natural conversations. It can generate voice responses in various emotive styles and adjust its answers in real time, even when interrupted or given additional information. This adaptability is a game-changer for human-machine interaction, positioning OpenAI at the forefront of this rapidly evolving field.

OpenAI’s investment in humanoid companies like Figure hints at the broader applications of GPT-4o. The advanced capabilities of this model could significantly enhance the functionality of humanoid robots, making interactions with these machines more fluid and human-like. Additionally, AI devices like wearables and smartphones stand to benefit immensely from GPT-4o’s real-time processing and contextual understanding.

Transforming Customer Service and Virtual Assistants

With its improved contextual understanding and ability to handle complex tasks, GPT-4o is poised to revolutionize customer service and virtual assistants. Its quick, accurate and context-aware responses could enhance user satisfaction and efficiency in these domains, setting new standards for AI-driven interactions. Siri looks outdated when compared to the GPT-4o voice assistant and it would be interesting to see how GPT-4o gets integrated with devices to be able to search and answer based on on-device files.

Advancing Language Translation

GPT-4o’s multilingual capabilities are particularly impressive. During the demonstration, the model translated from English to Italian almost instantaneously, showcasing its potential to improve language translation services. This feature can facilitate more accurate and context-aware translations, bridging communication gaps across different languages.

Personalized Learning Experiences

In education, GPT-4o could offer more personalized and effective learning experiences by adapting content to individual learners’ needs and preferences. For instance, the model’s ability to assist with solving mathematics problems step by step, though seemingly basic, holds the potential to transform educational practices by providing tailored support to students. Schools and colleges are geared towards one-to-many interactions leaving some of the learners behind. GPT-4o as a personal tutor can help students get one-on-one support. However, it remains to be seen how efficient and effective the model is in solving complex problems.

Concerns on Potential Misuse

There are ethical considerations and societal implications in developing human-like AI technologies as they are next step to AGI. The new models can be misused by creating a potentially manipulative AI companion. The model’s ability to process audio and visual inputs could be used to generate highly realistic but fabricated content, such as deepfake videos or synthetic voices, which can be difficult to distinguish from authentic content.

First Impressions

Counterpoint’s team tested GPT-4o on the mobile application as well as on browser and the model’s analytical prowess proved to be remarkable. The team uploaded a stock chart for analysis and shared the results with a seasoned stock technical expert who was thoroughly impressed by GPT-4o’s remarkable output.

Image Source: Mohit Agrawal, Counterpoint Research

In another test, we provided the model with a stock report for ABN AMRO and requested a summary. Remarkably, not only did GPT-4o summarize the report accurately, but it also responded with precision to pointed questions derived from the document. Some inquiries even required the model to interpret charts within the report, which it delivered accurately and without hesitation.

However, the mobile application’s audio experience fell short of expectations. High latency detracted from the smoothness anticipated from OpenAI’s demo event. Despite significant lag in translating from English to Italian, the quality of translation remained exceptional, demonstrating the model’s linguistic prowess.

On the downside, the free version of the application often ran out of credits, hindering file uploads and leading to downgrades to GPT-3.5. However, there was a silver lining in the form of more frequent limit resets, which increased from every 12 hours to every 5 hours. We expect limits to increase substantially as capacity constraints are addressed – a familiar hurdle faced by OpenAI during its initial launch.

Conclusion

OpenAI’s focus with GPT-4o is clear – enhancing user experience. By prioritizing the integration of advanced features and a user-friendly interface, OpenAI aims to maintain its competitive edge. The commitment to improving human-machine interaction highlights the company’s strategic direction in the AI landscape.

GPT-4o represents a significant advancement in AI technology, not through the introduction of a new model, but by fundamentally improving how users interact with AI. Its real-time multimodal integration, enhanced features and focus on user experience make it a pivotal development in the AI field. As OpenAI continues to innovate, GPT-4o stands as a testament to the company’s dedication to leading the future of human-machine interaction.

GenAI in Retail: Opportunities Galore, Challenges Remain

For retailers to fully embrace the opportunity of advanced technologies like GenAI, solution enablers need to find ways to remove obstacles holding back implementation.
There are opportunities left untouched right now to make use of the existing device capabilities and push user engagement further. This is because of long-held beliefs as to how users prefer to engage with their devices and content.
There have been instances of technology rollouts being reversed, confirming the need for a careful evaluation of available solutions.

The Location Based Marketing Association (LBMA) recently held its annual RetailLoco conference in Minneapolis, Minnesota. The event brings together experts and stakeholders from across the retail technology ecosystem. This year’s edition showcased GenAI brand activations while underscoring the persistent challenges retailers face in integrating advanced smartphone features and AI across their physical and online shopping experiences.

Opportunities to grow for all

While a cautious approach to AI and GenAI was a theme throughout the event, there were a few interesting implementations highlighted:

Burger King Brazil ran a promotion where menu combinations were presented to mobile app users once they scanned their facial expressions.
L’Oréal’s Perso at-home assistant integrates Breezometer weather and location information to provide personalized makeup suggestions.

Image source: Gerrit Schneemann, Counterpoint Research

These activations show the potential of GenAI capabilities to engage customers in new ways. At scale, these types of examples can lead to personalized shopping or dining experiences, previously impossible to deliver.

For retailers to fully embrace the opportunity of advanced technologies like GenAI, AR/VR and network-based positioning, solution enablers need to meet them halfway and find ways to remove most of the obstacles currently holding back implementation:

Retailers must prioritize the seamless integration of mobile payments and user-centric experiences.
AI-driven solutions possess the capability to enhance operational efficiency, personalize interactions and streamline business processes, be it predictive maintenance in manufacturing or AR-enhanced marketing endeavors.
Technology providers must recognize that a one-size-fits-all approach to the deployment of new technologies will not work, and the availability of customizable solutions will be critical for adoption.
End users are likely to embrace new technologies if there is a clear benefit for them. This includes sharing location information, downloading specific apps, and changing device use patterns to take advantage of new features.

Fragmentation of backend systems overshadows new technologies’ potential utility

One of the key themes of the presentations by representatives from companies like Harley Davidson, Glympse and Kroger was that technology availability does not necessarily translate to a clear strategy on how to implement these solutions across mobile apps, online, and retail locations.

A striking example of the lack of utilization of existing features on smartphones is augmented reality (AR) for discovery and guidance within stores. The focus of navigation remains on A-to-B guidance to a store with heavy stress on displaying the famous blue dot on the map, with pedestrian guidance and AR content integration appearing out of favor at this point.

There are opportunities left untouched right now to make use of the existing device capabilities and push user engagement further. This is because of long-held beliefs as to how users prefer to engage with their devices and content. One such point of contention is how users would engage with AR content, and the need to hold up a phone to view it. However, this should be less of an issue now as video content creation across social media is standard behavior for many. Gone are the days when phones were neatly tucked away while on the go.

Another challenge to the full embrace of advanced mobile features appears to be the long tail of proprietary internal systems. Retailers feel inadequately prepared to navigate technologies such as AI, chatbots, AR and VR. Constraints stemming from budgetary limitations, dearth of internal resources and lack of executive endorsement impede their adoption efforts further. While mobile payments, AI and chatbots hold immense promise, retailers grapple with the complexities of implementation.

Image source: Gerrit Schneemann, Counterpoint Research

Importantly, there have been instances of technology rollouts being reversed, confirming the need for a careful evaluation of available solutions. Walmart’s reversal on self-checkout stations and findings that Amazon’s touchless shopping experience in physical stores was powered by a large workforce decoding video streams instead of AI are key examples that are likely to cause large organizations to pause and re-evaluate their plans and adjust accordingly.

RetailLoco 2024 served as an interesting snapshot of the status quo and future of retail, where AI and innovation are significant opportunities for all involved in the value chain. However, there are a host of obstacles in the way of faster adoption of the most advanced solutions, driven by an undeniable wariness to over-committing to potentially flawed technologies and the realities of proprietary internal systems, ill-equipped to deal with fast-changing 5G and AI solutions.

Meet Counterpoint at Computex Taipei 2024 Event

Counterpoint will be attending Computex Taipei 2024 Event from June 4 – June 7

Our team of directors and analysts will be attending Computex Taipei Event, 2024. You can schedule a meeting with them to discuss the latest trends in the technology, media and telecommunications sector and understand how our leading research and services can help your business.

Here is the list of team members attending the event:

Brady Wang – Associate Director
Leo Liu –Director (DSCC Taiwan)
William Li – Senior Analyst
Adam Chang – Research Associate
Zoey Hsu – Research Associate

When: June 4– June 7 | 9:30 Am – 5:30 PM

Where: Taipei Nangang Exhibition Center, Hall 1 (TaiNEX 1) | Taipei Nangang Exhibition Center, Hall 2 (TaiNEX 2)

About the event:

COMPUTEX has grown, transformed with the industry, and established its reputation as the world’s leading platform. The expo will continue with the position of “connecting AI”, featuring the latest tech trends: AI Computing, Advanced Connectivity, Future Mobility, Immersive Reality, Sustainability, and Innovations.

Click here (or mail to Counterpoint Taiwan PR Shirley.cheng@counterpointresearch.com) to schedule a meeting with them.

Huawei Expands its AI Ambition With Pangu Large Models

Huawei has underlined its pivotal new objective for 2024 – to capitalize on AI’s strategic prospects and enhance intelligence from networks to industries.
Huawei not only aims to dedicate resources towards fundamental AI research to foster continuous innovation but also aims to actively participate in the formation of global AI policies.
During the summit, Huawei revealed the current progress in training Pangu models with an impressive 230 billion parameters.

Huawei conducted its annual Huawei Analyst Summit (HAS) in Shenzhen, China, between April 17 and April 20. A team of analysts from Counterpoint attended this 21^st edition of HAS to get updates on the company’s progress, vision, and strategy for 2024 and beyond. We utilized the opportunity to also enquire about Huawei’s AI strategy during the keynote’s Q&A segment with Huawei Deputy Chairman and Rotating Chairman Eric Xu.

It was clear from Xu’s statements and the keynote that this year’s HAS marked a leap forward for Huawei from previous events. The company underlined its pivotal new objective for 2024 – to capitalize on AI’s strategic prospects and enhance intelligence from networks to industries. Huawei intends to harness AI to boost the appeal and performance of its products and services, optimize internal operations, and save as well as make more money by improving business and operational performance across industries. Huawei not only aims to dedicate resources towards fundamental AI research to foster continuous innovation but also aims to actively participate in the formation of global AI policies.

Pangu models take the spotlight

Pangu models, introduced by Huawei in 2021 as the world’s largest pre-trained Chinese large language models (LLMs) with over 100 billion parameters, are now advancing into their fourth iteration. During the summit, Huawei revealed the current progress in training Pangu models with an impressive 230 billion parameters.

Pangu for industry

Huawei is helping industries with its ready-to-call AI models in the form of Ascend AI-as-a-Service models. Also, Huawei has already successfully implemented its Pangu large models in industry-specific applications to drive value creation and solve major challenges. Notable deployments driving intelligent R&D and manufacturing include:

AI-assisted coding copilot that enhances R&D efficiency by 50%.
AI-augmented visual inspection systems in manufacturing that attain an accuracy rate of 99%.
Pangu Mining Model intelligently analyses the quality of stress relief drilling and assists rock-burst prevention personnel in quality verification in a coal mine.
It helps reduce manual review workload by 82% and delivers a 100% acceptance rate for rock burst prevention engineering work.

Pangu for science

Huawei also highlighted Pangu’s advanced simulation technologies for science:

Pangu-Weather brings a revolutionary increase in the speed of weather forecasting. It achieves a simulation velocity that is 10,000 times faster than current standards. Such a leap could drastically improve the precision and reliability of meteorological predictions, benefiting everything from agriculture to disaster preparedness.
Pangu Fluid focuses on fluid dynamics simulations, with speeds exceeding current capabilities by 20 times or more. Enhanced simulation speeds can be crucial for a range of applications, including aerodynamics, climate research and engineering.

Pangu Drug points toward a breakthrough in drug discovery with the generation of 100 million new molecules and a tenfold increase in the efficiency of drug design processes. Notably, it also claims a 50% increase in the success rate, potentially accelerating the pace of pharmaceutical innovation and therapeutic breakthroughs.

Next move: Pangu for device, Celia as super AI agent

Amid the surge of LLM chatbots like ChatGPT, Huawei is set to transform its smart assistant Celia into an advanced AI agent. This upgrade will be powered by its Pangu foundation models at the device level.

Celia will be equipped with capabilities for perceiving user intent, delivering anticipatory services, summarizing content, conducting intelligent image searches, and providing a superior AI experience in varied contexts such as work, fitness, entertainment, travel, and home settings.

Key takeaways

Huawei’s Pangu models distinctively target industrial applications, optimizing operations, product R&D and software engineering with remarkable precision and speed, setting them apart from broader-focus models like Baidu’s Ernie Bot and Alibaba’s Tongyi Qianwen. However, this might limit their applicability in general-purpose generative AI models, where applications such as OpenAI’s ChatGPT excel.
Given Huawei’s well-established ecosystem, the success of Pangu models in industrial settings heavily relies on collaborations with its partners. This could restrain its ability to operate and scale independently.
Despite lagging behind SenseTime and Baidu in LLM and multimodal generative AI, Huawei is playing catchup, injecting more resources into fundamental AI research, especially in the areas of AI agent and world model building, the core technologies for realizing on-device chatbot and text-to-video applications.
Challenges remain in getting the required computing power for training larger AI models, as Huawei’s in-house Ascend 910B processors, while capable, still fall short of the superior performance levels offered by NVIDIA’s latest chips.
In summary, the 2024 edition of HAS unveiled Huawei’s aggressive AI strategy, marking a strategic pivot to capitalize on the capabilities of its Pangu foundation models, with significant ramifications across diverse industries.

Apple Could be Looking for iPhone GenAI Partnerships in China and Globally

Rumors of Apple partnering with external GenAI providers highlight potential nuanced AI strategy to meet the needs of disparate markets.
Regulatory compliance, local needs are drivers for possible Baidu tie-up in China.
Apple’s global GenAI strategy – the how and why of in-house vs Gemini.

Generative Artificial Intelligence (GenAI) has emerged as a focal point of innovation within the smartphone industry, captivating the attention of Original Equipment Manufacturers (OEMs) worldwide. Companies such as Samsung, HONOR, and Google have made significant strides by introducing smartphones powered by GenAI technology, reshaping the landscape of mobile devices. Notably, within the Android ecosystem, GenAI integration has taken center stage, positioning it as a frontrunner in leveraging AI capabilities.

In contrast, Apple has been perceived as lagging in this domain, prompting recent discussions of potential collaborations with tech giants like Google and Baidu to integrate their advanced GenAI solutions into the Apple ecosystem. Anticipation is building as rumors suggest that Apple may unveil details of these strategic partnerships at the upcoming Worldwide Developers Conference (WWDC) scheduled for June, signaling a potential shift in the dynamics of AI adoption in the smartphone market.

Bloomberg and the New York Times recently published articles about Apple’s potential deal with Google. Chinese media also carried news about Apple being in talks with Baidu to introduce GenAI technologies in the iPhone.

Here is our take on the contours of Apple’s potential partnership with Baidu and Google:

Apple With Baidu, All for Compliance?

Regulatory Compliance

In the dynamic landscape of Chinese technology regulations, Apple’s pursuit of integrating GenAI into its ecosystem necessitates collaboration with a local partner. Additionally, a tie-up with a Chinese partner will also enable Apple to bring local knowledge to its offering.

The stringent regulatory framework in China – in particular, those around the collection of sensitive public data – makes it highly improbable for AI models developed by Western entities to receive the necessary approvals. AI models of Chinese origin are more favored. This regulatory environment compels Apple to forge new alliances within the country, with Baidu emerging as a potential partner.

Localized AI Solutions

China’s top Internet search engine provider Baidu’s GenAI model is one of 40 models approved by Chinese regulators, with the most famous being the Ernie Chatbot.

Baidu claims its latest version, the Ernie Bot 4.0, outperforms GPT-4 in Chinese, leveraging the world’s largest Chinese language corpus for training.

It excels in grasping Chinese linguistic subtleties, traditions, and history, and can compose acrostic poems, areas that ChatGPT may struggle with.

Although nothing has been officially confirmed, should negotiations between Apple and Baidu prove fruitful, Baidu’s progress in LLM on the mobile device would see strong acceleration, particularly in system optimization, given that about one in five Chinese smartphone users own an iPhone.

Apple might also consider partnering with other GenAI providers in China, like Zhipu AI and its prominent GLM-130B model, or Moonshot AI with its Kimi Chatbot, notable for processing up to 2 million Chinese characters per prompt.

Despite Apple’s choice, embedding a Chinese LLM model into the forthcoming iOS 18 seems improbable. Apple will not let any third-party model weaken its influence on the iOS ecosystem. Consequently, Apple might seek a more collaborative partnership with Baidu with inferencing largely restricted to the cloud. This evolving partnership will showcase Apple’s flexibility and strategic maneuvering within China’s regulatory framework, marking a new era in its AI ventures in the Chinese market.

Apple and Google, Friend or Foe?

Apple is also rumored to be considering a global collaboration with Google to integrate the Gemini AI model on iPhones.

Catching up in AI

Gemini is a suite of large language models developed by Google, renowned for its robust capabilities and extensive applications. While Apple is actively developing its own AI models, including a large language model codenamed Ajax and a basic chatbot named Apple GPT, its technology is unproven.

This makes collaboration with Google a more prudent choice for now. The potential use of Google’s Gemini AI would underscore an effort from Apple to incorporate sophisticated AI functionalities into its devices, beyond its in-house capabilities.

External AI integration: While Apple may use its own models for some on-device features in the upcoming iOS 18 update, partnering with an external provider like Google for GenAI use cases (such as image creation and writing assistance) could indicate a significant shift towards enhancing user experiences with third-party AI technologies.

Apple might follow the strategy of Chinese OEMs like OPPO, vivo, HONOR and Xiaomi who all have in-house LLMs that have been deployed into their latest flagship models. Meanwhile, Apple could work with third parties like Baichuan, Baidu and Alibaba to leverage a broader GenAI ecosystem. With this strategy, Chinese OEMs have full control of the local big model and resulting changes in hardware and software closely relating to smartphone design, and they can continue to make a profit by simultaneously owning an App store and AI agent. If Apple were to follow this strategy for international markets, it may seek alliances not only with Google but also with OpenAI and Anthropic. This approach would mean that Apple will not have to open iOS to third-party LLMs.

If Apple were indeed to incorporate Gemini into its new operating system to offer users an enhanced, more intelligent experience, these advancements would primarily be focused on-device, rather than on cloud-based features. This means Google’s Gemini Nano, the most lightweight LLM, is a preferred choice, as it has also been optimized and embedded in Google’s Pixel 8 Smartphone.

Implications of Apple’s Potential LLM Partnerships

In China, a potential partnership with Baidu would ensure Apple’s offerings are both culturally attuned and compliant with local regulations, while the global collaboration with Google promises to inject a new level of intelligence into the iPhone’s capabilities, thus redefining the essence of mobile user experience for Apple.

These types of partnerships would highlight a nuanced approach to AI integration, tailored to meet the distinct needs of different markets.

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Coverage: Emerging Tech

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