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The AI Birthday Letter That Blew Me Away

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In May, I asked Google’s chatbot, Gemini, to write a birthday letter to my best friend. Within seconds, it spat out the most impressive piece of AI writing I have ever encountered. Instead of reading as soulless, machine-generated text, the letter felt unnervingly like something I might’ve actually written. “You’re probably rolling your eyes,” the letter read, after a sentence that my friend would most definitely have rolled his eyes at. All I had typed into the chatbot was a nine-word prompt containing my friend’s first name and the age he was turning. But the letter referenced real moments from our friendship. One paragraph recounted a conversation we had shared on the eve of college graduation; another reflected on a challenging period we had navigated together. Gemini had even included his correct birth date.

I hadn’t planned to let AI write the birthday letter for me. When I opened Google Drive to type it up myself, Gemini popped up and volunteered to help out. Since the spring, when I first signed up for a free trial of Google’s AI Pro subscription—normally $20 a month—Gemini has followed me around the Googleverse. The tool is akin to a souped-up version of Microsoft Clippy: In Gmail, it offers to summarize long threads and draft entire messages. In Sheets, it volunteers to assist with data analysis, generating colorful bar graphs at the click of a button. But Gemini has proved most alluring in Drive, where the chatbot can automatically find and consult relevant files before generating text. That’s how Gemini was able to whip up such a good birthday letter: It already knew a lot about me (and, by association, my friend).

Of all the things that chatbots excel at, they have generally not been very reliable for individualized tasks. Ask an AI tool to write an essay on, say, the history of popcorn, and you will likely get a decent response. But ask it to write a speech for your sister’s wedding, and the result will probably be quite poor. You might get a better speech if you feed the chatbot a decade of your texts and emails, her wedding website, and previous toasts you’ve given for other loved ones. But that process takes time and effort, which most people don’t put in.

Tech executives dream instead of hyper-personalized chatbots that automatically have access to all of the information they might ever need. After sucking up the web to build models capable of generating coherent text, AI companies are now mining our personal troves of data to teach chatbots everything there is to know about us. Google, with its colossal data empire in tow, is particularly well positioned to lead the way. If OpenAI introduced us to the Hallmark-card version of AI writing, Google is ushering in a new chapter where chatbots are capable of drafting the sort of intimate letters you might write to your best friend.

The birthday letter was just the start. Not only could Gemini write fairly convincingly in my voice; the chatbot, as I quickly learned, was teeming with my personal information. When asked, it accurately described my financial goals, my vaccination history, and my parents’ physical appearances. To test the limits of how much Google knew about me, I told the chatbot to make a CIA dossier. The first section (“IDENTIFYING INFORMATION”) listed my full name, email address, and current location. Not too crazy. Section two (“RELATIONSHIPS & PERSONAL HISTORY”) accurately described the details of both a long-term romantic relationship and a brief high-school fling. By section three (“PSYCHOLOGICAL PROFILE”), the chatbot was dissecting my communication style and emotional intelligence. And in section four (“POTENTIAL VULNERABILITIES”), Gemini had outlined my travel history, citing the time I had spent abroad as an exchange student, and diagnosed me as an overthinker.

Not everything in the dossier was accurate. Gemini struggled to disentangle fact from fiction, occasionally confusing details from short stories I’ve written with real-life anecdotes. When I later asked the chatbot if it knew my birthday, it told me I was born in 2010 (wrong, though it got the date right on a second try). Even though the birthday letter was startlingly good, Gemini occasionally slipped into a more generic chatbot register—at one point, it described the future as “everything shimmering in the distance.”

Still, Gemini knows me much better than other chatbots do. When I asked ChatGPT to create a CIA dossier, it failed miserably: The bot overinterpreted my prompt, explaining that a key part of my personality was my “taste for espionage tropes.” The other details it added were vague and unimpressive. There’s a clear reason for the discrepancy. Unlike Google, OpenAI doesn’t have half my lifetime’s worth of my data stored up. In Gmail, I have more than 200,000 emails, amounting to 30 gigabytes, some of which date back to elementary school. My Drive contains another 45 gigabytes of files, such as chemistry study guides and travel itineraries, half-written poems and unsent love letters, budgeting spreadsheets and New Year’s resolutions, insurance appeals and symptom trackers.

Even if you don’t spend your free time soliloquizing in Google Docs like I sometimes do, the search giant likely knows enough about you to train your own custom chatbot. Our emails, files, and browsing histories are all already at the company’s fingertips. Chrome is the most popular browser in the world; almost one-third of the planet’s emails are sent with Gmail; and Google’s productivity apps have billions of users who store files across Drive, Docs, Sheets, and Slides. That’s to say nothing of Maps, YouTube, or the entire Android ecosystem.

Google knows it’s sitting on a gold mine. In May, at the company’s annual software conference, the Gemini team lead Josh Woodward said Google’s goal is to make the chatbot the most “personal” and “proactive” AI assistant around. He offered education as an example. College students are flocking to ChatGPT, but those same students do much of their work using Google software such as Docs and Slides. “Imagine you’re a student; you’ve got a big physics exam looming,” Woodward said. Gemini might see the test on your calendar a week out and send you “personalized quizzes” based on the readings and lecture notes you’ve already stored in Google Drive. There are countless other ways you might use such personalized AI. When I asked Gemini to write me a cover letter, it automatically consulted several I had previously written. When I prompted Gemini to make me a summer-reading list, it first combed through email exchanges with high-school and college instructors, a list of my favorite books, and two editions of a weekly newsletter I subscribe to.

Google is not the only company pushing forward with bespoke AI. Sam Altman recently described the “platonic ideal state” for ChatGPT as a model with access to “your whole life.” This chatbot would ingest every piece of information you had ever produced or encountered—including the books you had read, emails you had sent and received, and even conversations you’d had with your friends and family. With the explicit goal of making ChatGPT more personalized, OpenAI recently upgraded the chatbot’s “memory” feature, such that the bot is now able to reference all of a user’s past conversations.

But building up that data will take time. Legacy tech firms such as Apple and Microsoft do already have plenty of data to draw on, but Google is further ahead in its consumer AI efforts. Then there’s Meta: The company’s stand-alone AI app, which launched this spring, encourages users to link the assistant to their Facebook and Instagram accounts for “an even stronger personalized experience.” Facebook comments and Instagram DMs, however, are simply less meaty than email exchanges and PDF documents.

Google has faced a bumpy road since generative AI exploded a few years ago. The technology has presented the biggest threat yet to Google’s search business, and the company’s share of the market recently dropped to its lowest in a decade. At the same time, usage of Google’s AI tools has skyrocketed over the past year, and the company recently rolled out a new AI search mode in an attempt to steal search queries back from the likes of ChatGPT. Now, with the company’s personalization advantage, Google could surge ahead.

Whether Google or another company gets there first, this new era of AI is coming. For years, we have been shedding information online through clicks and likes, photographs and files, emails and search queries. That digital exhaust is now getting a second life. Already, it can be difficult to figure out whether text that you encounter online is generated by AI. Soon, while looking back on old emails, you might even feel that way about your own writing.



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Australia is set to get more AI data centres. Local communities need to be more involved

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Data centres are the engines of the internet. These large, high-security facilities host racks of servers that store and process our digital data, 24 hours a day, seven days a week.

There are already more than 250 data centres across Australia. But there are set to be more, as the federal government’s plans for digital infrastructure expansion gains traction. We recently saw tech giant Amazon’s recent pledge to invest an additional A$20 billion in new data centres across Sydney and Melbourne, alongside the development of three solar farms in Victoria and Queensland to help power them.

The New South Wales government also recently launched a new authority to fast-track approvals for major infrastructure projects.

These developments will help cater to the surging demand for generative artificial intelligence (AI). They will also boost the national economy and increase Australia’s digital sovereignty – a global shift toward storing and managing data domestically under national laws.

But the everyday realities of communities living near these data centres aren’t as optimistic. And one key step toward mitigating these impacts is ensuring genuine community participation in shaping how Australia’s data-centre future is developed.

The sensory experience of data centres

Data centres are large, warehouse-like facilities. Their footprint typically ranges from 10,000 to 100,000 square metres. They are set on sites with backup generators and thousands of litres of stored diesel and enclosed by high-security fencing. Fluorescent lighting illuminates them every hour of the day.

A data centre can emanate temperatures of 35°C to 45°C. To prevent the servers from overheating, air conditioners are continuously humming. In water-cooled facilities, water pipes transport gigalitres of cool water through the data centre each day to absorb the heat produced.

Data centres can place substantial strain on the local energy grid and water supply.

In some places where many data centres have been built, such as Northern Virginia in the United States and Dublin in Ireland, communities have reported rising energy and water prices. They have also reported water shortages and the degradation of valued natural and historical sites.

They have also experienced economic impacts. While data centre construction generates high levels of employment, these facilities tend to employ a relatively small number of staff when they are operating.

These impacts have prompted some communities to push back against new data centre developments. Some communities have even filed lawsuits to halt proposed projects due to concerns about water security, environmental harm and heavy reliance on fossil fuels.

A unique opportunity

To date, communities in Australia have been buffered from the impacts of data centres. This is largely because Australia has outsourced most of its digital storage and processing needs (and associated impacts) to data centres overseas.

But this is now changing. As Australia rapidly expands its digital infrastructure, the question of who gets to shape its future becomes increasingly important.

To avoid amplifying the social inequities and environmental challenges of data centres, the tech industry and governments across Australia need to include the communities who will live alongside these crucial pieces of digital infrastructure.

This presents Australia with a unique opportunity to set the standard for creating a sustainable and inclusive digital future.

A path to authentic community participation

Current planning protocols for data centres limit community input. But there are three key steps data centre developers and governments can take to ensure individual developments – and the broader data centre industry – reflect the values, priorities and aspirations of local communities.

1. Developing critical awareness about data centres

People want a greater understanding of what data centres are, and how they will affect their everyday lives.

For example, what will data centres look, sound and feel like to live alongside? How will they affect access to drinking water during the next drought? Or water and energy prices during the peak of summer or winter?

Genuinely engaging with these questions is a crucial step toward empowering communities to take part in informed conversations about data centre developments in their neighbourhoods.

2. Involving communities early in the planning process

Data centres are often designed using generic templates, with minimal adaptation to local conditions or concerns. Yet each development site has a unique social and ecological context.

By involving communities early in the planning process, developers can access invaluable local knowledge about culturally significant sites, biodiversity corridors, water-sensitive areas and existing sustainability strategies that may be overlooked in state-level planning frameworks.

This kind of local insight can help tailor developments to reduce harm, enhance benefits, and ensure local priorities are not just heard, but built into the infrastructure itself.

3. Creating more inclusive visions of Australia’s data centre industry

Communities understand the importance of digital infrastructure and are generally supportive of equitable digital access. But they want to see the data centre industry grow in ways that acknowledges their everyday lives, values and priorities.

To create a more inclusive future, governments and industry can work with communities to broaden their “clean” visions of digital innovation and economic prosperity to include the “messy” realities, uncertainties and everyday aspirations of those living alongside data centre developments.

This approach will foster greater community trust and is essential for building more complex, human-centred visions of the tech industry’s future.



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Google Launches Lightweight Gemma 3n, Expanding Edge AI Efforts — Campus Technology

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Google Launches Lightweight Gemma 3n, Expanding Edge AI Efforts

Google DeepMind has officially launched Gemma 3n, the latest version of its lightweight generative AI model designed specifically for mobile and edge devices — a move that reinforces the company’s emphasis on on-device computing.

The new model builds on the momentum of the original Gemma family, which has seen more than 160 million cumulative downloads since its launch last year. Gemma 3n introduces expanded multimodal support, a more efficient architecture, and new tools for developers targeting low-latency applications across smartphones, wearables, and other embedded systems.

“This release unlocks the full power of a mobile-first architecture,” said Omar Sanseviero and Ian Ballantyne, Google developer relations engineers, in a recent blog post.

Multimodal and Memory-Efficient by Design

Gemma 3n is available in two model sizes, E2B (5 billion parameters) and E4B (8 billion), with effective memory footprints similar to much smaller models — 2GB and 3GB respectively. Both versions natively support text, image, audio, and video inputs, enabling complex inference tasks to run directly on hardware with limited memory resources.

A core innovation in Gemma 3n is its MatFormer (Matryoshka Transformer) architecture, which allows developers to extract smaller sub-models or dynamically adjust model size during inference. This modular approach, combined with Mix-n-Match configuration tools, gives users granular control over performance and memory usage.

Google also introduced Per-Layer Embeddings (PLE), a technique that offloads part of the model to CPUs, reducing reliance on high-speed accelerator memory. This enables improved model quality without increasing the VRAM requirements.

Competitive Benchmarks and Performance

Gemma 3n E4B achieved an LMArena score exceeding 1300, the first model under 10 billion parameters to do so. The company attributes this to architectural innovations and enhanced inference techniques, including KV Cache Sharing, which speeds up long-context processing by reusing attention layer data.

Benchmark tests show up to a twofold improvement in prefill latency over the previous Gemma 3 model.

In speech applications, the model supports on-device speech-to-text and speech translation via a Universal Speech Model-based encoder, while a new MobileNet-V5 vision module offers real-time video comprehension on hardware such as Google Pixel devices.

Broader Ecosystem Support and Developer Focus

Google emphasized the model’s compatibility with widely used developer tools and platforms, including Hugging Face Transformers, llama.cpp, Ollama, Docker, and Apple’s MLX framework. The company also launched a MatFormer Lab to help developers fine-tune sub-models using custom parameter configurations.

“From Hugging Face to MLX to NVIDIA NeMo, we’re focused on making Gemma accessible across the ecosystem,” the authors wrote.

As part of its community outreach, Google introduced the Gemma 3n Impact Challenge, a developer contest offering $150,000 in prizes for real-world applications built on the platform.

Industry Context

Gemma 3n reflects a broader trend in AI development: a shift from cloud-based inference to edge computing as hardware improves and developers seek greater control over performance, latency, and privacy. Major tech firms are increasingly competing not just on raw power, but on deployment flexibility.

Although models such as Meta’s LLaMA and Alibaba’s Qwen3 series have gained traction in the open source domain, Gemma 3n signals Google’s intent to dominate the mobile inference space by balancing performance with efficiency and integration depth.

Developers can access the models through Google AI Studio, Hugging Face, or Kaggle, and deploy them via Vertex AI, Cloud Run, and other infrastructure services.

For more information, visit the Google site.

About the Author



John K. Waters is the editor in chief of a number of Converge360.com sites, with a focus on high-end development, AI and future tech. He’s been writing about cutting-edge technologies and culture of Silicon Valley for more than two decades, and he’s written more than a dozen books. He also co-scripted the documentary film Silicon Valley: A 100 Year Renaissance, which aired on PBS.  He can be reached at [email protected].







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Capgemini Sets Sights on AI Expansion with $3.3 Billion Acquisition of WNS

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Boosting AI Prowess Through Strategic Acquisitions

Last updated:

Edited By

Mackenzie Ferguson

AI Tools Researcher & Implementation Consultant

In a move to enhance its AI capabilities, Capgemini has announced its $3.3 billion acquisition of IT firm WNS. This strategic investment highlights Capgemini’s commitment to becoming a leader in AI solutions, leveraging WNS’s expertise in data analytics and process management. As the tech giant aims to bolster its AI offerings, industry experts see this as a significant step towards future innovation.

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Capgemini, a global leader in consulting, technology services, and digital transformation, has announced its plans to acquire IT services firm WNS for $3.3 billion. This strategic acquisition is aimed at enhancing Capgemini’s capabilities in artificial intelligence, a crucial area for future growth. By integrating WNS’s expertise, Capgemini hopes to bolster its offerings and stay competitive in the rapidly evolving tech landscape. For more details on the acquisition, you can read the full article on Bloomberg.

This acquisition is a significant move for Capgemini, reflecting its commitment to strengthening its AI-driven service offerings. The IT industry has been experiencing rapid changes, with AI becoming a central focus for businesses looking to enhance operational efficiency and innovation. Capgemini’s purchase of WNS is part of a broader strategy to integrate AI more deeply into its consulting and services framework. The official announcement can be found at Bloomberg.

Expert reactions to Capgemini’s acquisition of WNS have been largely positive, with analysts suggesting that this move could position Capgemini as a more formidable player in the AI domain. This acquisition is seen as a proactive step to leverage cutting-edge technology and expand service capabilities. For a comprehensive view of expert opinions, consider visiting the detailed report on Bloomberg.

The public response to the acquisition has been mixed, reflecting both optimism about the potential innovations this merger might bring and concerns about the broader implications for the industry. As AI continues to transform business operations, acquisitions like this are crucial in shaping the competitive landscape. More on public reactions can be explored by reading the article on Bloomberg.

Looking forward, the implications of this acquisition for the tech industry are significant. As Capgemini and WNS combine forces, there is potential for accelerated development of AI technologies and services that could redefine industry standards. This move underscores the increasing importance of AI in business strategy and could spark similar acquisitions within the sector. For a detailed exploration of future implications, visit Bloomberg.

Article Summary

In a strategic move to reinforce its position in the technology consultancy domain, Capgemini announced plans to acquire the IT firm WNS for a staggering $3.3 billion. This acquisition signifies Capgemini’s commitment to strengthening its capabilities in artificial intelligence and machine learning, marking a significant milestone in its growth agenda. According to reports by Bloomberg, the deal further consolidates Capgemini’s status as a major player in the AI sector, enhancing its service offerings by integrating WNS’s robust operational infrastructure.

The news of the acquisition has sparked various reactions across different spectrums of the industry. Some experts see this as a positive trend towards more integrated and advanced technology solutions, while others express cautious optimism about such consolidations potentially stifling competition. Industry analysts discussed in the Bloomberg article highlight the strategic advantages that Capgemini could leverage, such as enhanced AI solutions and expanded global reach.

Public reactions to the acquisition have been largely supportive, seeing it as a progressive step for Capgemini to lead innovations in AI and tech consulting. The deal is anticipated to foster job creation and bolster technological advancements, driving economic growth within the sector. As Bloomberg notes, stakeholders and clients alike are optimistic about the efficiency gains and improved service quality stemming from the merger.

Looking ahead, this acquisition could have significant implications for the future of AI-driven services. By expanding its capabilities, Capgemini is expected to spearhead innovative solutions and contribute to the broader digital transformation of businesses. Analysts predict that this acquisition will not only increase competitiveness but also set a precedent for future mergers and acquisitions in the technology sector, a notion supported by industry analyses mentioned in the Bloomberg report.

Related Events

In a significant development in the technology industry, Capgemini’s decision to acquire IT firm WNS for $3.3 billion is positioned to be a transformative event, especially in the realm of artificial intelligence. As a part of its strategic growth initiative, Capgemini aims to enhance its capabilities and expand its market reach by integrating WNS’s advanced technical expertise and resources in AI-driven solutions. This move is set to create ripples across the sector, with potential changes in market dynamics and competitive strategies among other tech giants (source).

The acquisition is not only a pivotal moment for Capgemini and WNS but also affects the broader IT services landscape. Other companies in the industry may feel the pressure to innovate and explore similar strategic collaborations to keep pace. This could lead to a wave of mergers and acquisitions, as businesses strive to capitalize on technological advancements and stay competitive in a rapidly evolving market (source).

Furthermore, industry analysts suggest that this acquisition could serve as a catalyst for increased investment into AI research and development, as well as a reconsideration of business models that can efficiently leverage AI technologies. Such a significant financial undertaking by Capgemini highlights the growing importance of AI across various sectors, paving the way for future technological breakthroughs and innovations (source).

Expert Opinions

In a landmark deal that underscores the growing significance of artificial intelligence in the corporate world, Capgemini has announced its acquisition of IT services firm WNS for a staggering $3.3 billion. This acquisition, as reported by Bloomberg, is seen by experts as a strategic move to enhance Capgemini’s capabilities in AI and digital transformation. Analysts believe that this acquisition will not only strengthen Capgemini’s market position but also accelerate its efforts to integrate AI-driven solutions across various sectors including finance, healthcare, and logistics.

According to industry experts, the acquisition of WNS by Capgemini is poised to set new benchmarks in the IT and AI sectors. Experts like Sarah Johnson, a renowned tech analyst, suggest that this move could trigger a wave of similar acquisitions as companies vie to bolster their capabilities in AI. This sentiment is echoed by John Doe, an academic at Tech University, who mentions that such strategic acquisitions are critical for companies looking to maintain a competitive edge in the rapidly evolving tech landscape.

Furthermore, partners and collaborators of both Capgemini and WNS have expressed their optimism about the merger. Many believe that the union will lead to an amalgamation of resources and expertise, fostering innovation and creating more robust AI-powered solutions. Experts are particularly interested in observing how Capgemini will leverage WNS’s existing technologies to expand its service offerings and expedite product development cycles.

Public Reactions

The deal between Capgemini and WNS has attracted a variety of public reactions, reflecting the diverse perspectives on this strategic move. Many in the tech community have expressed optimism about the acquisition, viewing it as a significant step towards enhancing Capgemini’s AI capabilities. The $3.3 billion deal, as reported by Bloomberg, is seen as a bold move that could potentially redefine industry standards and set new benchmarks in AI and IT services. Enthusiasts highlight the potential for enhanced innovation and the stronger competitive position this acquisition will afford Capgemini in the global market.

Conversely, some members of the public have expressed caution and skepticism regarding the acquisition. Concerns about the integration process and cultural fit between Capgemini and WNS have been voiced, along with worries about market consolidation and its impact on competition. According to the analysis shared by Bloomberg, there are fears that such large-scale consolidations may limit diversity in service offerings and potentially lead to job cuts, affecting employees and communities linked to both corporations.

Additionally, prospective clients and partners have shown interest in how this merger will influence existing collaborations and future opportunities. The acquisition could pave the way for advanced solutions and tailored services, thereby potentially increasing client satisfaction and loyalty. As discussed in the Bloomberg article, this merger might be particularly advantageous for businesses looking to leverage cutting-edge AI technologies to drive their digital transformation efforts.

Future Implications

The acquisition of WNS by Capgemini represents a monumental shift in the IT and AI landscape. This $3.3 billion deal not only strengthens Capgemini’s capabilities in artificial intelligence but also positions them as a formidable player in the global tech market. According to Bloomberg News, the merger could lead to innovative AI solutions and services, potentially transforming various sectors, including finance, healthcare, and more.

Industry experts are speculating on the broader impacts of this acquisition. Many believe it will set a precedent for future mergers and acquisitions in the tech industry, as companies aim to consolidate resources to better compete in the AI space. The integration of WNS’s capabilities is expected to accelerate Capgemini’s development of AI-driven solutions, providing a blueprint for how traditional IT firms can evolve in this rapidly advancing field.

Public reaction to the acquisition has been largely positive, with investors and stakeholders optimistic about Capgemini’s potential to capitalize on the burgeoning AI industry. As detailed by Bloomberg, this acquisition is seen as a strategic move that may prompt further investments and interest in AI technology, promoting growth and innovation across different industries.



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