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Leaping into the AI Maelstrom, Guided by Google Cloud

Leaping into the AI Maelstrom, Guided by Google Cloud

A Remarkable Moment in Tech

The Google Cloud Next 2024 conference was a spectacular showcase of innovation and tech enthusiasm. It seemed almost to burst at the seams with advancements in the creation and operation of applications and their foundational data—as well as in AI, of course, showcasing the rapid progress being made there as well as in cloud and application development. The conference emphasized the seamless integration of AI into various aspects of our lives and work. The unveiling of Google Cloud’s AI Hypercomputer and a truly comprehensive AI ecosystem founded on Gemini and Vertex AI demonstrated the company’s commitment to pushing the boundaries of tech.

AI, Not AI

But before we get into all of it: Shout out to all my operators and cloud architects, the infrastructure managers and application managers, and the application developers of all kinds all over the world who are still doing 99% to 100% of their work on systems and applications that aren’t AI and don’t have AI yet. After all, these happen to be 99% of the systems that run businesses and organizations around the world. These standard and utterly necessary IT efforts ensure the uninterrupted operation and value of the tech, essential to businesses and organizations worldwide.

In a space that is growing increasingly fascinated with AI and its possibilities, this recognition serves as a reminder of the fundamental role of these professions. The ongoing maintenance, protection, and optimization of our current systems by IT professionals is essential as we evaluate and integrate emerging technologies.

That said, unlike all the other tech gold rushes we’ve been through since the Internet slammed into us 30 years ago, it is absolutely not hype at all to say that AI is everywhere. And it should not be tired for me to also say that this was really evident at Google Cloud Next. Don’t @ me. Here’s why.

The Pervasive Value of AI Was on Display

AI is an inescapable component of today’s digital environment and is truly not marketing hype, not even mostly. Google Cloud Next provided a powerful illustration of this as reality, highlighting the many places AI plays. When it comes to utilizing AI, Google Cloud is doing a fine job at empowering not just end users but also developers, data mavens, and system operators who create and oversee applications.

There were numerous examples at the event of AI’s versatile applicability. Gemini, Google’s AI engine—analogous to ChatGPT—was naturally enough demonstrated in support of communication activities such as marketing, writing, and customer service, as well as in expediting creative processes. A common extension of this was the embedding of Gemini for many uses in coding; but Google’s implementation of this is deep, including not just the creation of code but for regression testing, overseeing compliance, or handling increasingly larger and more complex data sets.

We Have to Change Our Frame of Reference

Generative AI alone (it is not all of AI by any stretch but is what everyone means right now when they say “AI”) can enhance almost any function. That means job functions, but critically, it also means software functions.

We need to stop talking about AI applications and start calling them AI features and AI services instead. I don’t really know what an AI application would be. For all the wonderful AI driven no-code capabilities of Google’s AppSheet, it is still a developer platform. AppSheet happens to be totally, and amazingly and very helpfully, infused with AI, but it is not an AI itself.

Chatbots are another example. Sorry, not sorry, but a chatbot is an interactive information tool; its simulated intelligence and knowledge are derived from AI services, but a chatbot is not an AI. In fact, a chatbot could use multiple AI and non-AI services for its simulated intelligence and knowledge, and seamlessly integrate live humans for some of its functionality.

Can we all agree to embrace this? Live AI, integrate AI, add AI to your life. But we don’t have to try to turn anything into AI.

Google Cloud Fits Right In

As I said, my deep appreciation (along with my heart) goes out to the IT, application, and data people that continue to keep the organization humming smoothly amidst all of this change. You are seen. There is still plenty of work to do and AI will undoubtedly be able to help. I can attest that Google Cloud sees you too.

A few other highlights from Google Cloud’s range of announcements at Google Cloud Next include:

  • The A3 Mega Machine Type, generally available next month with doubled GPU-to-GPU fabric bandwidth
  • The TPU v5p ML-training accelerator with GKE support, speeding the creation of new large language models (LLMs)
  • Gemini-based AI-driven optimization, management, and data-mining functionality in Google Cloud Storage
  • Long awaited Google-designed RISC processors for Machine Types, the Google Axion ARM CPU

Future Outlook

We’re just at the beginning. Google Cloud Next 2024 set some markers both for what we should be doing now and what’s to come. There’s plenty of work to do, and AI is definitely on the way to help, whether or not today’s task “is” or “isn’t” AI itself. Google Cloud’s innovations are a remarkable contribution to the effort.

Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.

Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.

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Author Information

Guy is the CTO at Visible Impact, responsible for positioning, GTM, and sales guidance across technologies and markets. He has decades of field experience describing technologies, their business and community value, and how they are evaluated and acquired. Guy’s specialty areas include cloud, DevOps/cloud-native/12-factor, enterprise applications, Big Data, governance-risk-compliance, containerization, virtualization, HPC, CPUs-GPUs, and systems lifecycle management.

Guy started his technology career as a research director for technology media company Ziff Davis, with stints at PC Magazine, eWeek, and CIO Insight. Prior to joining Visible Impact, he worked at Dell, including postings in marketing, product, and technical marketing groups for a wide range of products, including engineered systems, cloud infrastructure, enterprise software, and mission-critical cloud services. He lives and works in Austin, TX

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