2 weeks Ago
The main stage at Google Cloud Next 2024. Google is set to showcase some of its latest work in data management, artificial intelligence and cloud technology at its Google Cloud Next 2025 event in Las Vegas next week. I expect Next 25 to offer examples, from Gemini-powered databases to AI-driven data analytics, of how Google is approaching the next phase of its cloud computing efforts — and working to differentiate itself against an array of competitors in each of these areas.
Based on my conversations with the company, we can expect this year’s conference to explore practical ways to integrate cloud, data and AI across various industries at the same time it provides a platform for developers, nurtures AI talent and expands access to Google Cloud technologies. Let’s dig into where Google Cloud is today and what we might see next week at its big event. (Note: Google is an advisory client of my firm, Moor Insights & Strategy.
) A major area of interest is how Google will expand its Gemini models for database management and developer tooling. This isn’t about embedding AI for novelty but tackling real inefficiencies. Focus areas such as AI-assisted code generation, SQL query optimization and data and application migrations, along with data pipeline automation, aim to address key challenges that developers face when managing increasingly complex data systems.
Within Google Cloud, a data pipeline might use tools such as Pub/Sub to collect data, Dataflow to process it and BigQuery to store and analyze the results. This reflects a broader industry shift: tools aren’t judged only by their features, but by how well they reduce cognitive load for the professionals implementing these technologies. With Gemini integrated into Google services such as BigQuery, Cloud SQL, AlloyDB and Spanner, developers should be able to simplify workflows, focus on higher-level tasks and eliminate much of the manual effort that can slow progress.
If Gemini really does help reduce friction in data analysis, app development and migrations, the longstanding bottleneck of data wrangling may finally start to ease. It’s a shift worth watching, not least because of the steady arrival of competing offerings from rivals. For example, since last year’s Next conference, Oracle released Oracle Database 23ai and Microsoft debuted Microsoft Fabric ( which I analyzed in detail here ).
Those two software giants join Google, plus number-1 cloud service provider AWS and data-and-AI stalwart IBM, to expand the possibilities at the intersection of data management, AI and cloud services. All of these companies are seeking to differentiate themselves in this quest — and none of them can afford to fall behind. Google Cloud CEO Thomas Kurian presents at Google Cloud Next 2024.
Google’s continued investment in infrastructure that’s purpose-built for AI is sure to be another key focus. One area to watch closely is Google’s strategy around sustainable AI infrastructure. As AI workloads grow more compute-intensive, metrics like energy per inference, latency per cost (how efficiently a system can deliver fast results without being too expensive) and overall carbon footprint have become critical.
In this context, Google’s renewable-powered data centers, combined with the efficiency of its TPUs, will need to show measurable progress, not just in performance but in operational sustainability. Equally important is usability. Google technologies including Vertex AI, AI Hypercomputer and the integration of Cloud TPU within Google Kubernetes Engine should help lower barriers to entry, allowing organizations to tap into advanced AI infrastructure without needing specialized teams.
Ultimately, the real-world implementations of this will test how well Google — or potentially its partners — can translate technical advantages into accessible, scalable and sustainable solutions. If it succeeds, it could mark a positive step in democratizing generative AI infrastructure for organizations of different types and sizes, while helping Google stand out as it competes with larger CSPs AWS and Microsoft Azure. Google has stated its commitment to nurturing AI talent, and from what I’ve seen, the commitment seems genuine.
Google offers a range of resources, including training programs, Google Cloud Certifications, AI Essentials courses and support for early-stage companies through its Startups Accelerator and Cloud Program for Startups. These efforts provide early-stage teams with access to scalable infrastructure, mentorship and technical guidance — resources often out of reach for smaller companies. Through its various programs, Google supports the development of AI-focused startups by helping developers build and train models more efficiently from the beginning.
This reflects an understanding that progress often depends on how people apply technology, not just the tools themselves. As someone who closely follows startup ecosystems, I see this not just as community support, but as a strategic approach to empower developers from the outset to help create long-term adoption. From my perspective, it’s a smart investment on Google’s part in building a community that grows with the platform, creating a virtuous cycle of innovation, feedback and platform maturity — while cultivating potential long-term customers.
Data sprawl remains a significant challenge for enterprises. In a recent article on enterprise data management , I emphasized the value of unifying data across the entire data journey. Google seems to share my outlook, because its Data Cloud takes an open and unified approach across hybrid and multi-cloud environments; this helps reduce compliance risks, streamline manual processes and accelerate AI model deployment.
Beyond that, most organizations manage both structured and unstructured data. Analyzing both within a single workflow — without needing to move data across platforms — is critical for speed, accuracy and operational efficiency. Equally important is Google Cloud’s support for open formats and multi-cloud deployments.
The industry is moving away from vendor lock-in, and Google’s adoption of open standards reflects a pragmatic strategy that better fits the way modern enterprises work. Examples of this include Google’s use of Apache Iceberg, services with BigLake for unified storage across data lakes and warehouses and continued investments in PostgreSQL. Gemini, now integrated into tools like BigQuery, can translate conversational prompts into structured queries or backend code.
By evolving how users — even non-technical ones — access and manipulate data, Gemini also contributes to building a more unified platform where insights can be developed and shared across teams without relying on fragmented tools or workflows. Google’s emphasis on delivering a unified and open ecosystem is not new. Still, it is an important area that I expect will be a key focus at Next 25 and throughout the year.
Google has focused on improving data accessibility for years, but in my view its tools are now aligned better with this goal. Besides supporting developers and startups to build and train AI models more effectively, new AI-powered analytics and visualization capabilities enable non-technical users to extract insights without needing a background in data science. More specifically, products such as Looker Studio and Connected Sheets are helping bridge the gap between business users and complex datasets.
Vertex AI and BigQuery ML make running machine learning models and advanced analytics easier with simple SQL or no-code interfaces. If Google can continue to deliver solutions that combine ease of use with analytical depth, that should help close a persistent gap in the analytics landscape. Looking ahead, there is still more to do to spread these solutions more widely across enterprises.
For example, it will be critical for these tools to support collaborative workflows, version control and fine-grained access controls — capabilities essential for operationalizing data across teams and maintaining governance at scale. Agentic AI has rightly become a pervasive topic, so it’s no surprise that one of the most intriguing industry developments I want to explore at the event is AI-powered data agents. Google Colab’s Data Science Agent , for example, reflects a shift from AI as merely a tool to AI as more of a collaborator.
In other words, it’s one thing to have a notebook that helps you write Python, but it’s another to have an AI assistant that suggests data exploration paths, highlights anomalies or generates insights based on goals you describe in plain language. That kind of functionality could significantly change how analysts and data scientists approach their work. I’ll be watching to see which industries Google highlights in its case studies.
Real-world applications — whether in healthcare, finance, manufacturing or logistics — will show how these assistants can deliver measurable improvements. For AI agents to succeed, they need context awareness and domain relevance, not just technical proficiency. In this connection, my colleague Jason Andersen focused on Google’s approach of highlighting the practical benefits of agentic AI in a recent analysis .
(If this is a particular area of interest for you, he’s also written lately about the different approaches to AI development frameworks among AWS Bedrock, Microsoft Azure AI Foundry and Google Vertex AI, plus the approaches being taken by new entrants into the agentic landscape including Nvidia and ServiceNow .) As Google Cloud Next 2025 approaches, my hope is that the event will indeed go beyond product announcements, offering a look at how the company is thinking about the future of cloud infrastructure, data management and AI development — and the specific roles these technologies may play across different industries. Google is positioning itself to address some of the most pressing technology challenges facing organizations today; now I want to see how Gemini-powered developer tools, sustainable generative AI infrastructure and so on are operating in the real world.
What I think is especially promising is Google’s emphasis on usability and accessibility. These developments signal a broader emphasis on democratizing advanced technology, making it usable for a wider audience. The question at Google Cloud Next 2025 and beyond will be whether these innovations will yield tangible results.
Will they truly lead to faster development cycles, improved decision making processes, reduced costs and more responsible AI practices? If Google can fulfill its promises, this year could mark a pivotal moment for its platform. Moor Insights & Strategy provides or has provided paid services to technology companies, like all tech industry research and analyst firms. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking and video and speaking sponsorships.
Of the companies mentioned in this article, Moor Insights & Strategy currently has (or has had) a paid business relationship with AWS, Google, IBM, Microsoft, Nvidia, Oracle and ServiceNow..
