NVIDIA and Global Industrial Software Giants Bring Design, Engineering and Manufacturing Into the AI Era
NVIDIA announced partnerships with Cadence, Dassault Systèmes, PTC, Siemens and Synopsys to bring CUDA-X, Omniverse and GPU-accelerated tools into industrial workflows. The effort targets faster design, engineering and factory optimization across major manufacturers.

Hyundai Motor, Kia and NVIDIA Expand Strategic Partnership for Next-Generation Autonomous Driving Technology
NVIDIA said Hyundai and Kia are expanding their work with the company around autonomous driving built on the DRIVE Hyperion platform. The announcement signals continued momentum for NVIDIA’s automotive stack in next-generation vehicle systems.

NVIDIA Announces Open Physical AI Data Factory Blueprint to Accelerate Robotics, Vision AI Agents and Autonomous Vehicle Development
NVIDIA introduced an open reference architecture designed to automate the way physical AI training data is generated, augmented and evaluated. The blueprint is meant to cut the cost and complexity of training robotics, vision and autonomous-driving systems.

Roche Scales NVIDIA AI Factories Globally to Accelerate Drug Discovery, Diagnostic Solutions and Manufacturing Breakthroughs
Roche is expanding its NVIDIA deployment to more than 3,500 Blackwell GPUs across global operations. NVIDIA framed the project as a major example of AI factories being used across R&D, diagnostics and manufacturing.

NVIDIA, T-Mobile and Partners Integrate Physical AI Applications on AI-RAN-Ready Infrastructure
NVIDIA and T-Mobile said they are working with Nokia and developers to bring physical AI applications onto distributed edge AI networks. The project points to telecom infrastructure becoming a platform for real-time AI services.

Adobe and NVIDIA Announce Strategic Partnership to Deliver the Next Generation of Firefly Models and Creative, Marketing and Agentic Workflows
Adobe and NVIDIA unveiled a broader alliance around AI-powered creation, production and personalization. The partnership includes work on future Firefly models and agentic workflows for creative and marketing use cases.

BYD, Geely, Isuzu and Nissan Adopt NVIDIA DRIVE Hyperion for Level 4 Vehicles
NVIDIA said adoption of DRIVE Hyperion is expanding with BYD, Geely, Isuzu and Nissan, alongside other mobility players. The company presented this as evidence of growing demand for scalable Level 4 autonomous vehicle platforms.

NVIDIA Ignites the Next Industrial Revolution in Knowledge Work With Open Agent Development Platform
NVIDIA launched an open agent development platform centered on the Agent Toolkit and OpenShell runtime. The goal is to help enterprises build and manage autonomous, self-evolving AI agents more safely and efficiently.

NVIDIA Launches Nemotron Coalition of Leading Global AI Labs to Advance Open Frontier Models
NVIDIA introduced the Nemotron Coalition, a new collaboration among open-model builders and AI developers. The initiative is designed to pool research, data, expertise and compute to accelerate frontier open-model development.

NVIDIA Expands Open Model Families to Power the Next Wave of Agentic, Physical and Healthcare AI
NVIDIA expanded its open model families with new releases aimed at developers and scientists building systems that can reason and act across enterprise, robotics and healthcare settings. The company is positioning open models as a foundation for broader AI deployment.

NVIDIA Announces NemoClaw for the OpenClaw Community
NVIDIA unveiled NemoClaw, a stack for the OpenClaw agent platform that lets users deploy Nemotron models and the OpenShell runtime with a single command. NVIDIA said the package adds privacy and security controls for more trustworthy autonomous agents.

NVIDIA Launches Space Computing, Rocketing AI Into Orbit
NVIDIA said its accelerated computing platforms are being extended to orbital data centers, geospatial intelligence and autonomous space operations. The announcement marks a push to bring data-center-class AI performance into space-constrained environments.

NVIDIA Releases Vera Rubin DSX AI Factory Reference Design and Omniverse DSX Digital Twin Blueprint With Broad Industry Support
NVIDIA released a reference design for Vera Rubin DSX AI factories and made its Omniverse DSX digital twin blueprint generally available. The company said the combination should help customers design and simulate large AI infrastructure before deployment.

NVIDIA Launches Vera CPU, Purpose-Built for Agentic AI
NVIDIA introduced the Vera CPU, which it described as the first processor purpose-built for agentic AI and reinforcement learning. The company claims the chip delivers higher efficiency and faster performance than traditional rack-scale CPUs.

NVIDIA Launches BlueField-4 STX Storage Architecture With Broad Industry Adoption
NVIDIA announced BlueField-4 STX, a modular storage architecture designed for long-context reasoning in agentic AI. The system is intended to help enterprises and cloud providers deploy accelerated storage infrastructure more easily.

NVIDIA Vera Rubin Opens Agentic AI Frontier
NVIDIA said the Vera Rubin platform is entering full production with seven new chips to scale large AI factories. The company is presenting Rubin as a key platform for the next stage of agentic AI infrastructure.

NVIDIA DLSS 5 Delivers AI-Powered Breakthrough in Visual Fidelity for Games
NVIDIA unveiled DLSS 5, calling it its biggest graphics breakthrough since real-time ray tracing. The company says the new version pushes image quality and performance further through AI-powered rendering.

From Simulation to Production: How to Build Robots With AI
In a GTC blog post, NVIDIA outlined how its latest open models, simulation tools and embedded compute are being combined to speed cloud-to-robot workflows. The message was that robotics development is moving toward a more integrated software-to-deployment pipeline.

More Than Meets the Eye: NVIDIA RTX-Accelerated Computers Now Connect Directly to Apple Vision Pro
NVIDIA said CloudXR 6.0 now integrates natively with visionOS, allowing RTX-powered simulators and professional 3D applications to connect directly to Apple Vision Pro. The announcement broadens NVIDIA’s position in immersive enterprise and design workflows.

NVIDIA, Telecom Leaders Build AI Grids to Optimize Inference on Distributed Networks
NVIDIA used a blog post to argue that telecom networks are becoming a new layer for distributed inference. The company highlighted operator activity in the U.S. and Asia as AI-native applications expand to more devices, users and agents.

GTC Spotlights NVIDIA RTX PCs and DGX Sparks Running Latest Open Models and AI Agents Locally
NVIDIA said GTC 2026 showcased a new category of “agent computers,” including RTX PCs and DGX systems running open models and AI agents locally. The pitch is that personal computing is shifting toward on-device generative and agentic AI.

Snap Decisions: How Open Libraries for Accelerated Data Processing Boost A/B Testing for Snapchat
NVIDIA highlighted Snap’s use of open NVIDIA data-processing libraries on Google Cloud to speed product development and experimentation. The post positions accelerated analytics as a way to make large-scale A/B testing faster and more efficient.

Smooth Moves: 90 Frames-Per-Second Virtual Reality Arrives on GeForce NOW
NVIDIA said GeForce NOW now supports 90 fps streaming for compatible VR headsets. The update is aimed at making cloud gaming smoother and more immersive for virtual reality users.

Image: NVIDIA

Speaking on this week’s episode of the Equity podcast, Mowry said the era of easy traction for simple LLM-based products is largely over. Startups that rely almost entirely on back-end models such as OpenAI’s GPT family, Anthropic’s Claude or Google DeepMind’s Gemini without building meaningful intellectual property of their own are unlikely to see durable growth.

The End of the “Slap a UI on GPT” Era

LLM wrappers — startups that package an existing large language model with a user interface or workflow to solve a specific problem — once attracted rapid user growth. In mid-2024, when OpenAI launched its ChatGPT store, it was possible to gain attention by layering a clean UX on top of a powerful foundation model.

But that window has narrowed.

“If you’re really just counting on the back-end model to do all the work and you’re almost white-labeling that model, the industry doesn’t have a lot of patience for that anymore,”

Mowry said. Wrapping “very thin intellectual property around Gemini or GPT-5” signals a lack of differentiation, he added.

AI Is Redefining Startup Economics, Says Microsoft VP

The implication is clear: sustainable AI startups must build deeper product value, proprietary data, or vertical specialization. Otherwise, they risk being commoditized as foundation models improve and expand their own feature sets.

Why AI Aggregators Are Under Pressure

A subset of wrappers — AI aggregators — may face even steeper challenges. These companies combine multiple large language models into a single interface or API layer, routing queries across providers while offering monitoring, governance or evaluation tools.

Examples include AI search startup Perplexity AI and developer platform OpenRouter, which gives developers access to multiple AI models through one API.

While such platforms have gained visibility, Mowry’s advice to incoming founders is direct:

“Stay out of the aggregator business.”

According to Mowry, aggregators are not seeing strong growth or progression because users increasingly expect built-in intellectual property — smart routing based on real understanding of context and need — rather than simple model switching driven by compute availability or access constraints.

In his view, startups must build “deep, wide moats that are either horizontally differentiated or something really specific to a vertical market” to progress and grow.

A Familiar Pattern from the Early Cloud Era

Mowry draws a parallel with the early days of cloud computing in the late 2000s and early 2010s, when Amazon Web Services began to take off. At the time, numerous startups emerged to resell AWS infrastructure, positioning themselves as simpler gateways that offered billing consolidation, tooling and support.

But as Amazon Web Services built its own enterprise tools and customers became more sophisticated in managing cloud services directly, many of those resellers were squeezed out. The companies that survived were those that added real, defensible services such as security, migration expertise or DevOps consulting.

AI aggregators today face similar margin pressure. As model providers expand into enterprise features — including governance, monitoring and orchestration — middle layers that do not offer strong differentiation risk being sidelined.

Where Mowry Sees Opportunity

Despite his warnings, Mowry remains bullish on several AI-driven categories.

One is the wave of “vibe coding” and AI-native developer platforms. In 2025, startups such as Replit, Lovable and Cursor— all Google Cloud customers, according to Mowry — attracted significant investment and customer traction. These companies go beyond basic model access, embedding AI deeply into developer workflows and building sticky ecosystems around their tools.

Cursor, for example, represents what Mowry considers a stronger type of LLM wrapper: a GPT-powered coding assistant tightly integrated into software development environments, with product depth that extends well beyond a simple interface layer.

He also points to vertical AI plays such as Harvey AI, which targets the legal industry with specialized functionality. These startups build domain-specific capabilities, workflows and datasets that create defensible advantages.

Mowry expects continued growth in direct-to-consumer AI as well. He highlights the opportunity for creative students to use Google’s AI video generator Veo to bring stories to life — an example of powerful generative tools moving into the hands of everyday users.

Beyond AI infrastructure and applications, he sees biotech and climate tech as having a moment. Both sectors are benefiting from increased venture investment and unprecedented access to large datasets, enabling startups to create value in ways that were previously impossible.

The Bar for AI Startups Is Rising

The broader message from Mowry is not that AI opportunity is shrinking — but that it is maturing. As foundation models become more capable and cloud providers move up the stack, superficial differentiation is no longer enough.

Startups that merely wrap existing LLMs without building proprietary technology, deep integrations or vertical expertise may struggle to justify their existence. Those that develop meaningful intellectual property, embed themselves into critical workflows or harness unique data, however, could still define the next generation of AI companies.

Material by Iva Abadjievа

Image: Darren Mowry’s LinkedIn profile

“Flink’s high performance, low latency, and strong community make it the best choice for developers to use for stream processing”, said Shaun Clowes, chief product officer at Confluent.

Data stream processing allows experts to use new data as it arrives, rather than working on older data that has already been processed.

“Stream processing allows organizations to transform raw streams of data into powerful insights”, added Shaun Clowes.

Confluent says Apache Flink is designed to help detect fraud. It can also manage a supply chain in real time.

How can be Apache Flink used?

“With Kafka and Flink fully integrated in a unified platform, Confluent removes the technical barriers and provides the necessary tools so organizations can focus on innovating instead of infrastructure management”, shared Shaun Clowes.

Typically, Apache Flink is used to maintain data. It supports the activity of data processing experts in case of overload. The company points out that when starting a business, customers will face challenges such as: upfront costs and maintenance efforts.

Apache Flink enables users to quickly launch a business through monitoring, security and management services provided by Confluent. Apache Flink is currently available through AWS, Google Cloud, and Microsoft Azure.

In the ever-evolving realm of software development, a healthy work culture can prove to be a game-changer. Google Cloud’s recent release, the 2023 Accelerate State of DevOps Report, underscores the manifold advantages that a healthy work culture bestows upon software teams. These advantages include the prevention of burnout, an uptick in productivity, and a surge in job satisfaction.

The report sheds light on a striking statistic: teams with a thriving work culture demonstrate a staggering 30% higher performance when compared to their counterparts lacking this crucial component.

Google’s study takes a deep dive into the factors that contribute to a conducive work culture. Metrics used for this evaluation encompass the equitable distribution of work within the team, flexibility, feelings of job security, organizational stability, the extent of knowledge sharing, an understanding of user needs, and the efficiency in addressing problems and opportunities.

Apart from the significance of a healthy work culture, the report also highlights the pivotal role of user-centricity in performance. Teams that prioritize meeting user needs are shown to exhibit a remarkable 40% increase in performance compared to those that neglect this essential aspect.”

“Our research shows that a user-centric approach to building applications and services is one of the strongest predictors of overall organizational performance. To improve performance, develop a deep understanding of your users and iteratively adjust and incorporate their feedback,” Google Cloud wrote in the report.

The three user focus metrics on which the report evaluates DevOps teams are:

– How well user needs are met
– how aligned the team is with meeting those needs
– how user feedback is prioritized

“Balanced teams demonstrate strength in delivery, operations, and organizational performance by focusing on the needs of users. Members of these teams benefit from a clear understanding of user needs and the ability to adjust plans based on user feedback,” the report states.

According to the report, function-focused teams are failing to achieve organizational excellence. These teams place a high priority on delivery performance to the detriment of both organizational performance and the wellbeing of the organization’s employees.

Leveraging Google Cloud’s customizable models and open generative AI infrastructure, GitLab will provide customers with AI-assisted features directly within the DevSecOps enterprise platform.

“Together with GitLab, we’ll be able to deliver generative AI functionality that empowers our joint customers to increase delivery velocity without sacrificing security.”

said June Yang, VP of Cloud AI and Industry Solutions at Google Cloud.

With generative AI support in Vertex AI, GitLab can tune Google’s foundation models with their own data, and leverage these models to deliver new generative AI powered experiences. Google Cloud allows customers to control their data with enterprise-grade capabilities such as data isolation, data protection, sovereignty, and compliance support.

“GitLab’s vision for generative AI is grounded in privacy, security, and transparency. Our partnership with Google Cloud enables GitLab to offer private and secure AI-powered features, while maintaining customer data in our cloud infrastructure,”

said David DeSanto, Chief Product Officer at GitLab.

With Vertex AI, and leveraging the Built with Google Cloud AI program, GitLab will be able to leverage Google’s foundation models to provide customers with AI-powered offerings within its cloud infrastructure. This allows GitLab to maintain its commitment to protecting user privacy by containing customer intellectual property and source code within GitLab’s cloud infrastructure.

GitLab plans to improve its customers’ DevSecOps workflow efficiency by 10x, by applying AI-assisted workflows to all users involved in delivering software value.

This first experimental feature leveraging Google Cloud’s generative AI models is called Explain this Vulnerability. This capability empowers companies to make security a cross-organizational effort, by providing users with a natural language description of vulnerabilities found in their code and a recommendation for how to resolve them at the time of detection. Explain this Vulnerability can be used by developers, as well as security and operations teams, allowing customers to stay secure while remaining efficient and improving speed to delivery.

The partnership will allow Google Cloud and Workspace developers to use Replit’s collaborative code editing platform that allows them to create and share apps online.

The announcement comes just a week after Github announced the release of Copilot X, an enhanced version of its artificial intelligence software development platform that uses the latest OpenAI GPT-4 model and extends Copilot’s capabilities by adding chat and voice features and allowing developers to get instant answers to project questions.

Replit’s Ghostwriter app will use Google’s language patterns to suggest code blocks, complete programs, and answer developers’ questions instantly. According to the company, more than 30% of the code written by developers using Ghostwriter is generated by artificial intelligence. The most sophisticated language models can generate complete programs with simple hints in natural language, allowing the creation of complete websites in just a few minutes and with no programming experience required.

Until language models are integrated into development environments, the future envisioned by Replit’s CEO, Amjad Masad, in which AI helps non-developers become developers, turns software engineers into hyper-productive “10X engineers,” and enables 1000X performance for complex software, remains out of reach.

The platform supports more than 50 programming languages and allows users to create apps and websites through any browser and device (including mobile). It facilitates collaboration and project sharing, as well as access to code execution containers.

Google software users will see the benefit of generative AI improvements in Workspace productivity apps.

Google said that generative AI would be able to do the following in Workspace:

– Draft, reply, summarise, and prioritize your Gmail
– Brainstorm, proofread, write, and rewrite in Docs
– Create auto-generate images, audio, and video in Slides
– Go from raw data to insights and analysis via auto-completion, formula generation, and contextual categorization in Sheets
– Generate new backgrounds and capture notes in Meet enable workflows for getting things done in Chat

The first set of features will aim to help users write documents or emails in Google Docs and Gmail.

With the “Help me write” feature, users can generate a draft document or email that can reproduce a variety of styles – including job postings or birthday invitations.

The “Rewrite” feature allows you to generate a polished document from structured data – for example, a bulleted list of important topics discussed in a meeting – using the “Formalize” option.

Selecting “Elaborate” will allow the generating AI to expand the information, “Shorten” will shorten it, and “Bulletise” will list the most important elements of the text.

Google stresses that it will allow users to retain control over the content by allowing them to accept or edit the changes that the AI has made to the original text.

The company will initially make these features available to testers in the US before rolling them out to the public.

Such a partnership makes it easier for all developers to create multiplayer experiences by offering two solutions from Unity Gaming Services (UGS) – game server hosting (Multiplay) and voice and text chat (Vivox) – on Google Cloud Marketplace.

Additionally, the companies announced that Google Ads and Display & Video 360 will soon be bidding on Unity LevelPlay, Unity’s mediated offering, as part of Google’s partner bidding program.

“Unity and Google are working together to provide developers with the most robust tools and infrastructure to build, launch, and grow games seamlessly at a global scale,”

said Jeff Collins, General Manager, Unity Gaming Services.

“The integration of Google demand into Unity LevelPlay and the expansion of our UGS solutions, Game Server Hosting and Voice and Text Chat to Google Cloud Marketplace serve as important milestones in this deepening partnership that aims to enable game creators to deliver on their creative vision and to achieve business success.”

Jeff Collins continued.

Unity and Google Cloud are making it easier for all developers to deliver multiplayer games. Game Server Hosting (Multiplay) and Unity’s Voice and Text Chat (Vivox) are the first products from Unity Gaming Services to be available on Google Cloud Marketplace.

“Our expanded partnership with Unity enables us to more easily deliver products like Game Server Hosting and Voice and Text Chat at scale, making building games with Unity even more seamless.”

said Thomas Kurian, CEO, Google Cloud.

Game Server Hosting is a dedicated game server hosting service that helps creators effectively scale their game to millions of players in minutes. Voice and Text Chat enable in-game player communications, which, according to the Unity 2022 Multiplayer Report, is the primary way players want to communicate.

Self-serve features were also recently added to both solutions, giving developers immediate access to foundational management tools that will accelerate multiplayer operations and deliver the best connected gaming experiences.

Reliable infrastructure is a critical requirement for cloud workloads. As a cloud architect, in order to design a reliable infrastructure for your workloads, you need to thoroughly understand the reliability capabilities of your chosen cloud provider.
The company also provides guidelines for evaluating reliability requirements for user-caused workloads and presents architectural recommendations for building and managing a reliable Google Cloud infrastructure.

Reliability Overview
An application or workload is reliable when it meets current availability and fault tolerance goals.

Availability (or uptime) is the percentage of time an application is usable. For example, for an application that has an availability target of 99.99%, the total downtime should not exceed 8.64 seconds over a 24-hour period.
Availability is sometimes measured as the proportion of requests that the application serves successfully during a given period. For example, for an application that has an availability goal of 99.99%, no more than ten requests can fail for every 100,000 requests received. Availability is often expressed as the number of nines in a percentage. For example, 99.99% availability is expressed as “4 nines”.

Depending on the purpose of the application, there may be different sets of metrics for how reliable the application is. Below are examples of such reliability metrics:

• For applications that serve content, availability, latency, and throughput are important reliability metrics.
• For databases and storage systems, the reliability metrics are latency, throughput, availability, and durability (how well the data is protected against loss or corruption).
• For big data and analytic workloads, such as data processing pipelines, consistent pipeline performance (throughput and latency) is essential to ensure the freshness of data products and is an important reliability metric. It indicates how much data can be processed and how long it takes the conveyor to go from ingesting data to processing it.
• For most applications, data correctness is an essential reliability metric.

Finally, Google provides more guidance with models and best practices for building scalable and resilient applications.

In the email, he categorically states that he takes full responsibility for the actions that led to this, as well as the decisions taken. The cuts affect multiple business units. According to the Wall Street Journal, Alphabet’s recruiting group and departments focused on areas “outside the company’s core business.” Some engineering and product teams are also affected, according to CNBC.

Alphabet has not yet provided any information, but according to The Information, among those fired there are people who have performed excellently and some have earned from 500 000 to 1 million dollars a year.

Under Prabhakar Raghavan, who was in charge of search, ads, commerce and other areas, they covered Google Cloud, Chrome, Android and search.

Google Cloud, in particular, is said to have fired “workers in strategy, recruiting and so-called go-to-market teams that offer marketers data, training and research to close deals.”

The good news is that laid-off employees will receive at least two months’ notice, 16 weeks’ severance pay and two additional weeks’ pay for each year they worked for Alphabet. The vesting period for their restricted stock will be shortened by at least 16 weeks. Alphabet also plans to offer six months of medical coverage, job placement assistance and immigration assistance. The reason for the cuts are cost cutting measures being taken to stay afloat in these difficult times.

This is another company that has come to this decision. Meta Platforms Inc, Twitter Inc. and Amazon.com Inc. are just a few examples of tech corporations that have also cut staff to keep operations going.

Tough economic times bring with them a not-so-pleasant trend of layoffs that is growing.