Introducing Gemma 3n: The developer guide

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Introducing Gemma 3n: The developer guide

The first Gemma model launched early last year and has since grown into a thriving Gemmaverse of over 160 million collective downloads. This ecosystem includes our family of over a dozen specialized models for everything from safeguarding to medical applications and, most inspiringly, the countless innovations from the community. From innovators like Roboflow building enterprise computer vision to the Institute of Science Tokyo creating highly-capable Japanese Gemma variants, your work has shown us the path forward.Building on this incredible momentum, we’re excited to announce the full release of Gemma 3n. While last month’s preview offered a glimpse, today unlocks the full power of this mobile-first architecture. Gemma 3n is designed for the developer community that helped shape Gemma. It’s supported by your favorite tools including Hugging Face Transformers, llama.cpp, Google AI Edge, Ollama, MLX, and many others, enabling you to fine-tune and deploy for your specific on-device applications with ease. This post is the developer deep dive: we’ll explore some of the innovations behind Gemma 3n, share new benchmark results, and show you how to start building today.What’s new in Gemma 3n?Gemma 3n represents a major advancement for on-device AI, bringing powerful multimodal capabilities to edge devices with performance previously only seen in last year’s cloud-based frontier models. Achieving this leap in on-device performance required rethinking the model from the ground up. The foundation is Gemma 3n’s unique mobile-first architecture, and it all starts with MatFormer.MatFormer: One model, many sizesAt the core of Gemma 3n is the MatFormer (🪆Matryoshka Transformer) architecture, a novel nested transformer built for elastic inference. Think of it like Matryoshka dolls: a larger model contains smaller, fully functional versions of itself. This approach extends the concept of Matryoshka Representation Learning from just embeddings to all transformer components. During the MatFormer training of the 4B effective parameter (E4B) model, a 2B effective parameter (E2B) sub-model is simultaneously optimized within it, as shown in the figure above. This provides developers two powerful capabilities and use cases today:1: Pre-extracted models: You can directly download and use either the main E4B model for the highest capabilities, or the standalone E2B sub-model which we have already extracted for you, offering up to 2x faster inference.2: Custom sizes with Mix-n-Match: For more granular control tailored to specific hardware constraints, you can create a spectrum of custom-sized models between E2B and E4B using a method we call Mix-n-Match. This technique allows you to precisely slice the E4B model’s parameters, primarily by adjusting the feed forward network hidden dimension per layer (from 8192 to 16384) and selectively skipping some layers. We are releasing the MatFormer Lab, a tool that shows how to retrieve these optimal models, which were identified by evaluating various settings on benchmarks like MMLU. MMLU scores for the pre-trained Gemma 3n checkpoints at different model sizes (using Mix-n-Match) Looking ahead, the MatFormer architecture also paves the way for elastic execution. While not part of today’s launched implementations, this capability allows a single deployed E4B model to dynamically switch between E4B and E2B inference paths on the fly, enabling real-time optimization of performance and memory usage based on the current task and device load.Per-Layer Embeddings (PLE): Unlocking more memory efficiencyGemma 3n models incorporate Per-Layer Embeddings (PLE). This innovation is tailored for on-device deployment as it dramatically improves model quality without increasing the high-speed memory footprint required on your device’s accelerator (GPU/TPU).While the Gemma 3n E2B and E4B models have a total parameter count of 5B and 8B respectively, PLE allows a significant portion of these parameters (the embeddings associated with each layer) to be loaded and computed efficiently on the CPU. This means only the core transformer weights (approximately 2B for E2B and 4B for E4B) need to sit in the typically more constrained accelerator memory (VRAM). With Per-Layer Embeddings, you can use Gemma 3n E2B while only having ~2B parameters loaded in your accelerator. KV Cache sharing: Faster long-context processingProcessing long inputs, such as the sequences derived from audio and video streams, is essential for many advanced on-device multimodal applications. Gemma 3n introduces KV Cache Sharing, a feature designed to significantly accelerate time-to-first-token for streaming response applications.KV Cache Sharing optimizes how the model handles the initial input processing stage (often called the “prefill” phase). The keys and values of the middle layer from local and global attention are directly shared with all the top layers, delivering a notable 2x improvement on prefill performance compared to Gemma 3 4B. This means the model can ingest and understand lengthy prompt sequences much faster than before.Audio understanding: Introducing speech to text and translationGemma 3n uses an advanced audio encoder based on the Universal Speech Model (USM). The encoder generates a token for every 160ms of audio (about 6 tokens per second), which are then integrated as input to the language model, providing a granular representation of the sound context.This integrated audio capability unlocks key features for on-device development, including:Automatic Speech Recognition (ASR): Enable high-quality speech-to-text transcription directly on the device.Automatic Speech Translation (AST): Translate spoken language into text in another language.We’ve observed particularly strong AST results for translation between English and Spanish, French, Italian, and Portuguese, offering great potential for developers targeting applications in these languages. For tasks like speech translation, leveraging Chain-of-Thought prompting can significantly enhance results. Here’s an example: user Transcribe the following speech segment in Spanish, then translate it into English: model Plain text At launch time, the Gemma 3n encoder is implemented to process audio clips up to 30 seconds. However, this is not a fundamental limitation. The underlying audio encoder is a streaming encoder, capable of processing arbitrarily long audios with additional long form audio training. Follow-up implementations will unlock low-latency, long streaming applications.MobileNet-V5: New state-of-the-art vision encoderAlongside its integrated audio capabilities, Gemma 3n features a new, highly efficient vision encoder, MobileNet-V5-300M, delivering state-of-the-art performance for multimodal tasks on edge devices.Designed for flexibility and power on constrained hardware, MobileNet-V5 gives developers:Multiple input resolutions: Natively supports resolutions of 256×256, 512×512, and 768×768 pixels, allowing you to balance performance and detail for your specific applications.Broad visual understanding: Co-trained on extensive multimodal datasets, it excels at a wide range of image and video comprehension tasks.High throughput: Processes up to 60 frames per second on a Google Pixel, enabling real-time, on-device video analysis and interactive experiences.This level of performance is achieved with multiple architectural innovations, including:An advanced foundation of MobileNet-V4 blocks (including Universal Inverted Bottlenecks and Mobile MQA).A significantly scaled up architecture, featuring a hybrid, deep pyramid model that is 10x larger than the biggest MobileNet-V4 variant.A novel Multi-Scale Fusion VLM adapter that enhances the quality of tokens for better accuracy and efficiency.Benefiting from novel architectural designs and advanced distillation techniques, MobileNet-V5-300M substantially outperforms the baseline SoViT in Gemma 3 (trained with SigLip, no distillation). On a Google Pixel Edge TPU, it delivers a 13x speedup with quantization (6.5x without), requires 46% fewer parameters, and has a 4x smaller memory footprint, all while providing significantly higher accuracy on vision-language tasksWe’re excited to share more about the work behind this model. Look out for our upcoming MobileNet-V5 technical report, which will deep dive into the model architecture, data scaling strategies, and advanced distillation techniques.Making Gemma 3n accessible from day one has been a priority. We’re proud to partner with many incredible open source developers to ensure broad support across popular tools and platforms, including contributions from teams behind AMD, Axolotl, Docker, Hugging Face, llama.cpp, LMStudio, MLX, NVIDIA, Ollama, RedHat, SGLang, Unsloth, and vLLM.But this ecosystem is just the beginning. The true power of this technology is in what you will build with it. That’s why we’re launching the Gemma 3n Impact Challenge. Your mission: use Gemma 3n’s unique on-device, offline, and multimodal capabilities to build a product for a better world. With $150,000 in prizes, we’re looking for a compelling video story and a “wow” factor demo that shows real-world impact. Join the challenge and help build a better future.Get started with Gemma 3n todayReady to explore the potential of Gemma 3n today? Here’s how:Experiment directly: Use Google AI Studio to try Gemma 3n in just a couple of clicks. Gemma models can also be deployed directly to Cloud Run from AI Studio.Learn & integrate: Dive into our comprehensive documentation to quickly integrate Gemma into your projects or start with our inference and fine-tuning guides.

Building on this incredible momentum, we’re excited to announce the full release of Gemma 3n. While last month’s preview offered a glimpse, today unlocks the full power of this mobile-first architecture. Gemma 3n is designed for the developer community that helped shape Gemma. It’s supported by your favorite tools including Hugging Face Transformers, llama.cpp, Google AI Edge, Ollama, MLX, and many others, enabling you to fine-tune and deploy for your specific on-device applications with ease. This post is the developer deep dive: we’ll explore some of the innovations behind Gemma 3n, share new benchmark results, and show you how to start building today.

What’s new in Gemma 3n?

Gemma 3n represents a major advancement for on-device AI, bringing powerful multimodal capabilities to edge devices with performance previously only seen in last year’s cloud-based frontier models.

Achieving this leap in on-device performance required rethinking the model from the ground up. The foundation is Gemma 3n’s unique mobile-first architecture, and it all starts with MatFormer.

MatFormer: One model, many sizes

At the core of Gemma 3n is the MatFormer (🪆Matryoshka Transformer) architecture, a novel nested transformer built for elastic inference. Think of it like Matryoshka dolls: a larger model contains smaller, fully functional versions of itself. This approach extends the concept of Matryoshka Representation Learning from just embeddings to all transformer components.

During the MatFormer training of the 4B effective parameter (E4B) model, a 2B effective parameter (E2B) sub-model is simultaneously optimized within it, as shown in the figure above. This provides developers two powerful capabilities and use cases today:

1: Pre-extracted models: You can directly download and use either the main E4B model for the highest capabilities, or the standalone E2B sub-model which we have already extracted for you, offering up to 2x faster inference.

2: Custom sizes with Mix-n-Match: For more granular control tailored to specific hardware constraints, you can create a spectrum of custom-sized models between E2B and E4B using a method we call Mix-n-Match. This technique allows you to precisely slice the E4B model’s parameters, primarily by adjusting the feed forward network hidden dimension per layer (from 8192 to 16384) and selectively skipping some layers. We are releasing the MatFormer Lab, a tool that shows how to retrieve these optimal models, which were identified by evaluating various settings on benchmarks like MMLU.

MMLU scores for the pre-trained Gemma 3n checkpoints at different model sizes (using Mix-n-Match)

Looking ahead, the MatFormer architecture also paves the way for elastic execution. While not part of today’s launched implementations, this capability allows a single deployed E4B model to dynamically switch between E4B and E2B inference paths on the fly, enabling real-time optimization of performance and memory usage based on the current task and device load.

Per-Layer Embeddings (PLE): Unlocking more memory efficiency

Gemma 3n models incorporate Per-Layer Embeddings (PLE). This innovation is tailored for on-device deployment as it dramatically improves model quality without increasing the high-speed memory footprint required on your device’s accelerator (GPU/TPU).

While the Gemma 3n E2B and E4B models have a total parameter count of 5B and 8B respectively, PLE allows a significant portion of these parameters (the embeddings associated with each layer) to be loaded and computed efficiently on the CPU. This means only the core transformer weights (approximately 2B for E2B and 4B for E4B) need to sit in the typically more constrained accelerator memory (VRAM).

With Per-Layer Embeddings, you can use Gemma 3n E2B while only having ~2B parameters loaded in your accelerator.

Processing long inputs, such as the sequences derived from audio and video streams, is essential for many advanced on-device multimodal applications. Gemma 3n introduces KV Cache Sharing, a feature designed to significantly accelerate time-to-first-token for streaming response applications.

KV Cache Sharing optimizes how the model handles the initial input processing stage (often called the “prefill” phase). The keys and values of the middle layer from local and global attention are directly shared with all the top layers, delivering a notable 2x improvement on prefill performance compared to Gemma 3 4B. This means the model can ingest and understand lengthy prompt sequences much faster than before.

Audio understanding: Introducing speech to text and translation

Gemma 3n uses an advanced audio encoder based on the Universal Speech Model (USM). The encoder generates a token for every 160ms of audio (about 6 tokens per second), which are then integrated as input to the language model, providing a granular representation of the sound context.

This integrated audio capability unlocks key features for on-device development, including:

Automatic Speech Recognition (ASR): Enable high-quality speech-to-text transcription directly on the device.

Automatic Speech Translation (AST): Translate spoken language into text in another language.

We’ve observed particularly strong AST results for translation between English and Spanish, French, Italian, and Portuguese, offering great potential for developers targeting applications in these languages. For tasks like speech translation, leveraging Chain-of-Thought prompting can significantly enhance results. Here’s an example:

user
Transcribe the following speech segment in Spanish, then translate it into English: 

model

Plain text

At launch time, the Gemma 3n encoder is implemented to process audio clips up to 30 seconds. However, this is not a fundamental limitation. The underlying audio encoder is a streaming encoder, capable of processing arbitrarily long audios with additional long form audio training. Follow-up implementations will unlock low-latency, long streaming applications.

MobileNet-V5: New state-of-the-art vision encoder

Alongside its integrated audio capabilities, Gemma 3n features a new, highly efficient vision encoder, MobileNet-V5-300M, delivering state-of-the-art performance for multimodal tasks on edge devices.

Designed for flexibility and power on constrained hardware, MobileNet-V5 gives developers:

Multiple input resolutions: Natively supports resolutions of 256×256, 512×512, and 768×768 pixels, allowing you to balance performance and detail for your specific applications.

Broad visual understanding: Co-trained on extensive multimodal datasets, it excels at a wide range of image and video comprehension tasks.

High throughput: Processes up to 60 frames per second on a Google Pixel, enabling real-time, on-device video analysis and interactive experiences.

This level of performance is achieved with multiple architectural innovations, including:

An advanced foundation of MobileNet-V4 blocks (including Universal Inverted Bottlenecks and Mobile MQA).

A significantly scaled up architecture, featuring a hybrid, deep pyramid model that is 10x larger than the biggest MobileNet-V4 variant.

A novel Multi-Scale Fusion VLM adapter that enhances the quality of tokens for better accuracy and efficiency.

Benefiting from novel architectural designs and advanced distillation techniques, MobileNet-V5-300M substantially outperforms the baseline SoViT in Gemma 3 (trained with SigLip, no distillation). On a Google Pixel Edge TPU, it delivers a 13x speedup with quantization (6.5x without), requires 46% fewer parameters, and has a 4x smaller memory footprint, all while providing significantly higher accuracy on vision-language tasks

We’re excited to share more about the work behind this model. Look out for our upcoming MobileNet-V5 technical report, which will deep dive into the model architecture, data scaling strategies, and advanced distillation techniques.

Making Gemma 3n accessible from day one has been a priority. We’re proud to partner with many incredible open source developers to ensure broad support across popular tools and platforms, including contributions from teams behind AMD, Axolotl, Docker, Hugging Face, llama.cpp, LMStudio, MLX, NVIDIA, Ollama, RedHat, SGLang, Unsloth, and vLLM.

But this ecosystem is just the beginning. The true power of this technology is in what you will build with it. That’s why we’re launching the Gemma 3n Impact Challenge. Your mission: use Gemma 3n’s unique on-device, offline, and multimodal capabilities to build a product for a better world. With $150,000 in prizes, we’re looking for a compelling video story and a “wow” factor demo that shows real-world impact. Join the challenge and help build a better future.

Get started with Gemma 3n today

Ready to explore the potential of Gemma 3n today? Here’s how:

Experiment directly: Use Google AI Studio to try Gemma 3n in just a couple of clicks. Gemma models can also be deployed directly to Cloud Run from AI Studio.

Learn & integrate: Dive into our comprehensive documentation to quickly integrate Gemma into your projects or start with our inference and fine-tuning guides.

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The data center industry has spent the past several years announcing the future. Gigawatts. AI factories. New regions. New power architectures. Campuses at a scale that would have seemed extraordinary before generative AI reset the industry’s expectations. Now the public has entered the room. Communities are asking harder questions about who pays for electrical infrastructure, where the water comes from, how much noise reaches neighboring properties and what remains locally after construction crews leave. Utilities are being pressed to protect ratepayers from speculative load and costly system upgrades. Elected officials who once treated data centers primarily as economic-development wins are finding that the politics have changed. The defining question is no longer whether demand is real. It is whether the data center industry can keep earning the permission required to build at the scale it has promised. I mean permission in a broader sense than zoning approval, an environmental permit or a signed utility agreement. I mean the political and social room to develop infrastructure measured in hundreds of megawatts and billions of dollars—often in places whose residents have only recently begun to understand what is being proposed around them. That room is narrowing. A Different Kind of Constraint On July 18, opponents organized 142 demonstrations across 42 states in what Reuters described as the first coordinated national protest against the data center buildout. The movement crossed familiar political boundaries, bringing together environmental advocates, rural landowners and residents concerned about power prices, water, noise and the pace of development. A June Reuters/Ipsos poll found that 57% of respondents would oppose a data center in their community. Only 14% said they would be comfortable with one nearby. Those findings deserve the industry’s full attention. New York has imposed a one-year pause on certain environmental approvals for new hyperscale data centers while

NVIDIA’s Reported $50B Lease and the Nuclear-Powered AI Factory

Aalo and Crusoe Pursue the Nuclear-Powered AI Factory The Aalo-Crusoe partnership addresses the industry’s power problem by bringing power generation directly to the compute. In this case, skipping intermediary power stages such as minimal grid or custom BTM gas turbine solutions and going straight to nuclear. Aalo Atomics and Crusoe said they plan to deploy a Crusoe Spark modular data center running Crusoe Cloud at Idaho National Laboratory in 2027. The proof-of-concept project is intended to demonstrate an AI workload operating on power from an Aalo advanced reactor. Crusoe continues to expand their other data center campus projects. The companies then intend to deploy Aalo Pods, Aalo’s 50-megawatt-electric nuclear power plants, at Crusoe data centers by the end of 2029. Aalo has already begun work on a second reactor beside its initial test unit at the Idaho site. That reactor is expected to produce electricity for the Crusoe installation. On July 4, 2026, Aalo’s zero-power Critical Test Reactor reached criticality, sustaining a nuclear chain reaction without generating commercial electricity. The test reactor contains a full-scale core and components analogous to those planned for the 10-megawatt-electric Aalo-X power reactor being built next door, but it operates before sodium coolant and electricity-generating systems are added. Aalo plans to continue experiments with the Critical Test Reactor to refine its reactor-physics models, characterize control behavior and generate data supporting development and licensing of the full-power Aalo-X system. Advanced nuclear announcements sometimes blur the line between a successful test, an electricity-producing demonstration and a commercially licensed fleet. Aalo has achieved an important technical milestone, but substantial work remains before reactors can be manufactured, licensed, financed and operated at commercial data center sites. The pairing with Crusoe should be noted because it connects a reactor developer with a company that can provide the data center load,

Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist CFD Engineer – Data Center Mechanical DesignNew York, NY (remote)This position is also available as a remote role anywhere in the US in addition to key markets such as Cedar Rapids, IA; Kansas City, CA or White Plains, NY. Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, and sustainable design expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Agent – Data CentersColumbus, OH (limited travel) Non-traveling CxA positions available in: Indianapolis, IN; Cedar Rapids, IA; Phoenix, AZ; Atlanta, GA and Austin, TX. Traveling CxA based really near any major airport, otherwise traveling to: New York, NY; White Plains, NY; Dallas, TX; Richmond, VA; Montvale, NJ; Charlotte, NC; Salt Lake City, UT; Kansas City, MO; Chesterton, IN or Chicago, IL. ***Also looking for a lead EE and ME CxA Agents and CxA PMs.*** This opportunity is with a leading EPC company of data center design / build / commissioning solutions. This company provides a complete life cycle of solutions that are custom-fit to the requirements of their client’s mission-critical facilities. This opportunity provides a career-growth minded role with exciting projects with leading-edge technology

Navigating Virginia’s Data Center Boom: Policy Shifts, Local Projects, and Future Challenges

Virginia’s newest high profile data center story is no longer the announcement of the next generation AI data center campus, it is now how the state is beginning to set the trend for legislative process to protect its communities while still encouraging the data center industry development. On August 3, state Senators Richard Stuart, a Republican, and Russet Perry, a Democrat, called on Gov. Abigail Spanberger to convene a special legislative session to address groundwater strain. Their request followed a state study warning that eastern Virginia’s groundwater supply is constrained and that large new industrial withdrawals may be difficult to sustain. The debate has expanded into calls for a broader pause: Senator Glen Sturtevant has asked for an immediate statewide moratorium on new data center development, while Senate President Pro Tempore Louise Lucas has said such a moratorium deserves serious consideration. Those proposals are not yet law, but they are the clearest indication that Virginia’s policy discussion has moved beyond incremental regulation. The Commonwealth spent years treating data centers primarily as an economic-development and tax-base success. It is now evaluating them simultaneously as power, water, land-use, air-quality and ratepayer issues. That shift is especially important for projects outside Northern Virginia, where developers are increasingly pursuing large sites in communities with less experience reviewing hyperscale infrastructure. The calls for a special session arrive only weeks after a significant package of data center laws and budget provisions took effect July 1. Virginia’s new budget established what the administration describes as a first-of-its-kind electricity consumption tax on data centers. The charge is 1.1 cents per kilowatt-hour, began July 1 and is capped at $600 million in annual collections, with excess revenue refunded to data center taxpayers. The compromise preserved Virginia’s sales-and-use-tax exemption for qualifying data center equipment, avoiding the abrupt repeal sought by

Land and Expand: The Gigawatt Credibility Test

The midsummer wave of U.S. data center development is not defined by a single market, developer or technology company. It stretches from the Georgia coast to West Texas, from the industrial Midwest to the Mississippi River. What links the projects announced since early June is not just their scale, it is the realization that scale alone is not enough. Developers are still announcing multibillion-dollar campuses and gigawatt power requirements, but the language surrounding those announcements has changed. Companies are emphasizing who will pay for new generation and transmission, how cooling systems will limit water consumption, what communities will receive beyond temporary construction employment, and when contracted customers will begin occupying capacity. In several cases, the announcement is less about acquiring land than proving that a project has become commercially and electrically credible. As we have seen progressing through the industry, the latest announcements point toward campuses that combine compute, power, financing and community agreements in one development package. OpenAI Goes Direct in Georgia OpenAI, on July 22 disclosed Project Camellia, a long-term data center development in Effingham County, Georgia. OpenAI said it is designing and developing the campus itself and has contracted with Georgia Power for 3.2 gigawatts of electricity, to be delivered in phases from 2028 through 2032. The project has been reported as a roughly $20 billion investment on approximately 1,400 acres, making it one of the largest individual data center proposals currently moving through the U.S. pipeline. Project Camellia is notable not only for its size but for OpenAI’s more direct role. The company has traditionally secured capacity through cloud providers and infrastructure partners. By taking responsibility for designing and developing the Georgia campus, OpenAI is signaling that control over power, schedule and facility design has become strategically important as AI companies compete for increasingly scarce large-scale

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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