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Google’s Data Science Agent: Can It Really Do Your Job?

On March 3rd, Google officially rolled out its Data Science Agent to most Colab users for free. This is not something brand new — it was first announced in December last year, but it is now integrated into Colab and made widely accessible. Google says it is “The future of data analysis with Gemini”, stating: “Simply describe your analysis goals in plain language, and watch your notebook take shape automatically, helping accelerate your ability to conduct research and data analysis.” But is it a real game-changer in Data Science? What can it actually do, and what can’t it do? Is it ready to replace data analysts and data scientists? And what does it tell us about the future of data science careers? In this article, I will explore these questions with real-world examples. What It Can Do The Data Science Agent is straightforward to use: Open a new notebook in Google Colab — you just need a Google Account and can use Google Colab for free; Click “Analyze files with Gemini” — this will open the Gemini chat window on the right; Upload your data file and describe your goal in the chat. The agent will generate a series of tasks accordingly; Click “Execute Plan”, and Gemini will start to write the Jupyter Notebook automatically. Data Science Agent UI (image by author) Let’s look at a real example. Here, I used the dataset from the Regression with an Insurance Dataset Kaggle Playground Prediction Competition (Apache 2.0 license). This dataset has 20 features, and the goal is to predict the insurance premium amount. It has both continuous and categorical variables with scenarios like missing values and outliers. Therefore, it is a good example dataset for Machine Learning practices. Jupyter Notebook generated by the Data Science Agent (image by author) After running my experiment, here are the highlights I’ve observed from the Data Science Agent’s performance: Customizable execution plan: Based on my prompt of “Can you help me analyze how the factors impact insurance premium amount? “, the Data Science Agent first came up with a series of 10 tasks, including data loading, data exploration, data cleaning, data wrangling, feature engineering, data splitting, model training, model optimization, model evaluation, and data visualization. This is a pretty standard and reasonable process of conducting exploratory data analysis and building a machine learning model. It then asked for my confirmation and feedback before executing the plan. I tried to ask it to focus on Exploratory Data Analysis first, and it was able to adjust the execution plan accordingly. This provides flexibility to customize the plan based on your needs. Initial tasks the agent generated (image by author) Plan adjustment based on feedback (image by author) End-to-end execution and autocorrection: After confirming the plan, the Data Science Agent was able to execute the plan end-to-end autonomously. Whenever it encountered errors while running Python code, it diagnosed what was wrong and attempted to correct the error by itself. For example, at the model training step, it first ran into a DTypePromotionError error because of including a datetime column in training. It decided to drop the column in the next try but then got the error message ValueError: Input X contains NaN. In its third attempt, it added a simpleImputer to impute all missing values with the mean of each column and eventually got the step to work. The agent ran into an error and auto-corrected it (image by author) Interactive and iterative notebook: Since the Data Science Agent is built into Google Colab, it populates a Jupyter Notebook as it executes. This comes with several advantages: Real-time visibility: Firstly, you can actually watch the Python code running in real time, including the error messages and warnings. The dataset I provided was a bit large — even though I only kept the first 50k rows of the dataset for the sake of a quick test — and it took about 20 minutes to finish the model optimization step in the Jupyter notebook. The notebook kept running without timeout and I received a notification once it finished. Editable code: Secondly, you can edit the code on top of what the agent has built for you. This is something clearly better than the official Data Analyst GPT in ChatGPT, which also runs the code and shows the result, but you have to copy and paste the code elsewhere to make manual iterations. Seamless collaboration: Lastly, having a Jupyter Notebook makes it very easy to share your work with others — now you can collaborate with both AI and your teammates in the same environment. The agent also drafted step-by-step explanations and key findings, making it much more presentation-friendly. Summary section generated by the Agent (image by author) What It Cannot Do We’ve talked about its advantages; now, let’s discuss some missing pieces I’ve noticed for the Data Science Agent to be a real autonomous data scientist. It does not modify the Notebook based on follow-up prompts. I mentioned that the Jupyter Notebook environment makes it easy to iterate. In this example, after its initial execution, I noticed the Feature Importance charts did not have the feature labels. Therefore, I asked the Agent to add the labels. I assumed it would update the Python code directly or at least add a new cell with the refined code. However, it merely provided me with the revised code in the chat window, leaving the actual notebook update work to me. Similarly, when I asked it to add a new section with recommendations for lowering the insurance premium costs, it added a markdown response with its recommendation in the chatbot 🙁 Although copy-pasting the code or text isn’t a big deal for me, I still feel disappointed – once the notebook is generated in its first pass, all further interactions stay in the chat, just like ChatGPT. My follow-up on updating the feature importance chart (image by author) My follow-up on adding recommendations (image by author) It does not always choose the best data science approach. For this regression problem, it followed a reasonable workflow – data cleaning (handling missing values and outliers), data wrangling (one-hot encoding and log transformation), feature engineering (adding interaction features and other new features), and training and optimizing three models (Linear Regression, Random Forest, and Gradient Boosting Trees). However, when I looked into the details, I realized not all of its operations were necessarily the best practices. For example, it imputed missing values using the mean, which might not be a good idea for very skewed data and could impact correlations and relationships between variables. Also, we usually test many different feature engineering ideas and see how they impact the model’s performance. Therefore, while it sets up a solid foundation and framework, an experienced data scientist is still needed to refine the analysis and modeling. These are the two main limitations regarding the Data Science Agent’s performance in this experiment. But if we think about the whole data project pipeline and workflow, there are broader challenges in applying this tool to real-world projects: What is the goal of the project? This dataset is provided by Kaggle for a playground competition. Therefore, the project goal is well-defined. However, a data project at work could be pretty ambiguous. We often need to talk to many stakeholders to understand the business goal, and have many back and forth to make sure we stay on the right track. This is not something the Data Science Agent can handle for you. It requires a clear goal to generate its list of tasks. In other words, if you give it an incorrect problem statement, the output will be useless. How do we get the clean dataset with documentation? Our example dataset is relatively clean, with basic documentation. However, this usually does not happen in the industry. Every data scientist or data analyst has probably experienced the pain of talking to multiple people just to find one data point, solving the myth of some random columns with confusing names and putting together thousands of lines of SQL to prepare the dataset for analysis and modeling. This sometimes takes 50% of the actual work time. In that case, the Data Science Agent can only help with the start of the other 50% of the work (so maybe 10 to 20%). Who Are the Target Users With the pros and cons in mind, who are the target users of the Data Science Agent? Or who will benefit the most from this new AI tool? Here are my thoughts: Aspiring data scientists. Data Science is still a hot space with lots of beginners starting every day. Given that the agent “understands” the standard process and basic concepts well, it can provide invaluable guidance to those just getting started, setting up a great framework and explaining the techniques with working code. For example, many people tend to learn from participating in Kaggle competitions. Just like what I did here, they can ask the Data Science Agent to generate an initial notebook, then dig into each step to understand why the agent does certain things and what can be improved. People with clear data questions but limited coding skills. The key requirements here are 1. the problem is clearly defined and 2. the data task is standard (not as complicated as optimizing a predictive model with 20 columns).. Let me give you some scenarios: Many researchers need to run analyses on the datasets they collected. They usually have a data question clearly defined, which makes it easier for the Data Science Agent to assist. Moreover, researchers usually have a good understanding of the basic statistical methods but might not be as proficient in coding. So the Agent can save them the time of writing code, meanwhile, the researchers can judge the correctness of the methods AI used. This is the same use case Google mentioned when it first introduced the Data Science Agent: “For example, with the help of Data Science Agent, a scientist at Lawrence Berkeley National Laboratory working on a global tropical wetland methane emissions project has estimated their analysis and processing time was reduced from one week to five minutes.” Product managers often need to do some basic analysis themselves — they have to make data-driven decisions. They know their questions well (and often the potential answers), and they can pull some data from internal BI tools or with the help of engineers. For example, they might want to examine the correlation between two metrics or understand the trend of a time series. In that case, the Data Science Agent can help them conduct the analysis with the problem context and data they provided. Can It Replace Data Analysts and Data Scientists Yet? We finally come to the question that every data scientist or analyst cares about the most: Is it ready to replace us yet? The short answer is “No”. There are still major blockers for the Data Science Agent to be a real data scientist — it is missing the capabilities of modifying the Jupyter Notebook based on follow-up questions, it still requires someone with solid data science knowledge to audit the methods and make manual iterations, and it needs a clear data problem statement with clean and well-documented datasets. However, AI is a fast-evolving space with significant improvements constantly. Just looking at where it came from and where it stands now, here are some very important lessons for data professionals to stay competitive: AI is a tool that greatly improves productivity. Instead of worrying about being replaced by AI, it is better to embrace the benefits it brings and learn how it can improve your work efficiency. Don’t feel guilty if you use it to write basic code — no one remembers all the numpy and pandas syntax and scikit-learn models 🙂 Coding is a tool to complete complex statistical analysis quickly, and AI is a new tool to save you even more time. If your work is mostly repetitive tasks, then you are at risk. It is very clear that these AI agents are getting better and better at automating standard and basic data tasks. If your job today is mostly making basic visualizations, building standard dashboards, or doing simple regression analysis, then the day of AI automating your job might come sooner than you expected. Being a domain expert and a good communicator will set you apart. To make the AI tools work, you need to understand your domain well and be able to communicate and translate the business knowledge and problems to both your stakeholders and the AI tools. When it comes to machine learning, we always say “Garbage in, garbage out”. It is the same for an AI-assisted data project. Featured image generated by the author with Dall-E

On March 3rd, Google officially rolled out its Data Science Agent to most Colab users for free. This is not something brand new — it was first announced in December last year, but it is now integrated into Colab and made widely accessible.

Google says it is “The future of data analysis with Gemini”, stating: “Simply describe your analysis goals in plain language, and watch your notebook take shape automatically, helping accelerate your ability to conduct research and data analysis.” But is it a real game-changer in Data Science? What can it actually do, and what can’t it do? Is it ready to replace data analysts and data scientists? And what does it tell us about the future of data science careers?

In this article, I will explore these questions with real-world examples.

What It Can Do

The Data Science Agent is straightforward to use:

  1. Open a new notebook in Google Colab — you just need a Google Account and can use Google Colab for free;
  2. Click “Analyze files with Gemini” — this will open the Gemini chat window on the right;
  3. Upload your data file and describe your goal in the chat. The agent will generate a series of tasks accordingly;
  4. Click “Execute Plan”, and Gemini will start to write the Jupyter Notebook automatically.

Data Science Agent UI (image by author)

Let’s look at a real example. Here, I used the dataset from the Regression with an Insurance Dataset Kaggle Playground Prediction Competition (Apache 2.0 license). This dataset has 20 features, and the goal is to predict the insurance premium amount. It has both continuous and categorical variables with scenarios like missing values and outliers. Therefore, it is a good example dataset for Machine Learning practices.

Jupyter Notebook generated by the Data Science Agent (image by author)

After running my experiment, here are the highlights I’ve observed from the Data Science Agent’s performance:

  • Customizable execution plan: Based on my prompt of “Can you help me analyze how the factors impact insurance premium amount? “, the Data Science Agent first came up with a series of 10 tasks, including data loading, data exploration, data cleaning, data wrangling, feature engineering, data splitting, model training, model optimization, model evaluation, and data visualization. This is a pretty standard and reasonable process of conducting exploratory data analysis and building a machine learning model. It then asked for my confirmation and feedback before executing the plan. I tried to ask it to focus on Exploratory Data Analysis first, and it was able to adjust the execution plan accordingly. This provides flexibility to customize the plan based on your needs.

Initial tasks the agent generated (image by author)

Plan adjustment based on feedback (image by author)

  • End-to-end execution and autocorrection: After confirming the plan, the Data Science Agent was able to execute the plan end-to-end autonomously. Whenever it encountered errors while running Python code, it diagnosed what was wrong and attempted to correct the error by itself. For example, at the model training step, it first ran into a DTypePromotionError error because of including a datetime column in training. It decided to drop the column in the next try but then got the error message ValueError: Input X contains NaN. In its third attempt, it added a simpleImputer to impute all missing values with the mean of each column and eventually got the step to work.

The agent ran into an error and auto-corrected it (image by author)

  • Interactive and iterative notebook: Since the Data Science Agent is built into Google Colab, it populates a Jupyter Notebook as it executes. This comes with several advantages:
    • Real-time visibility: Firstly, you can actually watch the Python code running in real time, including the error messages and warnings. The dataset I provided was a bit large — even though I only kept the first 50k rows of the dataset for the sake of a quick test — and it took about 20 minutes to finish the model optimization step in the Jupyter notebook. The notebook kept running without timeout and I received a notification once it finished.
    • Editable code: Secondly, you can edit the code on top of what the agent has built for you. This is something clearly better than the official Data Analyst GPT in ChatGPT, which also runs the code and shows the result, but you have to copy and paste the code elsewhere to make manual iterations.
    • Seamless collaboration: Lastly, having a Jupyter Notebook makes it very easy to share your work with others — now you can collaborate with both AI and your teammates in the same environment. The agent also drafted step-by-step explanations and key findings, making it much more presentation-friendly.

Summary section generated by the Agent (image by author)

What It Cannot Do

We’ve talked about its advantages; now, let’s discuss some missing pieces I’ve noticed for the Data Science Agent to be a real autonomous data scientist.

  • It does not modify the Notebook based on follow-up prompts. I mentioned that the Jupyter Notebook environment makes it easy to iterate. In this example, after its initial execution, I noticed the Feature Importance charts did not have the feature labels. Therefore, I asked the Agent to add the labels. I assumed it would update the Python code directly or at least add a new cell with the refined code. However, it merely provided me with the revised code in the chat window, leaving the actual notebook update work to me. Similarly, when I asked it to add a new section with recommendations for lowering the insurance premium costs, it added a markdown response with its recommendation in the chatbot 🙁 Although copy-pasting the code or text isn’t a big deal for me, I still feel disappointed – once the notebook is generated in its first pass, all further interactions stay in the chat, just like ChatGPT.

My follow-up on updating the feature importance chart (image by author)

My follow-up on adding recommendations (image by author)

  • It does not always choose the best data science approach. For this regression problem, it followed a reasonable workflow – data cleaning (handling missing values and outliers), data wrangling (one-hot encoding and log transformation), feature engineering (adding interaction features and other new features), and training and optimizing three models (Linear Regression, Random Forest, and Gradient Boosting Trees). However, when I looked into the details, I realized not all of its operations were necessarily the best practices. For example, it imputed missing values using the mean, which might not be a good idea for very skewed data and could impact correlations and relationships between variables. Also, we usually test many different feature engineering ideas and see how they impact the model’s performance. Therefore, while it sets up a solid foundation and framework, an experienced data scientist is still needed to refine the analysis and modeling.

These are the two main limitations regarding the Data Science Agent’s performance in this experiment. But if we think about the whole data project pipeline and workflow, there are broader challenges in applying this tool to real-world projects:

  • What is the goal of the project? This dataset is provided by Kaggle for a playground competition. Therefore, the project goal is well-defined. However, a data project at work could be pretty ambiguous. We often need to talk to many stakeholders to understand the business goal, and have many back and forth to make sure we stay on the right track. This is not something the Data Science Agent can handle for you. It requires a clear goal to generate its list of tasks. In other words, if you give it an incorrect problem statement, the output will be useless.
  • How do we get the clean dataset with documentation? Our example dataset is relatively clean, with basic documentation. However, this usually does not happen in the industry. Every data scientist or data analyst has probably experienced the pain of talking to multiple people just to find one data point, solving the myth of some random columns with confusing names and putting together thousands of lines of SQL to prepare the dataset for analysis and modeling. This sometimes takes 50% of the actual work time. In that case, the Data Science Agent can only help with the start of the other 50% of the work (so maybe 10 to 20%).

Who Are the Target Users

With the pros and cons in mind, who are the target users of the Data Science Agent? Or who will benefit the most from this new AI tool? Here are my thoughts:

  1. Aspiring data scientists. Data Science is still a hot space with lots of beginners starting every day. Given that the agent “understands” the standard process and basic concepts well, it can provide invaluable guidance to those just getting started, setting up a great framework and explaining the techniques with working code. For example, many people tend to learn from participating in Kaggle competitions. Just like what I did here, they can ask the Data Science Agent to generate an initial notebook, then dig into each step to understand why the agent does certain things and what can be improved.
  2. People with clear data questions but limited coding skills. The key requirements here are 1. the problem is clearly defined and 2. the data task is standard (not as complicated as optimizing a predictive model with 20 columns).. Let me give you some scenarios:
    • Many researchers need to run analyses on the datasets they collected. They usually have a data question clearly defined, which makes it easier for the Data Science Agent to assist. Moreover, researchers usually have a good understanding of the basic statistical methods but might not be as proficient in coding. So the Agent can save them the time of writing code, meanwhile, the researchers can judge the correctness of the methods AI used. This is the same use case Google mentioned when it first introduced the Data Science Agent: “For example, with the help of Data Science Agent, a scientist at Lawrence Berkeley National Laboratory working on a global tropical wetland methane emissions project has estimated their analysis and processing time was reduced from one week to five minutes.”
    • Product managers often need to do some basic analysis themselves — they have to make data-driven decisions. They know their questions well (and often the potential answers), and they can pull some data from internal BI tools or with the help of engineers. For example, they might want to examine the correlation between two metrics or understand the trend of a time series. In that case, the Data Science Agent can help them conduct the analysis with the problem context and data they provided.

Can It Replace Data Analysts and Data Scientists Yet?

We finally come to the question that every data scientist or analyst cares about the most: Is it ready to replace us yet?

The short answer is “No”. There are still major blockers for the Data Science Agent to be a real data scientist — it is missing the capabilities of modifying the Jupyter Notebook based on follow-up questions, it still requires someone with solid data science knowledge to audit the methods and make manual iterations, and it needs a clear data problem statement with clean and well-documented datasets.

However, AI is a fast-evolving space with significant improvements constantly. Just looking at where it came from and where it stands now, here are some very important lessons for data professionals to stay competitive:

  1. AI is a tool that greatly improves productivity. Instead of worrying about being replaced by AI, it is better to embrace the benefits it brings and learn how it can improve your work efficiency. Don’t feel guilty if you use it to write basic code — no one remembers all the numpy and pandas syntax and scikit-learn models 🙂 Coding is a tool to complete complex statistical analysis quickly, and AI is a new tool to save you even more time.
  2. If your work is mostly repetitive tasks, then you are at risk. It is very clear that these AI agents are getting better and better at automating standard and basic data tasks. If your job today is mostly making basic visualizations, building standard dashboards, or doing simple regression analysis, then the day of AI automating your job might come sooner than you expected.

Being a domain expert and a good communicator will set you apart. To make the AI tools work, you need to understand your domain well and be able to communicate and translate the business knowledge and problems to both your stakeholders and the AI tools. When it comes to machine learning, we always say “Garbage in, garbage out”. It is the same for an AI-assisted data project.

Featured image generated by the author with Dall-E

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PEAK:AIO adds power, density to AI storage server

There is also the fact that many people working with AI are not IT professionals, such as professors, biochemists, scientists, doctors, clinicians, and they don’t have a traditional enterprise department or a data center. “It’s run by people that wouldn’t really know, nor want to know, what storage is,” he said. While the new AI Data Server is a Dell design, PEAK:AIO has worked with Lenovo, Supermicro, and HPE as well as Dell over the past four years, offering to convert their off the shelf storage servers into hyper fast, very AI-specific, cheap, specific storage servers that work with all the protocols at Nvidia, like NVLink, along with NFS and NVMe over Fabric. It also greatly increased storage capacity by going with 61TB drives from Solidigm. SSDs from the major server vendors typically maxed out at 15TB, according to the vendor. PEAK:AIO competes with VAST, WekaIO, NetApp, Pure Storage and many others in the growing AI workload storage arena. PEAK:AIO’s AI Data Server is available now.

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SoftBank to buy Ampere for $6.5B, fueling Arm-based server market competition

SoftBank’s announcement suggests Ampere will collaborate with other SBG companies, potentially creating a powerful ecosystem of Arm-based computing solutions. This collaboration could extend to SoftBank’s numerous portfolio companies, including Korean/Japanese web giant LY Corp, ByteDance (TikTok’s parent company), and various AI startups. If SoftBank successfully steers its portfolio companies toward Ampere processors, it could accelerate the shift away from x86 architecture in data centers worldwide. Questions remain about Arm’s server strategy The acquisition, however, raises questions about how SoftBank will balance its investments in both Arm and Ampere, given their potentially competing server CPU strategies. Arm’s recent move to design and sell its own server processors to Meta signaled a major strategic shift that already put it in direct competition with its own customers, including Qualcomm and Nvidia. “In technology licensing where an entity is both provider and competitor, boundaries are typically well-defined without special preferences beyond potential first-mover advantages,” Kawoosa explained. “Arm will likely continue making independent licensing decisions that serve its broader interests rather than favoring Ampere, as the company can’t risk alienating its established high-volume customers.” Industry analysts speculate that SoftBank might position Arm to focus on custom designs for hyperscale customers while allowing Ampere to dominate the market for more standardized server processors. Alternatively, the two companies could be merged or realigned to present a unified strategy against incumbents Intel and AMD. “While Arm currently dominates processor architecture, particularly for energy-efficient designs, the landscape isn’t static,” Kawoosa added. “The semiconductor industry is approaching a potential inflection point, and we may witness fundamental disruptions in the next 3-5 years — similar to how OpenAI transformed the AI landscape. SoftBank appears to be maximizing its Arm investments while preparing for this coming paradigm shift in processor architecture.”

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Nvidia, xAI and two energy giants join genAI infrastructure initiative

The new AIP members will “further strengthen the partnership’s technology leadership as the platform seeks to invest in new and expanded AI infrastructure. Nvidia will also continue in its role as a technical advisor to AIP, leveraging its expertise in accelerated computing and AI factories to inform the deployment of next-generation AI data center infrastructure,” the group’s statement said. “Additionally, GE Vernova and NextEra Energy have agreed to collaborate with AIP to accelerate the scaling of critical and diverse energy solutions for AI data centers. GE Vernova will also work with AIP and its partners on supply chain planning and in delivering innovative and high efficiency energy solutions.” The group claimed, without offering any specifics, that it “has attracted significant capital and partner interest since its inception in September 2024, highlighting the growing demand for AI-ready data centers and power solutions.” The statement said the group will try to raise “$30 billion in capital from investors, asset owners, and corporations, which in turn will mobilize up to $100 billion in total investment potential when including debt financing.” Forrester’s Nguyen also noted that the influence of two of the new members — xAI, owned by Elon Musk, along with Nvidia — could easily help with fundraising. Musk “with his connections, he does not make small quiet moves,” Nguyen said. “As for Nvidia, they are the face of AI. Everything they do attracts attention.” Info-Tech’s Bickley said that the astronomical dollars involved in genAI investments is mind-boggling. And yet even more investment is needed — a lot more.

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IBM broadens access to Nvidia technology for enterprise AI

The IBM Storage Scale platform will support CAS and now will respond to queries using the extracted and augmented data, speeding up the communications between GPUs and storage using Nvidia BlueField-3 DPUs and Spectrum-X networking, IBM stated. The multimodal document data extraction workflow will also support Nvidia NeMo Retriever microservices. CAS will be embedded in the next update of IBM Fusion, which is planned for the second quarter of this year. Fusion simplifies the deployment and management of AI applications and works with Storage Scale, which will handle high-performance storage support for AI workloads, according to IBM. IBM Cloud instances with Nvidia GPUs In addition to the software news, IBM said its cloud customers can now use Nvidia H200 instances in the IBM Cloud environment. With increased memory bandwidth (1.4x higher than its predecessor) and capacity, the H200 Tensor Core can handle larger datasets, accelerating the training of large AI models and executing complex simulations, with high energy efficiency and low total cost of ownership, according to IBM. In addition, customers can use the power of the H200 to process large volumes of data in real time, enabling more accurate predictive analytics and data-driven decision-making, IBM stated. IBM Consulting capabilities with Nvidia Lastly, IBM Consulting is adding Nvidia Blueprint to its recently introduced AI Integration Service, which offers customers support for developing, building and running AI environments. Nvidia Blueprints offer a suite pre-validated, optimized, and documented reference architectures designed to simplify and accelerate the deployment of complex AI and data center infrastructure, according to Nvidia.  The IBM AI Integration service already supports a number of third-party systems, including Oracle, Salesforce, SAP and ServiceNow environments.

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Nvidia’s silicon photonics switches bring better power efficiency to AI data centers

Nvidia typically uses partnerships where appropriate, and the new switch design was done in collaboration with multiple vendors across different aspects, including creating the lasers, packaging, and other elements as part of the silicon photonics. Hundreds of patents were also included. Nvidia will licensing the innovations created to its partners and customers with the goal of scaling this model. Nvidia’s partner ecosystem includes TSMC, which provides advanced chip fabrication and 3D chip stacking to integrate silicon photonics into Nvidia’s hardware. Coherent, Eoptolink, Fabrinet, and Innolight are involved in the development, manufacturing, and supply of the transceivers. Additional partners include Browave, Coherent, Corning Incorporated, Fabrinet, Foxconn, Lumentum, SENKO, SPIL, Sumitomo Electric Industries, and TFC Communication. AI has transformed the way data centers are being designed. During his keynote at GTC, CEO Jensen Huang talked about the data center being the “new unit of compute,” which refers to the entire data center having to act like one massive server. That has driven compute to be primarily CPU based to being GPU centric. Now the network needs to evolve to ensure data is being fed to the GPUs at a speed they can process the data. The new co-packaged switches remove external parts, which have historically added a small amount of overhead to networking. Pre-AI this was negligible, but with AI, any slowness in the network leads to dollars being wasted.

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Critical vulnerability in AMI MegaRAC BMC allows server takeover

“In disruptive or destructive attacks, attackers can leverage the often heterogeneous environments in data centers to potentially send malicious commands to every other BMC on the same management segment, forcing all devices to continually reboot in a way that victim operators cannot stop,” the Eclypsium researchers said. “In extreme scenarios, the net impact could be indefinite, unrecoverable downtime until and unless devices are re-provisioned.” BMC vulnerabilities and misconfigurations, including hardcoded credentials, have been of interest for attackers for over a decade. In 2022, security researchers found a malicious implant dubbed iLOBleed that was likely developed by an APT group and was being deployed through vulnerabilities in HPE iLO (HPE’s Integrated Lights-Out) BMC. In 2018, a ransomware group called JungleSec used default credentials for IPMI interfaces to compromise Linux servers. And back in 2016, Intel’s Active Management Technology (AMT) Serial-over-LAN (SOL) feature which is part of Intel’s Management Engine (Intel ME), was exploited by an APT group as a covert communication channel to transfer files. OEM, server manufacturers in control of patching AMI released an advisory and patches to its OEM partners, but affected users must wait for their server manufacturers to integrate them and release firmware updates. In addition to this vulnerability, AMI also patched a flaw tracked as CVE-2024-54084 that may lead to arbitrary code execution in its AptioV UEFI implementation. HPE and Lenovo have already released updates for their products that integrate AMI’s patch for CVE-2024-54085.

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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.

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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

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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

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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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