Developers love meritocracy. Software engineering professionals don’t judge individuals by the way they look, the way they dress and whether or not they use a purple-green hair dye rinse (spoiler alert, it’s actually considered a good thing)… and they never have. They tend to classify their counterparts and contemporaries on the basis of their skillset, their ability to show technical competency and their enthusiasm for the combined arts of coding and data science.

If there’s one chink in that argument, it’s a possible hierarchy between the developer community and the operations team. While the developers get to build, program and create, the Ops team are assigned the responsibility to underpin, maintain and manage. Some developers occasionally regard the sysadmins, database administrators and testing team as less skilled; the rise of DevOps has sought to unite these two streams, and platform engineering is also aiming to create and reinforce bonds, but fractures inevitably exist.

Agentic Administrators

Could a new wave of agentic AI services in the data management space actually help elevate the status of this essential function and, just maybe, actually help elevate the status of this role to the tier it deserves?

Lithuania-based tech writer Jastra Kranjec says we’re on the cusp. Citing the multiplicity of management consultancy reports in this space that suggest AI agents are about to really start helping us work (Capgemini’s Top Tech Trends of 2025 survey points to their use to boost efficiency and develop automation), Kranjec says that AI agents have now “evolved from experimental tools” into mainstream business solutions.

“Last year, even major enterprises like OpenAI, Google DeepMind, Microsoft and PwC began integrating them into their operations, proving them as one of the top AI trends. Moreover, this is just the beginning of AI agents’ growth, with market projections showing a surging adoption in the years ahead. Last year, the AI agent industry was valued at around $5.1 billion. This figure is projected to soar by a whopping 821%, reaching $47 billion by 2030,” wrote Kranjec.

While such massive percentage projections make for dizzying reading, perhaps we should centralize our focus on the actual jobs agentic AI can now take on. In the data management and manipulation space, that brings us back to the poor database administrator, could the AI DBA be about to become the real hero?

Disparate Data Drivers

Stewart Bond sees a role for this exact job function. In his role as VP of data intelligence and integration software at technology analyst house IDC, he projects that AI can now take on a central role in data orchestration and administration.

“The rise of agentic AI orchestration is expected to accelerate, and companies need to start preparing now,” said Bond. “To unlock agentic AI’s full potential, companies should seek solutions that unify disparate data types, including structured, unstructured, real-time and historical information, in a single environment. This allows AI to derive richer insights and drive more impactful outcomes.”

Bond makes his comments in order to contextualize new services stemming from data streaming company Confluent. The organization is known for its real-time data platform built on Apache Kafka, an open source stream-processing technology. A new “snapshot queries” service in Confluent Cloud for Apache Flink will enable both real-time and historic data processing to happen concurrently. This company has promised that this will “make AI agents and analytics smarter” and it has also included IP filtering to add secure access controls.

Blended Data Brew: Real-Time and Batch

“Agentic AI is moving from hype to enterprise adoption,” said Shaun Clowes, chief product officer at Confluent. “But without high-quality data, even the most advanced systems can’t deliver real value.”

For AI data agents to make the right decisions, they need historical context about what happened in the past and insight into what’s happening right now, explains Clowes and his team. For example, for fraud detection, banks need real-time data to react in the moment and historical data to see if a transaction fits a customer’s usual patterns. Hospitals need real-time vitals alongside patient medical history to make safe, informed treatment decisions. But to use both past and present data, IT often has to use separate tools and develop manual workarounds, which can result in broken workflows.

Confluent’s latest service addresses that duality with its latest service by blending real-time and batch data “so that enterprises can trust their agentic AI to drive real change”, Clowes says.

The Rise of the Agentic DBA

Confluent didn’t necessarily build this technology to enable and create the agentic DBA, but Clowes points out, if the continued extension of the company’s platform makes this “workplace role” a reality, then it will surely serve IT stacks for the better.

“The rise of the Agentic DBA is already happening… and there are some very ‘human’ reasons behind it. Dealing with disruptions like anomalies, outages, or performance optimizations is distracting (to say the least) for DBAs and data infrastructure teams,” enthused Karthik Ranganathan, co-founder and CEO of cloud-native open source database company Yugabyte. “DBA agents are trained to respond and optimize automatically, allowing human workers to focus on more strategic business value tasks.”

Ranganathan says that agentic DBAs are capable of anything from performing query execution patterns to analyzing resource trends to mentoring cloud cluster health, which means all these tasks can now be dealt with automatically. This allows DBAs to avoid “alert fatigue” and learn from previously taken actions when their workload permits.

Industry Response

There are many technologies in this space now coming forward. If you’re lucky enough to get invited to an Oracle welcome keynote on a Sunday night at its tech events, this is the kind of technology that the company talks about volubly. With so many database functions now ripe for moving to automation such as patching, maintenance checks, upgrades and perhaps also data normalization and deduplication, it’s no surprise to hear the database giant talk about database automation.

Does IBM Make One?

Does IBM make something in this area too? Usually, is the safe answer. The company last month announced its answer to database automation challenges in the form of Db2 Intelligence Center, an AI-powered database management platform designed specifically for Db2 database administrators and IT professionals managing databases.

“We’ve spent years talking to Db2 database administrators, understanding their pain points, frustrations and the complexity of their workflows. The feedback we have captured is loud and clear: DBAs are tired of fragmented tools that don’t integrate with each other. They’re tired of the endless libraries of scripts where each DBA maintains his or her own variations and they’re tired of constantly reacting to problems and manually troubleshooting, as opposed to being proactive in their database management approach,” said Ani Joshi, senior product manager for Db2, IBM data and AI.

Db2 Intelligence Center is a unified, intelligent management console purpose-built for Db2 administrators. It combines advanced monitoring, AI-powered troubleshooting and query optimization into an integrated service that simplifies and accelerates many aspects of Db2 management.

Are Human DBAs Now Redundant?

With these (arguably) not insignificant automations now coming to the fore, some may ask whether we will have succeeded in making the role of the human database administrator redundant. The answer to that question is, obviously, of course no, don’t be silly.

What we’re seeing here are the mechanical repetitively rote tasks that a DBA has to undertake, now taken out of their workflow to some degree (in some cases totally) and so creating a new DBA role that can start to work more closely with the developer team, provide more business-centric value through increased proximity to commercial teams while also now working to innovate and create new data services.

Conclusion

The rise of agentic AI services in the data management space is set to elevate the status of database administrators and create new opportunities for them to work more closely with developers and provide business-centric value. With the ability to automate repetitive tasks, DBAs can focus on more strategic tasks and drive real change in their organizations.

FAQs

Q: What is an agentic DBA?
A: An agentic DBA is a database administrator who uses AI-powered tools to automate repetitive tasks and focus on more strategic tasks.
Q: Will agentic AI make human DBAs redundant?
A: No, agentic AI will not make human DBAs redundant. Instead, it will automate repetitive tasks and create new opportunities for DBAs to work more closely with developers and provide business-centric value.
Q: What are the benefits of agentic AI in database management?
A: The benefits of agentic AI in database management include increased efficiency, improved accuracy, and enhanced decision-making capabilities.
Q: Which companies are working on agentic AI solutions for database management?
A: Companies such as Confluent, IBM, and Oracle are working on agentic AI solutions for database management.
Q: What is the future of database administration?
A: The future of database administration is likely to involve increased use of AI-powered tools to automate repetitive tasks and create new opportunities for DBAs to work more closely with developers and provide business-centric value.

Introduction to MLPerf Benchmarks

As you AI pros know, the 125-member MLCommons organization alternates training and inference benchmarks every three months. This time around, its all about training, which remains the largest AI hardware market, although not by much as inference drives more growth as the industry shift from research (building) to production (using). As usual, Nvidia took home all the top honors.

AMD Joins the Training Party

For the first time, AMD joined the training party (they had previously submitted inference benchmarks), while Nvidia trotted out their first GB200 NVL72 runs to demonstrate industry leadership. Each company focussed on their best features. For AMD it is larger HBM memory, while Nvidia exploited its Arm/GPU GB200 superchip and NVLink scaling.

The Bottom Line

The bottom line is that AMD can now compete head to head with H200 for smaller models that fit into MI325’s memory. That means AMD cannot compete with Blackwell today, and certainly cannot compete with NVLink-enabled configurations like NVL72.

AMD: Its All About The Memory

AMD has more HBM memory on their MI325 platform than any Nvidia’s GPU, and can therefore contain an entire medium-sized model on a single chip. So, they ran the training benchmark that fits, the Llama 2-70B LORA model. The results are reasonably impressive, besting the Nvidia H200 by an average of 8%. While a good result, I doubt many would choose AMD for 8% better performance, even at a somewhat lower price. The real question, of course, is how much better the MI350 will be when it launches next week, likely with higher performance and even more memory.

AMD’s Limitations

One thing AMD will not offer soon is better networking for scale-up; the UA-Link needed to compete with NVLink is still many months away (possibly in the MI400 timeframe in 2026). So, if you only need a 70B model, AMD may be a better deal than Nvidia H200; but not by much.

Traction with Partners

AMD is also showing traction with partners, and better performance from its ROCm software, which took quite a beating from SemiAnalysis last December. With better ease-of-use from ROCm, partners can benefit from offering customers a choice; many enterprises do not need the power of an NVL72 or NVLink, especially if they are focussed on simple inference processing. And of course, AMD can offer better availability, as NVIDIA GB200 is much harder to obtain due to overwhelming demand and pre-sold capacity. The rumor mill says GB200 still takes over a full year delivery time if you order today.

Nvidia: Its All About Scale-Up

Nvidia says the GB200 NVL72 has now arrived, if you were smart enough to put in an early order. With over fifty benchmark submissions using up to nearly 2500 GPUs, Nvidia and their partners ran every MLPerf benchmark on the ~3000 pound rack, winning each one. CoreWeave submitted the largest configuration, with nearly 2500 GPUs.

Nvidia’s Advantage

While the GB200 NVL72 can outperform Hopper by some 30X for inference processing, its advantage for training is “only” about 2.5X; thats still a lot of savings in time and money. The reason is that inference processing benefits greatly from the lower 4- and 8-bit precision math available in Blackwell, and the new Dynamo "AI Factory OS” optimizes inference processing and reuses previously calculated tokens in KV-Cache.

My Takeaway

While AMD does not yet have the scale-up networking required to train larger models at Nvidia’s level of performance, this benchmark shows that they are getting close enough to be a contender once that networking is ready next year. And AMD can already out-perform the Nvidia H200, once you clear the ROCm development hurdle.

The Future of AI Hardware

It could take a year or more for AMD to be able to scale efficiently, and by then Nvidia will have moved on to the Kyber-based NVL576 with the new NVLink7, Vera CPU and upgraded Rubin GPU.

Conclusion

If you start late; you stay behind. The AI hardware market is rapidly evolving, and companies need to stay ahead of the curve to remain competitive.

FAQs

• What is MLPerf?
  MLPerf is a benchmarking suite for machine learning workloads, used to evaluate the performance of AI hardware.
• What is the difference between training and inference?
  Training refers to the process of training a machine learning model, while inference refers to the process of using a trained model to make predictions.
• What is NVLink?
  NVLink is a high-speed interconnect developed by Nvidia, used to connect GPUs and other devices in a system.
• What is UA-Link?
  UA-Link is a high-speed interconnect developed by AMD, used to connect GPUs and other devices in a system.
• What is ROCm?
  ROCm is an open-source software platform developed by AMD, used to manage and optimize machine learning workloads on AMD hardware.

Software and platforms for Diversity, Equity, Inclusion, and Accessibility (DEIA) are revolutionizing the way we approach social and economic disparities. By harnessing the power of technology, we can create a more just and equitable society for all. In this article, we’ll explore the critical role of software in shaping a more inclusive future.

The Current State of Inequality

The world is facing numerous challenges, from racial and gender disparities to unequal access to education and economic opportunities. These inequalities have far-reaching consequences, including social unrest, economic stagnation, and a decline in overall well-being. It’s essential to address these issues and create a more equitable society.

The Impact of Inequality on Society

Inequality affects not only individuals but also entire communities and societies. It can lead to social and economic instability, decreased economic growth, and a decline in mental and physical health. Furthermore, inequality can perpetuate cycles of poverty, making it challenging for marginalized groups to break free from systemic barriers.

The Role of Technology in Perpetuating Inequality

Technology can both perpetuate and alleviate inequality. On one hand, it can exacerbate existing disparities by providing unequal access to resources, information, and opportunities. On the other hand, technology can be a powerful tool for promoting inclusivity and equality. By developing and implementing inclusive software and platforms, we can create a more level playing field and provide opportunities for marginalized groups to thrive.

The Power of Inclusive Tech

Inclusive tech refers to software and platforms designed to promote diversity, equity, inclusion, and accessibility. These technologies can help address social and economic disparities by providing equal access to resources, information, and opportunities. Inclusive tech can take many forms, including accessible websites, mobile apps, and online platforms.

Examples of Inclusive Tech

There are numerous examples of inclusive tech, including:
– Accessible websites and mobile apps that provide equal access to information and resources for people with disabilities.
– Online platforms that promote diversity and inclusion in the workplace, such as diversity and inclusion training programs.
– Software that helps address systemic barriers, such as bias detection tools and diversity metrics.

The Benefits of Inclusive Tech

Inclusive tech offers numerous benefits, including:
– Increased diversity and inclusion in the workplace and society.
– Improved accessibility and equal access to resources and opportunities.
– Enhanced social and economic mobility for marginalized groups.
– A more equitable and just society.

Challenges and Opportunities

While inclusive tech has the potential to create a more equitable society, there are challenges and opportunities that must be addressed. These include:
– Ensuring equal access to technology and digital literacy.
– Addressing bias and discrimination in AI and machine learning algorithms.
– Developing and implementing inclusive tech that meets the needs of diverse users.

Addressing Bias and Discrimination

Bias and discrimination in AI and machine learning algorithms can perpetuate existing disparities and create new ones. It’s essential to develop and implement algorithms that are transparent, fair, and unbiased. This can be achieved by:
– Using diverse and representative data sets.
– Implementing bias detection and mitigation tools.
– Developing algorithms that prioritize fairness and equity.

Developing Inclusive Tech

Developing inclusive tech requires a deep understanding of the needs and experiences of diverse users. This can be achieved by:
– Conducting user research and testing.
– Involving diverse stakeholders in the development process.
– Prioritizing accessibility and usability.

Case Studies and Success Stories

There are numerous case studies and success stories that demonstrate the impact of inclusive tech. These include:
– Companies that have implemented diversity and inclusion training programs, resulting in increased diversity and inclusion in the workplace.
– Organizations that have developed accessible websites and mobile apps, providing equal access to information and resources for people with disabilities.
– Software that has helped address systemic barriers, such as bias detection tools and diversity metrics.

Lessons Learned

These case studies and success stories offer valuable lessons learned, including:
– The importance of prioritizing diversity, equity, inclusion, and accessibility in tech development.
– The need for ongoing testing and evaluation to ensure that inclusive tech is effective and meets the needs of diverse users.
– The potential for inclusive tech to create a more equitable and just society.

Conclusion

In conclusion, software and platforms for DEIA have the potential to create a more equitable and just society. By harnessing the power of technology, we can address social and economic disparities and promote diversity, equity, inclusion, and accessibility. It’s essential to prioritize inclusive tech and develop software and platforms that meet the needs of diverse users. By doing so, we can create a brighter future for all.

Frequently Asked Questions

What is inclusive tech?

Inclusive tech refers to software and platforms designed to promote diversity, equity, inclusion, and accessibility.

How can inclusive tech address social and economic disparities?

Inclusive tech can address social and economic disparities by providing equal access to resources, information, and opportunities.

What are some examples of inclusive tech?

Examples of inclusive tech include accessible websites and mobile apps, online platforms that promote diversity and inclusion in the workplace, and software that helps address systemic barriers.

How can we ensure that inclusive tech is effective and meets the needs of diverse users?

We can ensure that inclusive tech is effective and meets the needs of diverse users by conducting user research and testing, involving diverse stakeholders in the development process, and prioritizing accessibility and usability.

What are some challenges and opportunities in developing and implementing inclusive tech?

Challenges and opportunities in developing and implementing inclusive tech include ensuring equal access to technology and digital literacy, addressing bias and discrimination in AI and machine learning algorithms, and developing and implementing inclusive tech that meets the needs of diverse users.