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SC ‘24 brought together innovators, thought leaders, and cutting-edge technologies to push the boundaries of high-performance computing (HPC).

During the event, Solidigm’s own Yuyang Sun had the chance to speak with Chen Lee, VP of Sales, HPC, Data Center and Enterprise for Giga Computing, about Gigabyte’s pivotal role in the server and AI market and the transformative impact of high-density SSDs.

Gigabyte’s Evolution: From Gaming to Global Server Leader

Gigabyte is widely recognized for its gaming and consumer electronics. Perhaps less widely known is that the company established a server division in 1989 to address the growing demand for enterprise and data center solutions. Today, Gigabyte ranks among the world’s top white-box server manufacturers, with a portfolio tailored for HPC, AI, and enterprise workloads.

Chen shared an anecdote about Gigabyte’s early collaboration with Google during the inception of open compute, underscoring the company’s focus on innovation, foresight, and willingness to embrace challenges.

“Game-changing” new 122 TB SSDs

Just before SC ‘24, Solidigm unveiled its groundbreaking 122 TB SSD, a high-density storage solution designed to supercharge AI workloads and data-intensive applications. Chen praised the SSD’s ability to deliver unprecedented storage capacity and speed, essential for the seamless integration of data with CPUs and GPUs in AI systems.

“The density and performance of this SSD are game-changing,” he noted. “It allows organizations to store more data with a smaller physical footprint, reducing infrastructure costs and energy consumption.

“With the emergence of the AI market, it becomes necessary to be able to react fast along with the GPU, the CPU, and the data needs to be seamless with the systems. So being able to have something with this kind of capacity, plus the speed, is amazing.”

Efficiency Meets Performance

High-density SSDs not only boost performance, but also address critical challenges in modern data centers. Compared to traditional 3.5-inch SAS drives, these compact SSDs dramatically reduce weight and space requirements, enabling enterprises to optimize their hardware investments.

Chen highlighted how Solidigm’s innovations align with Gigabyte’s vision of delivering efficient and reliable server solutions.

“Imagine holding a petabyte of data in just eight of these drives—it’s a testament to how far we’ve come. High-density storage is not just about capacity; it’s about enabling faster, smarter, and more sustainable computing.”

Gigabyte and Solidigm share a commitment to advancing AI and HPC. By integrating leading-edge technologies like high-density SSDs into their server solutions, Gigabyte empowers customers to tackle complex workloads with ease, efficiency, and scalability.

So what’s the TechArena take? As AI evolves and the infrastructure to support it becomes more advanced, hats off to Gigabyte for doubling down on its strengths—high-performance GPU servers, innovative cooling technologies, and partnerships with leading hardware and storage vendors.

At SC ‘24, Solidigm’s Jeniece Wnorowski caught up with NASA's Laura Carriere to discuss the space agency’s high performance computing (HPC) capabilities and the role of supercomputers in advancing Earth sciences, climate research, and more.

Laura, the high-performance computing (HPC) lead for NASA's Center for Climate Simulation (NCCS) in Greenbelt, Maryland, discussed how the smaller of NASA’s two supercomputing facilities focuses on Earth sciences while also supporting astrophysics, heliophysics, and planetary science research.

NASA’s Discover supercomputer plays a pivotal role in climate modeling, helping scientists tackle challenges like air pollution, aerosol movement, and hurricane formation, Laura said. With 60 petabytes of storage, Discover integrates powerful CPUs and GPUs to process massive datasets. Complementing Discover is the Prism system, a GPU-dense supercomputing environment that supports artificial intelligence (AI) and machine learning (ML) applications for tasks such as atmospheric simulations and space studies.

Visualizing Climate Change

One standout area of focus for NASA is aerosols—tiny particles that affect air quality and weather patterns. Using 3D climate models, NASA’s scientists can visualize how dust travels from the Sahara Desert to Florida or how sea salt is picked up at the poles. These dynamic visualizations provide insights into air pollution, emissions, and the impacts of climate change, helping policymakers and researchers develop data-driven strategies.

Power and Storage Challenges

While NASA’s supercomputers are powerful, they are also constrained by power and cost limitations. Discover’s modular design allows for incremental upgrades, decommissioning older components as new ones are added, Laura said. This ensures optimal performance but necessitates careful resource planning.

On the storage side, NASA maintains datasets on spinning disks, balancing cost-efficiency with performance. Laura noted that transitioning to high-capacity solid-state drives (SSDs) could drastically reduce power consumption and physical footprint, enabling more efficient operations. However, the highly compressed nature of NASA’s datasets poses unique challenges for cost-effectiveness in SSD adoption.

AI Meets Space Exploration

NASA is increasingly incorporating AI into its operations. The Prism system has enabled researchers to develop foundation models for Earth sciences and accelerate tasks such as binary star detection. Laura shared a captivating example of using HPC and ML to identify sextuple star systems—three binary star pairs gravitationally bound together.

NASA also used its supercomputers to generate awe-inspiring visualizations of black holes, giving the public a glimpse into the mysteries of the universe. These groundbreaking projects demonstrate the synergy between HPC, AI, and scientific discovery.

So what’s the TechArena take? As NASA’s awe-inspiring work continues to push the boundaries of science and technology, the agency’s commitment to HPC and data-driven research remains steadfast. As the agency continues to make incremental improvements in its HPC hardware and capabilities, we’ll be watching closely to see which of the great mysteries of Earth and space they solve next.

During SC ‘24, Yuyang Sun of Solidigm caught up with Joseph Lu, Director of Storage Technology at ASUS, to discuss ASUS’s role in advancing HPC and AI server solutions.

While ASUS is widely recognized for its gaming and consumer electronics products, Joseph highlighted the company’s extensive history in server technology. Since establishing its server division in 1989, ASUS has continually evolved to address the growing demands of enterprise, data center, and HPC markets.

“We’ve been in the server business for over 25 years,” he said, emphasizing ASUS’s commitment to innovation and focus on delivering comprehensive, customizable server packages. The company provides consultation, installation, and after-service support tailored to meet diverse customer needs.

At SC ‘24, ASUS demonstrated its latest AI supercomputing solutions, featuring NVIDIA, Intel and AMD GPU servers. Joseph emphasized that ASUS’s broad portfolio enables the company to optimize solutions for various workloads and customer preferences, empowering organizations to tackle complex challenges in AI and HPC.

“Our offerings cater to diverse workloads, providing customers with the flexibility and performance they need to succeed,” he noted.

High-Density SSDs: A Game-Changer for AI Workloads

A highlight of the discussion centered on Solidigm’s recently launched 122TB SSD and the transformative potential of high-density storage for data-intensive applications.

The new SSD is a great option for ASUS customers, Joseph said, because AI workloads require high-speed data transmission between GPU and storage servers.

“High-density SSDs help overcome bandwidth bottlenecks, enabling seamless data flow and non-blocking infrastructure,” he said.

The combination of high capacity and performance offered by such SSDs not only supports massive data volumes, but also enhances infrastructure efficiency. Joseph highlighted the benefits of reduced physical footprint, lower infrastructure costs, and improved energy efficiency—key considerations for modern data centers.

As AI data volumes continue to grow exponentially, scalability remains a top priority. ASUS’s commitment to integrating high-density storage into its offerings ensures customers can scale without worrying about future limitations.

“With the increase in AI data, it’s hard to predict how massive the storage requirements will be in a few years,” Joseph noted. “By adopting high-density storage solutions early, customers can scale effortlessly, ensuring they’re prepared for future demands.”

He also shared insights into ASUS’s ongoing efforts to develop software-defined storage solutions in collaboration with independent software partners. These innovations aim to provide customers with flexible, high-capacity storage options for both core and edge workloads.

“High-density storage is not just about capacity—it’s about driving innovation in AI and HPC,” Joseph said. “It empowers organizations to store more data, run diverse workloads, and achieve unprecedented performance.”

So what’s the TechArena take? ASUS and Solidigm’s collaboration underscores the importance of leading-edge storage technologies in unlocking the full potential of AI applications. High-density SSDs represent an important advancement, enabling faster, smarter, and more sustainable computing.

As 2024 draws to a close, I began to reflect both on the year behind us and the transformative changes ahead. Below are my five major predictions for AI in 2025.

Prediction 1: AI is going to move past simply using previous linguistic patterns to generate text and will begin to use a framework for associating facts and knowledge with linguistic patterns. Today, AI talks a good game, but doesn’t know anything. I predict that this will change in 2025, probably as the result of a marriage between knowledge graphs and LLMs. It will require a new architecture to make it work, so it's not as simple as combining knowledge graphs and LLM in a graphRAG type of application. 

Prediction 2: 2025 will be the year of the Chief AI Officer. Enterprises large and small will hire someone responsible for ensuring that AI projects make sense, that security and regulatory structures are in place, and that use guidelines are published and enforced. Chief AI Officers will be responsible for minimizing the risk of organizational damage caused by hallucination and will need to ensure that enterprises are not investing in products that don't actually work. I recently heard a Chief AI Officer say that they are looking to block “fly-by-night” AI products.

Prediction 3: We will see a proliferation of locally-deployed small GenAI models along the lines of Llama 3 that can be tuned or pre-prompted for very specific use cases. These applications will be especially important for use cases that require access to highly sensitive data or secure mobility. We are seeing an increasing number of “LLMOps” products that address the need to seamlessly train, test, deploy, and update such local models. Infrastructure costs to deploy local models have come down, making the economics of local models more sensible, but the risk is that without an integrated LLMOps pipeline, locally deployed models could become hopelessly out of date every few months.

Prediction 4: Enterprises will move beyond simple AI guidelines to begin to deploy real regulatory and governance frameworks and tooling that allows them to monitor model performance, data quality, regulatory compliance, security, and network connectivity. Many of these monitoring activities on their AI infrastructure will be done by agents.

Prediction 5: Agentic AI, which I view as using an AI quarterback to help automatically pick which specific AI tool or service is best for each enterprise application, will begin to become more commonplace. This is a natural evolution of the current practice of using ChatGPT, Claude, Gemini, Co-pilot and other tools to generate content and solve problems, and then comparing the quality and validity of each before choosing which output to use.

“The future bears a resemblance to the past, only more so.”

- Faith Popcorn (of course)

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Out here in the mountains, the days – well, the days really aren’t. As the TV tunes to the morning market opening, some of us still can’t see if that lump in the back yard is a deer or coyote, requiring more attention than typical with the fuzzy creatures and morning routines. It’s great standing out here when the Fahrenheit scale is in the teens. We’re also realizing that our last calendar item of the day will end after twilight. Thus, Prediction 0000: I predict there will be no changes to Daylight Savings to give we Northerners a glimpse of the sun on either side of a workday and provide cheer to our lives.

As the days become shorter and the holidays near, it’s the season to posit about the year ahead, so here we go. The C-suites are restless. Execution has not been great in technology this year, even though the market has responded well. That pressure to keep the market momentum could be the final straw to improve the execution through observation. That will mean increases in costs in some areas, which will result in the push to decrease costs elsewhere. Thus:

Prediction 0001: The back-to-the-office push will ramp significantly, with many tech and tech-centered companies reverting to five days of cubicle labor. This will mirror the general global industry. Time to wear shoes, folks. Additionally, there will be a frankly unsurprising lack of movement of the employee base who have been threatening to switch jobs for a couple years if they have to go back. 

Prediction 0010: The general tech industry trend will be to do more with less. Lack-of-skilled-workforce claims aside, the industry will contract globally with the influx of graduating and new employees, displacing significant experience that is still slow to adapt to new methodologies. We, the former planners, will tell you that it’s not about the people, it’s about the people required to do the job.

Corollary to the above, not a prediction, just an observation: M&A in the tech industry will be very noisy. Second Corollary to the above, the vocal global momentum on a four-day work week will grow with no action.

Corollary that leads to an actual Prediction 0011: Global industry will see a strong resurgence in the workspace client market. Hey, The Management might be cheap at heart, but they know that cool office schwag gets people back to said offices. This is as much a software trend as it is a hardware one, created by new PCs and tablets, wireless infrastructure, collaboration tools, and the like. It will even extend to furniture and commercial real-estate pricing. New cubes and new whiteboards for the peeps! West Coast commute times will be hardest hit.

Oddly enough, however, this rising tide won’t lift all newer and heavier boats. Prediction 0100: By about mid-year, the market will hammer more than a few companies who admit that the monetization of their new AI services (is AIaaS in use yet? If not, consider it ™-d here) is significantly lower than expected. Basic economics applies: while demand is high, supply is, well, over-supplied. This won’t (yet) extend to the high-end hardware providers. It also will not decrease the momentum in software and services to create newer and shinier AI tools, perhaps to the derision of the analyst crowds. Hey, live by the, “We have AI!” die by the, “We have AI!”

Corollary Prediction 0101: The AI services that buck the trend will be client-based. This will create an opportunity for continued AI development on single-system images that will have a longer-term impact on hardware; a topic for a future predictions column. The real result will be a problem that will start, but not totally come to the fore next year. While clients individually will have some cool new search and usability tools, clients as a whole will have fragmented individual services. Productivity results will vary. Literally. No back-end services will resolve this next year, though bets are that there will be speculation on how to do so in those shiny new conference rooms.

A few more off the cuff:

Prediction 0110: Not only will there be more announced government investment in technology infrastructure globally, there will be SIGNIFICANTLY more announced government investment in technology infrastructure globally.

Prediction 0111: Tariffs will not have an impact on the movement of global technology, though some companies will probably try to blame them in an earnings report around Q4.

Prediction 1000: Hardware innovation in the datacenter will continue to be in a stall. The industry as a whole will admit it’s up against a wall that’s a conglomeration of power consumption, bandwidth limitations, delayed products, and the intransigence of one or two major players to participate in general solutions. It’ll get fixed, just not next year.

Prediction 1001: Noted above. There will be at least two positive M&A or fragmentation deals in the tech industry next year. We mean big ones, in the multi-billions.

Prediction 1010: There will also be at least one pretty grim security breach that will create a good couple months of debate on whether the as-a-service industry has fundamental flaws.

Prediction 1011: We will review all of these predictions next year. Yer’ Humble Author (YHA) is not afraid of admitting mistakes. 

And proof that this entire column was conceived to deliver a geek joke: There was no way we were going to take this to 1111 predictions. That would give this column an F. Have a good set of holidays, everyone.

In the world of artificial intelligence (AI), the quest to recognize value upside can be compared to the wide divide between elite athletes and basic fitness for the masses. Just as the most elite athletes work tirelessly to improve their physical strength, stamina, speed and mental game, the most elite AI researchers and data scientists are pushing the limits on super intelligence.

While the race for Artificial General Intelligence (AGI) might be akin to breaking world records in an Olympic event, the real transformation in AI is happening incrementally—much like the steady progress made by weekend warrior athletes striving to maintain their physical fitness. In 2025, applied AI – the kind that has the potential to return more value than is spent in achieving outcomes from it will resemble a regular fitness tune-up.

Key Trends Shaping the Future of AI

As we look ahead, the AI landscape is evolving rapidly, with several emerging trends that promise to reshape the industry. Much like fitness routines that adapt to new goals, making progress in the AI journey requires effort in data strategies, new business models, and evolving user experiences. Here are three key trends to watch for in 2025:

1. Data Structure Becoming Crucial

Much like how ‘great abs begin in the kitchen’ rather than through long, daily ab workouts, great AI results start with high-quality, well-structured data. With people, as with AI, it’s a matter of garbage in, garbage out. In 2025, the importance of data management, security, and governance will be more critical than ever. A key learning from listening to industry leaders at the recent AI Summit NYC is that customization to your business workflows using your data drives the specific results you need. Don’t be fearful of the tuning necessary to extract the best of what AI offers your enterprise – think of it as the normal process to integrate other more mature enterprise software platforms such as Salesforce.

This is no small challenge as there are no automatic tools that simply fix years or decades of neglect – whether on our eating habits or on enterprise data structures and silos. The upfront investment is an absolute must however. According to a blog from Pure Storage, data bloat, mismatched formatting and incompatible structures can result in both a reduced return from the investment in new AI platforms, and may balloon costs if a usage-based provider is used for AI training or inferencing.

2. Business Model Innovations – Freemium Tools on Open-Source Models

The biggest challenge I have heard from IT Directors trying to figure out how to offer AI platforms and services to their internal customers is that commercial off-the-shelf AI platforms with upfront fees come with the disclaimer “…you have to work with it to find the value to your enterprise.” Because a specific enterprise has its own data and data structures, AI platforms that rely on unique data require self-assembly, self-discovery and experimentation to find the nuggets of value for a specific business. While assembling a program requires significantly less time for an AI-partnered software expert, the majority of users may lack interest or incentive to go through the process of self-assembly just to discover if AI has value in a subset of their specific workflows. The upfront cost of entry for commercial AI platforms combined with the vague upsides a user must discover on their own through trial and error will limit the deployments to early adopters and enthusiasts if it continues.

As a result, the introduction of more freemium business models will ensure that there isn’t an adoption stall from the upfront cost of off the shelf tools combined with the treasure hunt required to find clear and specific business value.

3. The “Pixar effect”

There is a lot of debate about what creative industries AI could destroy and, at the same time, more content consumers are resisting and pushing back on AI-generated content. Whether videos, automated comments on social media platforms that reward ‘clicks’, or even the first two drafts of this blog – there is a spark missing and difficulty connecting to concepts that present like a chatbot reading a manual back to us.

When Pixar first began using computing technology to push the bounds of animation, many believed the art that previously underpinned animated film making was dead. Pixar’s end product and emphasis on story crafting, using technology to rapidly iterate on the art of what was visually possible changed animated film making disruptively. Like the technology Pixar relied on, AI amplifies what’s already there offering insight potential from underutilized data to do more, better.

Applying AI will only amplify what’s already there – great insights, great products, robust data. Don’t lose the importance of improving your differentiation, and weighing the potential of AI against staying true to your core mission in your business.

References:

• Pure Storage (2024). “Improve Data Hygiene, Overcome the AI “GIGO” Problem”

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Explore how OCP’s Composable Memory Systems group tackles AI-driven challenges in memory bandwidth, latency, and scalability to optimize performance across modern data centers.

Since today’s modern car is a data center on wheels, it should come as no surprise that the neural networks that appear to be gaining all the attention these days are also deployed in the automobile, and have been for some time now.

In a previous blog titled, “Why a Self-Driving Car Might Run a “STOB” Sign: To ViT or Not To ViT,” I wrote about some of the different classes of neural networks addressing vision processing. In this blog, I’m going to discuss another class of neural networks employed in the vehicle, referred to as Large Language Models (LLMs).

As a brief genealogy, an LLM is a type of neural network referred to as a transformer model, which falls under the larger generative AI umbrella. Keeping up with the latest developments in these models is difficult, as the number of neural networks being introduced into the public domain is growing at an explosive rate. As a proxy for this growth, over 100 machine learning (ML) white papers are being published daily.

LLMs used for natural language processing (NLP) most likely first caught the awareness of the broader population with Apple’s introduction of Siri. Suddenly, the futuristic concept of computers recognizing speech had gone from being an idea right out of Star Trek to a hand-held reality. And while these initial versions still had room to improve, Siri’s capabilities were nonetheless quite impressive. From personal experience using an early speech-to-text program after a bicycle accident left me unable to type for several months, the improvements were profound.

More recently, the release and growth of ChatGPT, which provides an accessible frontend for various OpenAI LLMs, has marked the next major milestone in raising the broader population’s understanding of the power, potential, and capabilities of AI. This, in turn, has caused a frenzied uptick in the pace of AI research, both in areas of neural networks and semiconductor architectures that improve power efficiency and performance.  It has also given rise to the explosive growth of AI in the data center and the “AI bubble” that we are currently experiencing.  The power of AI, and of LLMs, in particular, is so profound that governments are aggressively evaluating different approaches to potentially manage and regulate this technology.

So how does this all fit into the automobile? NLP had its humble beginnings a few decades ago with the introduction of simple keyword recognition, that when properly enunciated, would typically allow for control over the body control or stereo system.  Similar to my early experiences with speech-to-text processing, the capabilities were clumsy, yielded inconsistent performance, and at some point, it was easier to simply adjust the car’s thermostat manually vs. continuing to repeat keywords over and over while waiting for a response.

Fast forward to today, where NLP in the car is on par in capability with Alexa-enabled devices. In fact, Alexa is finding its way into the car, as there is a land grab underway to capture as much information regarding user behavior as possible. Beyond Alexa, multiple other digital assistants are typically resident in the high-end vehicles, allowing for voice control over the vehicle, searching the user manual, in addition to voice-controlled navigation, etc. Despite all this original equipment manufacturer (OEM) attention to NLP, however, it’s been shown that these features don’t see much use today.

One of the greatest challenges that manufacturers face in deploying NLP to accomplish more useful tasks stems from onboard compute limitations. The compute demands for NLP are so significant that the data is generally sent to the cloud for processing, avoiding the need for very high-performance and power-hungry AI processors to reside in the vehicle. All is well when connectivity to the cloud can be guaranteed, but when driving in remote locations where cell service may be spotty or non-existent, the NLP will fail. Hence, there is a race underway to design more power-efficient compute architectures that can harness local compute to run LLMs.

While NLP accounts for the majority of the use cases for LLMs in today’s automobile, there are other key areas where they will soon be employed to take on more significant tasks that will yield greater value than voice control over the car’s stereo or heater.  Through the combination of computer vision and other AI technologies, LLMs can be used for scene understanding, which can lead to greater levels of driver confidence and safety in ADAS systems. This is similar to more advanced adaptive cruise controls that employ the heads-up display to communicate with the driver that the cruise control does indeed “see” the car that it is following and will keep a safe distance. It does this by providing insights through image captioning on a central display so the driver can have confidence that the vehicle understands the surroundings and is explaining the rationale behind a given action that is going to take place.

This is typically referred to as contextual analysis, and the concept is similar to the way a human typically takes in the entire environment, pedestrians, street signs, road construction, road hazards, etc., and takes action accordingly. Contextual analysis, in conjunction with NLP, will also allow for more human interaction with the vehicle – such as, “What was that building we just passed,” or, “What brand of car is in front of us?” The driver could then ask “How much does this car cost, and where can I purchase it?”  (That’s Alexa’s fantasy, anyway.) I’m sure you get the picture by now (pun intended).

Contextual awareness can also enable the vehicle to more closely mimic a driver’s behavior, showing caution when entering an intersection that has a dense population of pedestrians, despite having the full right-of-way. And while V2X (vehicle-to-vehicle communication) offers the promise of understanding traffic ahead etc., to date, deployment has been spotty. With contextual awareness, the driver can be alerted that there is a traffic jam ahead. When V2X is fully deployed, it is not only belt-and-suspenders, but it can also “see traffic” in zero visibility. Here again, if the driver is suddenly experiencing extreme deceleration with no understanding as to why, contextual awareness, in conjunction with V2X, can communicate to the driver that there is a pile-up ahead and that’s why the brakes are being aggressively applied.

So will LLMs end up in the car? I believe so. I also believe that, as they become more integral to the safety of the vehicle, compute architectures that can deliver the requisite performance in vanishingly small power envelopes will come to market to address this need – necessity being the mother of invention.

For now, relying on a connection to the cloud with unpredictable, spotty coverage and indeterminate latency for such an integral function will be a show-stopper. Power is a topic that the AI community, in general, likes to avoid talking about, but it’s currently an “inconvenient truth.” 

Based upon current AI compute architectures, GPT-3, which is based on an LLM, requires roughly 1,300 megawatt-hours of electricity for training, and a single query is estimated to consume almost 3 watt-hours of energy. These are astonishingly high levels of energy and hence why data centers are now being considered to be immersed in the SF bay to secure a low-cost means of cooling and the 3-mile island nuclear power plant is being spun up to power a data center. As is happening in the memory industry, it is said that automotive is now the technology driver for memory, it can well be the same for future AI compute architectures.

The incoming U.S. administration’s position on climate change, regulation, and Environmental, Social and Governance (ESG) disclosures is poised to wreak havoc on environmental progress. The potential rollback of US-based commitments may force multinational corporations (MNCs) to navigate a complex regulatory landscape, balancing compliance in international markets while avoiding lawsuits from boardroom activists, shareholders, and domestic consumers. This "Not In My Back Yard" (NIMBY) mentality could lead to a fragmented approach to emissions regulations, further complicating the path to sustainability.

Following are three things I see on the horizon for ESG and sustainability in 2025.

Prediction 1) Innovation At Risk

The confusion of the new regulatory environment promises to stifle ESG software investment, innovation and strategic flexibility – with a potential domino effect to follow: Companies would opt to redraw their plans at the expense of pursuing innovative sustainability initiatives that fit better within the prescribed frameworks. This would result in a more rigid approach to sustainability, where businesses prioritize regulatory compliance over genuine environmental and social impact. And ultimately, progress would slow down in corporate sustainability efforts, as businesses become more conservative in their ESG communications and disclosures.

Prediction 2) Evolving ESG Efforts & the Role of AI

Those who take on ESG efforts without having boardroom and executive sponsorship risk failure, simply because ESG reporting can be very disruptive to a company’s workflow across all departments, including externally with suppliers and customers. Company-wide support and engagement is crucial.

However, companies who opt to integrate artificial intelligence (AI) with advanced technologies in their workflows have an opportunity to revolutionize data collection, analysis and governance to create true business value. Automation will be a driver for data gathering from a wide range of sources; improving the accuracy of ESG metrics will result in more reliable and timely reporting. Using AI to follow the ‘digital thread’ to enhance end-to-end traceability and auditability of ESG data will create a fast ROI.

Prediction 3) Companies to Expand Data Science, Analytics Requirements

As ESG culture becomes mainstream for companies, scalability and flexibility should become table stakes for any ESG reporting. This will become critical given the diversity and large volumes of ESG data to be able to identify both sustainability and business trends, patterns, insights and opportunities. AI’s ability to apply advanced analytics and insights will become a daily outcome. This will drive companies to expand their data science and analytics requirements leading to additional staffing skills, software innovation and services opportunities.

Looking Forward

MNCs will still need an ESG disclosures strategy that is truly global. While they will still invest in collecting emissions data, they should also focus on planning to implement a double materiality assessment (DMA). The outcome of a DMA is a list of material important sustainability-related Impacts, Risks, and Opportunities (IROs). These IROs then determine the specific disclosures and data points the company must include in its final sustainability report.

AI and advanced technologies for ESG should be in every CEO’s top 10 strategic goals. Investment in building out the company’s AI roadmap with ESG as a leading workload will release unknown business outcomes and accelerate business process re-engineering. Companies who adopt AI into ESG processes will also become more efficient at producing their goods and services, be more competitive than their peers and be more sustainable – something that will be evident in their ESG reporting.

What if your biggest business challenges could be solved before you even identified them? That’s the potential of Artificial Intelligence (AI). The enterprise world is beginning to accept AI as more than a buzzword. They can see value in harnessing their data to solve some of their biggest business problems.

One thing is for sure: data is the key ingredient that enables AI. The more data you can use, the better your insights will be. So where will we see innovations? Will the AI-washing fizzle out? And are we even thinking about this new technology in the right way?

1. We will continue to see infrastructure innovations that support AI.

The digital universe continues to grow at an astonishing pace, hitting an estimated 147 zettabytes by the end of 2024 (via Statista). For perspective, that’s 147 trillion gigabytes—a staggering amount of data. This explosion underscores the need for AI to process and create actionable insights from such a vast ocean of information.

Thanks to advancements in GPUs and faster CPUs, algorithms can now process enormous datasets in record time. At the same time, storage technologies—like higher-capacity flash drives—are enabling faster, more efficient consolidation of data, setting the stage for the next evolution in AI-driven insights.

These are exciting advancements, but infrastructure only exists to support the data. If we are creating information in new ways, will there be new ways to consume these insights?

2. Information delivery will be reimagined.

AI computing advancements such as transformer models have given us a glimpse of what is possible with large language models (LLMs). Om Malik, founder of GigaOM and long-time tech reporter/investor wrote an article about how AI will change the browser as we know it.

He points us in the right direction about what information delivery will look like in the future:

Malik envisions a future where AI doesn’t just passively gather data—it actively connects the dots, delivering insights seamlessly. It’s easy to see this trend developing in tools we already use today. He gives an example of having a DietBot to deliver a data stream customized to you that includes an analysis of your eating patterns, health goals, and dietary requirements that match restaurants in real time.

My Samsung watch does a lot of that now, but I have to manually provide much of the information. I have to track my meals and water intake with a different app (Yazio) that I connected to my watch’s app. I weigh in with a digital scale that I also connected to the watch app. The watch can calculate my steps and heart rate.

But my blood pressure monitor can’t connect to my watch, so I have to enter that reading manually. It also can’t connect to my Kardia (used to check for atrial fibrillation). How cool would it be to have a DietBot to do all the work for me? Even better – what if I could tap my watch to my doctor’s tablet and she could get all my latest readings? What if she could subscribe to my data stream when I felt sick, for a better diagnosis?

That’s what is coming – data streams we’ll need to subscribe to in some way. The question becomes how will we consume it? Will it be a new type of browser, an app on your phone (or watch, or TV)? I think we’ll see innovation in how we consume these data streams in 2025.

3. AI hustlers will be everywhere.

With the AI market predicted to hit $1 trillion by 2027, it’s no wonder the bad actors are beginning to circle like sharks.

AI-washing, where companies exaggerate the AI capabilities of their products, is already rampant. Maybe you’ve seen an “AI” version of a product that has merely added a chatbot. The Federal Trade Commission (FTC) is cracking down on exaggerated AI claims, such as the infamous 'world’s first robot lawyer,' which failed to deliver on its promises—mainly because it lacked real lawyers!

Cybercriminals aren’t far behind, using generative AI to launch scams that are more sophisticated than ever. Ransomware gangs are already on the AI bandwagon, probably looking for better ways to a big payout (up to 75% of ransom payments are over $1 million).

AI has lowered the barrier to entry to new hackers. Generative AI has helped these bad actors create more believable text, images, audio, and video to hook their victims. Once in, they are able to pinpoint the high-value data for exfiltration (and higher ransoms).

The bad guys are usually first adopters of new technology. And with so much money on the line, they are only going to double down on their AI efforts.

Adapting to the Changing AI Landscape

The basics of data management are not going to change. The Association for Computing Machinery (ACM) lays out the 7 Principles of an Organizational Data Strategy as governance, data content, data quality, data access, data management, data informed decision making, and analytics.

These steps won’t change because AI processes are using the data. But it will be easy to forget the things we learned as we get caught up in the excitement of new technologies to deploy. So remember your training!

The AI revolution is here, and it’s changing the way we work, think, and interact with information. Living through technological changes can be really exciting. We are bearing witness to new technologies that are driving an information revolution. The best way to enjoy this ride is to learn more about these technologies and explore new ways to consume data streams.

After all, the ride is only beginning.

Got ideas for how AI will reshape the way we consume data? Are you building one? Join the conversation on the Tech Aunties podcast—reach out on LinkedIn!

When you think of Gigabyte, gaming hardware probably comes to mind. But this Taiwan-based computer hardware manufacturer develops much more than motherboards and graphics cards – they provide a spectrum of computer hardware as well as liquid and immersion cooling and have a long history of contributing to open standards and advancing server technologies.

I recently had the pleasure of chatting with Chen Lee, VP of Sales, HPC, Data Center and Enterprise for Giga Computing and learned a fascinating tidbit about how the company became involved in the Open Compute Project Foundation (OCP).

“Around 2004, this very little-known company came to us and said, ‘We’ve got a search engine, and we want to build this motherboard and this thing called OpenRack,’” Chen explained.

The little-known company was Google, he said.

“So that's how we got into OCP,” Chen said. “(Gigabyte was) actually the first company to help Google develop open compute.”

Gigabyte’s collaboration with Google on OpenRack marked the company’s entry into the open infrastructure movement, making them one of the initial contributors to OCP standards.

Today, Gigabyte’s portfolio extends beyond Intel and AMD servers — they also produce Arm-based solutions using Ampere technology and specialize in advanced cooling systems like immersion and direct-to-chip liquid cooling. With this holistic approach, they continue to drive efficiency and performance in the data center space, reflecting their adaptability and forward-thinking approach.

Embracing AI: Gigabyte's Focus on GPU Servers

Artificial Intelligence (AI) has reshaped the demands on data centers, particularly in terms of computing power and infrastructure. Chen discussed how Gigabyte has been positioning itself in the AI hardware game, particularly through high-density GPU servers. He shared a pivotal moment for Gigabyte in 2010 when they introduced a 2U server that could support eight double-wide, dual-link GPUs, which at the time was the highest density on the market.

Today, Gigabyte’s expertise in GPU servers continues to be an asset, providing systems for AI model training and inferencing, using cutting-edge GPUs like Nvidia’s H100 and soon, Blackwell. As AI shifts towards edge deployments, Gigabyte is also preparing for the growing importance of edge inferencing, which Chen predicts will be a significant area of growth in the near future. Industries such as medical, finance, and retail are moving fast to adopt AI solutions at the edge, from convenience store smart shelving to real-time customer analytics. Gigabyte is ready to meet these needs with high-performance, scalable server technology that suits the unique challenges of edge computing.

Liquid Cooling and Efficiency in Data Centers

The demand for powerful servers to support AI training and inferencing has pushed energy consumption to unprecedented levels, making cooling a top priority. Chen highlighted how immersion and direct liquid cooling are allowing Gigabyte to manage energy efficiency better while meeting the needs of customers working on advanced AI projects. It’s a testament to the company’s adaptability and focus on sustainable solutions—aligning well with the OCP’s values of open innovation and energy efficiency.

AI Beyond the Data Center: The Future of Inference

Chen and I also discussed moving from centralized data center training to inferencing at the edge. Today, most inferencing still happens within large data centers, using high-power systems designed for training. But Chen believes that as AI technologies mature, edge inferencing will become critical—allowing smaller, more efficient hardware to perform tasks where the data is generated, such as in retail stores, hospitals, and banks.

Chen shared an interesting example involving a convenience store, where AI systems can detect customer behavior in real-time and use edge servers tucked away in the back to provide analytics directly to the headquarters. The potential for rapid, on-site AI-driven insights will push industries to adopt smaller-scale AI inferencing solutions—a market that Gigabyte is well-positioned to serve.

This shift to the edge will transform how AI is implemented across industries, bringing smarter technology closer to users and changing how data centers interact with local environments. Chen also shared that, in his view, AI isn’t just a passing trend—it’s a new wave that’s here to stay.

So what’s the TechArena take? As AI evolves and the infrastructure to support it becomes more advanced, hats off to Gigabyte for doubling down on its strengths—high-performance GPU servers, innovative cooling technologies, and partnerships with leading hardware and storage vendors.

Thanks to Solidigm for sponsoring this delightful Data Insights discussion. In case you missed it, check out the full episode here. As AI and edge computing continue to advance, the innovations coming from companies like Gigabyte are paving the way for the data centers of tomorrow.

Explore how Accenture and Oracle are revolutionizing the telco industry with their new all-in-one BSS solution, enabling cloud-native innovation, AI integration, and new revenue opportunities for service providers.

Predicting the future of technology is always a bit of a gamble—especially in the fast-moving world of data centers. After all, we’ve all seen how quickly things can change, and the internet never forgets a missed prediction.

Still, as digital transformation continues to reshape industries worldwide, there’s tremendous value in looking ahead and trying to map out where things might be going. So, in the spirit of taking a calculated risk, I’m sharing my thoughts on three major trends I expect to define the data center space in 2025.

Spoiler alert: Data centers could soon do far more than just power the digital world.

1. Data Centers Will Shift from a Twin-Transition to a Triple Transition

The idea of a "twin transition" for data centers—digital transformation and sustainability—has been a major talking point over the last few years. The growing demand for advanced computing, driven by cloud computing, AI, IoT, and automation, has forced data centers to adopt digital transformation capacity strategies. Simultaneously, the pressure to reduce energy consumption, adopt renewable energy, and implement efficient cooling has driven a sustainability transition.

However, with the rapid rise of artificial intelligence (AI), we’re looking at the emergence of a third transition. AI brings its own set of unique challenges that go beyond digital transformation or sustainability alone. First, AI workloads demand specialized hardware like GPUs and TPUs, driving up power, density, and cooling requirements significantly. Second, because AI’s energy consumption can be enormous, achieving sustainability goals while supporting AI’s growth will be a tough balancing act. Finally, AI will demand new infrastructure, new expertise, and entirely new business models to support its operations. By 2025, this triple transition—digital transformation, sustainability, and AI integration—will be in full swing, reshaping the entire data center landscape.

2. OCP Adoption Will Accelerate, and Its Ecosystem Will Evolve

As the data center industry faces this triple transition, the need for flexible, modular, and energy-efficient solutions has never been clearer. Enter the Open Compute Project Foundation (OCP). Originally developed by Facebook (now Meta) and supported by the likes of Microsoft, Google, and Amazon, OCP has already proven itself as a robust framework for addressing the demands of digital transformation and sustainability.

What’s exciting for 2025 is the increased adoption of OCP, not just by the tech giants, but also by tier-2 industry players. Leading manufacturers like Nvidia, Dell, and Supermicro have already jumped on the OCP bandwagon, recognizing its potential to meet AI-driven demands without compromising energy efficiency. OCP’s power-efficient designs, which provide power management at the rack level, and its readiness for liquid cooling make it a compelling choice for data center operators trying to balance high performance and low environmental impact.

In 2025, OCP will continue to grow into the cornerstone of data center infrastructure, extending beyond the major cloud players to become a common standard for all types of data centers. The modular nature of OCP means that as AI workloads grow, data centers can scale quickly and efficiently, deploying custom solutions with minimal vendor lock-in. With its proven track record, OCP will play a pivotal role in helping data centers meet the demands of the digital, sustainable, and AI-powered future.

3. Data Centers Will Emerge as Energy Providers and Critical Heat Sources

Data centers have long been known for their massive energy consumption, but by 2025, they could also play a pivotal role as active participants in the energy ecosystem. This shift is driven in part by the growing reliance on renewable energy, which can be intermittent. Hyperscalers—those massive cloud operators like Google, Microsoft, and Amazon—are already beginning to explore options to ensure reliable power supplies. Some are even considering restarting nuclear power plants to meet their growing energy demands, recognizing that clean, consistent power is key to their long-term operations.

In addition to these initiatives, we’re seeing an increasing interest in microgrids powered by Small Modular Reactors (SMRs), which could be deployed to directly power data centers. These small, efficient nuclear reactors could provide the stable, low-carbon energy needed to run data centers around the clock, especially in regions where renewable sources are less reliable. With the ability to provide a dedicated, resilient energy source, SMRs could alleviate pressure on the grid while providing the high energy densities required for AI workloads and other high-performance computing needs. As data centers become more energy-critical, this could lead to them not only drawing power but also supplying it back to the grid during peak demand.

But the energy benefits don’t stop at power supply. Data centers also produce substantial amounts of excess heat, which could be repurposed for local needs. In colder climates, this waste heat could help warm nearby homes or even support industrial processes that require high temperatures. By redirecting this heat, data centers will not only reduce reliance on fossil fuels for heating but also become integrated into local energy ecosystems, boosting overall efficiency. Or as one speaker simply put it: “Data centers will give every Kwh a second life.”

As a result, the location of data centers may shift to areas where both power supply and heat demand are high, making them critical players in the future of energy grids. Data centers will evolve from simple digital infrastructure to essential energy assets, playing a key role in urban sustainability.

Looking Ahead: Data Centers as Essential Urban Assets

Now, before anyone gets too excited about the idea of a data center on every block, let’s be honest: nobody really wants a data center in their backyard. They’re noisy, energy-intensive, and often not the most visually appealing structures. But here’s the thing—just like no one wants to throw away their phone or PC because they’ve become essential to our daily lives, we’re starting to realize that data centers are becoming just as indispensable. As our reliance on digital infrastructure grows, it’s time to rethink where data centers are located and how they can better fit into residential and urban landscapes–making them quieter, more visually appealing, and seamlessly aligned with their roles in energy distribution and urban sustainability.

In conclusion, the data center industry is on the verge of a major transformation. The next few years will see data centers evolve from isolated tech hubs into integrated energy and infrastructure players. OCP adoption will have accelerated, making energy-efficient, modular designs the standard. The triple transition of digital, sustainable, and AI-driven infrastructure will drive these changes, and data centers will not just store data, but also play an active role in stabilizing the power grid and supporting urban heat networks. The future of data centers is bright, and their influence will only grow as we move further into the digital age.

While AMD has been consistent in recognizing the new demands of AI-enabled applications, the company remains steadfast in ensuring that AMD EPYC^TM  processors continue to offer leading performance for traditional compute workloads, such as HPC, database, cloud native applications, collaboration systems, finance, and more.

I recently caught up with Ravi Kuppuswamy, AMD Senior Vice President of Server Product & Engineering, to explore the company’s approach to the evolving landscape of enterprise workloads, hyperscale innovation, and the growing influence of AI.

Traditional compute applications are also adapting and adding elements of AI into their application environment, he said.

“In a wide array of apps from Microsoft, Oracle, SAP, we see them adding AI-enhanced tools such as recommendation engines, chatbots, into their application,” he said. “While massive AI models are indeed a significant step…the vast majority of real world applications still are more evolutionary and focused on general compute.”

This dual focus allows AMD to serve diverse customer needs, ensuring that cutting-edge AI capabilities don’t overshadow the ongoing importance of reliable, efficient traditional computing.

A Portfolio Built for Versatility

AMD's diverse portfolio spans CPUs, GPUs, AI NICs, and more, offering flexibility for a wide range of customer requirements. Kuppuswamy described this strategy as “letting customer needs guide the discussion,” highlighting how AMD supports everything from cost-effective solutions to high-performance configurations.

For workloads requiring heavy training or models exceeding 13 billion parameters, AMD’s CPU-GPU combinations, such as the recently launched MI300 series, provide the scalability and efficiency necessary for advanced AI applications. This approach ensures that customers can select solutions tailored to their specific operational goals and budgets.

Hyperscale Design and Energy Efficiency

During the OCP Summit, hyperscale configurations took center stage. Kuppuswamy explained how AMD collaborates with customers to design systems optimized for evolving data center demands. The focus on energy-efficient design is critical, as global technology-related energy consumption rises in tandem with increasing data generation.

AMD’s commitment to open standards plays a significant role in these efforts. By embracing interoperability, AMD fosters innovation that benefits hyperscalers as well as enterprises looking to leverage cutting-edge technology without proprietary limitations.

The Enterprise and Cloud Continuum

Enterprises are increasingly adopting hybrid models that combine on-premises and cloud computing. Kuppuswamy highlighted how AMD technologies enable customers to build robust on-premises infrastructures while seamlessly scaling to the cloud when demand spikes.

This flexibility is especially valuable for enterprises that lack the resources of hyperscalers.

Impact and Future Vision

AMD’s leadership in the data center market has grown significantly, with a remarkable rise in market share from less than 1% to 34% in recent years. This growth underscores the appeal of AMD’s energy-efficient solutions and customer-first approach.

Looking ahead to 2025, Kuppuswamy anticipates a wave of IT infrastructure upgrades driven by outdated systems nearing the end of their lifecycles. He highlighted the dramatic efficiency improvements offered by the latest generation of AMD EPYC processors: replacing 1,000 four-year-old CPUs with just 131 new-generation processors delivers the same workload performance, with significantly reduced power and space requirements.

Collaboration and Open Standards

One of the most surprising announcements at the OCP Summit was the launch of the x86 Ecosystem Advisory Group, a collaboration between AMD and its key competitors. The initiative aims to establish common standards for compatibility and interoperability, reflecting the company’s commitment to open ecosystems.

So what’s the TechArena take? As data becomes increasingly distributed across edge and cloud environments, AMD solutions empower customers to extract value from this continuum. From the high-performance EPYC 9000 series for data centers to Ryzen-powered endpoints, AMD offers a comprehensive portfolio designed for efficiency and scalability. This adaptability is critical in a world where businesses and consumers demand instant access to data and services. 

Tune in to our Data Insights podcast with Kuppuswamy. For those seeking more insights into AMD data center technologies, Kuppuswamy encouraged audiences to explore resources on their website and social media platforms.

2025 will be a year of rising costs, changing focus, and increased cybersecurity challenges.

Cybersecurity is driven by a nearly complete transformation to a digital economy. This transformation exposes unprepared organizations to a range of malicious actors.

Organizations around the globe are struggling to adequately protect their sensitive data and systems. The rising costs of cybersecurity measures, including cybersecurity SMEs, has hindered their ability to effectively mitigate risks. Moreover, a growing compliance landscape diverts resources from meaningful cybersecurity initiatives. As a result, organizations are vulnerable to a range of cyberattacks.

While 2025 may bring more of the same in terms of cybersecurity failures, I see 10 clear cybersecurity trends that organizations worldwide are likely to face:

2025 Cybersecurity Forecast: More Threats, More Challenges

1. Reliance on Biometric Authentication: All mainstream phones and most new computers have biometric authentication hardware, gaining fast adoption. For client computers, Windows has Windows Hello, and Touch ID is available on MacBooks. Windows Hello is more convenient and is more secure than traditional password-based security in many situations. Biometric authentication is standardized with NIST Special Publications 800-63 and 800-78-3 as well as an ISO standard.
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2. Rise of IoT and OT Cyberattacks Threatens Critical Infrastructure, such as Healthcare and Energy: The proliferation of IoT devices and Operational Technology (OT) systems expands the potential attack surface beyond traditional IT. IoT and OT technologies are increasingly integrated into critical infrastructure to enhance efficiency, reliability, and remote monitoring. For instance, smart grids utilize IoT sensors to optimize energy distribution, while OT systems control industrial processes in power plants.
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3. Increased Cybersecurity Compliance: In 2025, we can expect a further surge in compliance requirements globally, from cybersecurity to privacy. These draw resources away from implementing cybersecurity improvements, slow innovation, and might not improve actual security proportional to their cost. During 2024, we’ve seen a few compliance directives already, such as Executive Order 14074, which requires a range of compliance actions for federal agencies, including the implementation of a zero-trust architecture (ZTA) and California SB 1047 (Vetoed). It would have added compliance actions for businesses that collect and store personal data of California residents.
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4. Increasing Cloud Responsibility: The cloud share of the overall computing is significant and growing. According to a report by IDC, the global cloud computing market is expected to reach $1.5 trillion by 2026, with a compound annual growth rate (CAGR) of 17.5%. With the cloud’s agility and benefits comes a sophisticated platform offering a wide range of complicated security options that must be managed. The AWS and Azure consoles have more dials and levers than a 747 cockpit.
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5. More Expensive, More Time-Consuming Training and Awareness: Cybersecurity hardware, software, and personnel are expensive, but often less than 5% of the overall IT spend for an organization. As new compliance requirements and new threats come into focus, the defensive spend will need to increase until eventually it matches the offensive threat. The alternative is accepting the consequences of a cyberattack, such as financial loss, reputational damage, and legal liabilities.
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6. Ransomware Attacks will Continue to Evolve: Many enterprise systems are riddled with severe vulnerabilities, making them easy targets for cyberattacks. Once an attacker successfully exploits a vulnerability, they can gain unauthorized access.  Ransomware is among the easiest and most profitable ways to quickly monetize this access.
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7. AI will be Leveraged by Both Sides: Artificial intelligence (AI) will play a dual role in cybersecurity, with both defensive and offensive applications. While AI can be used to detect and respond to threats more effectively, it can also be leveraged by attackers to lower the technical bar.
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8. Future Quantum Threats: Long term, the development of quantum computing poses a significant threat to current cryptographic methods. Recent news of researchers at Shanghai University demonstrating quantum decryption of an RSA public key means that the timeline to crack encryption – something that many feel is a decade off – may be much sooner. As quantum decryption capabilities advance, organizations will need to begin adopting quantum-resistant algorithms to protect their data now.
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9. Nation-State-Sponsored Cyberattacks: Further blurring the line between  cybercrime and statecraft, these will expose organizations to extremely skilled and well-funded adversaries that do not need to monetize their cyberattacks. About a year ago Microsoft botched disclosure of an outstandingly well orchastrated Chinese attack on the US government through Microsoft systems.
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10.  Supply chain security: The software supply chain in particular is slowly emerging as a concern as organizations recognize the risks associated with their sprawling SaaS and software portfolios.
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Stay Informed

In 2025, organizations will continue to struggle to adequately protect their sensitive data and systems, but new threats are taking focus. The best way to mitigate these threats is to stay informed about emergent tech trends, so you can make informed decisions about organizational priorities.

In this video from SC24, experts discuss how the world’s largest SSD will transform their AI and HPC workloads, enabling faster access and accelerating growth while reducing data center footprints.

Remember how hot it has been these past two years? We all have had weeks or months of sweltering heat and trying to stay cool at work or at home. However, if you were fortunate enough to work in a data center, then you had it made. Just think of being inside a data center when it is running at its perfect temperature, between 73^o and 75^o F? How cool would that be?! However, that coolness comes with a big environmental impact.

Data centers are water guzzlers! On average, it takes about 0.48 gallons (1.8 liters) of water to cool just one kilowatt-hour (kWh) of electricity in a datacenter. In 2022, U.S. data centers consumed approximately 200 terawatt-hours (TWh) of electricity, or about 4% of the total U.S. electricity demand. And guess what? This figure is forecasted to rise significantly in the coming years due to the growing adoption of 5G networks, cloud-based services, and AI technologies. In addition, the data center construction market in the U.S. is estimated to grow at a compound annual growth rate of 6.6% from now to 2030. Finally, let’s not forget that data centers indirectly consume water through the electricity they use, as power plants need water for cooling during electricity generation. In the U.S., it takes approximately 2 gallons of water to generate 1 kWh of electricity.

The Path Forward

As you can imagine, the key challenges in building and operating sustainable data centers revolve around managing water and energy consumption, adopting innovative cooling techniques, handling e-waste, ensuring supply chain sustainability, and complying with regulatory requirements. These methods not only improve cooling efficiency, but also help reduce energy consumption and environmental impact.  Traditional methods like Computer Room Air Conditioners (CRAC) and Computer Room Air Handlers (CRAH) are still widely used and account for 80% of all data center cooling techniques, underscoring the need for new thinking. Other techniques include:

• Liquid Cooling: This method involves circulating coolants directly to the heat-generating components. It is more efficient than air cooling, and is becoming increasingly popular, especially for high-performance computing.
• Immersion Cooling: Servers are submerged in a thermally conductive, but electrically insulating liquid. This method provides excellent heat transfer and is highly efficient, though it can be more complex and costly to implement.
• Free Cooling: Also known as economization, this technique uses outside air to cool the data center when external temperatures are low enough. This reduces the reliance on mechanical cooling systems and can significantly cut energy costs.
• Hot and Cold Aisle Containment: By separating hot and cold air streams, data centers can improve cooling efficiency. This method involves arranging server racks in alternating rows with cold air intakes facing one way, and hot air exhausts facing the other.‍
• Cryogenic Cooling: Although still experimental, cryogenic cooling involves using extremely cold liquids like liquid nitrogen to cool servers. This method can significantly increase computational performance but also requires substantial energy for cooling.

Moreover, industry must focus on renewable energy sources to power data centers. By integrating solar, wind, and other renewable energy sources, data centers can reduce their reliance on fossil fuels and minimize their carbon footprint.

Unfortunately, data centers do contribute to water insecurity, particularly in regions where water resources are already scarce. The emphasis on sustainability and environmental impact will lead to more strategic and environmentally conscious decisions regarding the locations of future data centers. So, the next time you run a search for a cat video, think of the amount of water that was consumed to feed your thirst for fun.

In this podcast, MLCommons President Peter Mattson discusses their just-released AILuminate benchmark, AI safety, and how global collaboration is driving trust and innovation in AI deployment.

When compared to other industries, such as consumer electronics, the automotive industry moves relatively slowly. Historically, for the incumbent automotive OEMs, there has been a five-year development cycle for new vehicle introduction and a two-year cycle for limited platform upgrades. Initiatives like Software Defined Vehicles (SDV) are focused on addressing these long development cycles with more efficient use of R&D and reuse of vehicle platforms; however, in general, for the broader market, the SDV has not yet arrived. That said, emerging Chinese automotive OEMs who have been able to design EVs starting from a clean sheet of paper appear to be able to reduce the design cycle from five years to two years.

So looking at the crystal ball to predict the future of automotive, specifically in 2025,  I predict we will see to see mostly incremental changes that align with broader industry megatrends, sometimes referred to as CASE - which stands for:

• Connected
• Autonomous
• Shared
• Electric

Unpacking the underlying details of each of the elements of CASE will help to understand the incremental progress that will be made in the 2025 timeframe. Before I discuss each of these specific megatrends and share my thoughts on what incremental announcements we can expect to see, I want to summarize some broader industry trends that I expect we will also see.

OEMs will be even more aggressively spinning back in engineering teams and embarking on their own ASIC designs in an attempt to develop truly unique, differentiated solutions vs. those from merchant semiconductor suppliers. However, the automotive industry still relies heavily on semiconductor and sensor solutions from third-party suppliers. Because of this dependency, the OEMs provide clear signals and direction to those suppliers well in advance to ensure solutions that are aligned with OEM platform requirements and automotive platform development/deployment timelines. With such clear guidance being signaled to the solution suppliers well in advance of the actual need, predicting the future with some level of accuracy can be relatively straightforward.

It is almost a certainty that at the 2025 CES show, now one of the largest automotive shows, there will be many new concepts that will be on display, which in many cases will be an attempt to throw something on the wall to see what sticks. It is also almost a certainty that dependency on AI and the integration of the technology will see exponential growth, where it will be used to provide a more tailored customer and passenger experience in addition to addressing new capabilities like virtual reality which will help further improve automotive safety.

What is also clear is that in 2025, there will be a reconciliation and reassessment of semiconductor players that reconsider/exit the automotive industry as the Gartner Hype Cycle for autonomous vehicles has transitioned from being at the Peak of Inflated Expectations in 2016 to now entering the Trough of Disillusionment in 2025.  Typically, there is significant over-investment at The Peak of Inflated Expectations which then gets reigned in once the market enters The Trough of Disillusionment – Intel’s relatively recent spinoff of Mobileye is a good example of what can be expected going forward as the true market size and the lengthy time to revenue for automotive and ADAS especially comes into focus.  

Semiconductor startups with a pure-play focus on autonomous vehicles will more than likely get acquired, go out of business, or pivot to focus on other markets. The costs of semiconductor development are significant, and while innovative solutions can yield differentiated results, automotive OEMs tend to be risk-averse and reluctant to embrace new semiconductor suppliers in critical application areas. The very long time to revenue, for ADAS and automotive in general, will eventually cause venture capitalists looking for large returns in a short period to lose their patience and exit the ADAS pure-play semiconductor companies.

Another broader trend is that software competence, along with AI, is becoming a critically important differentiating factor for ADAS, connectivity, and in-vehicle experience. Historically, auto OEMs have been referred to as “metal benders” where most of the other elements of the vehicle were addressed by third parties. As today’s leading vehicle contains 100 million lines of code – going to 1 billion lines of code by the next decade – we can expect that in 2025 and beyond, a great deal of focus is going to be placed on developing software competencies that not only address the operational domains of the vehicle but also support the new mobility ecosystems driven by consumer trends and demands. Software engineering can easily prove to be a bottleneck that will need to be addressed.

Connected

The connected tenant of CASE does a lot of heavy lifting, as many different key attributes of the vehicle are captured in this trend. To pause and give a feel for the importance of connectivity in the automobile, consider how anxious you feel when your internet connection is down, or how useless your tablet would be if it had no support for internet connectivity. Going forward, without connectivity, the vehicle will feel just as useless as the unconnected tablet does today. Some of the fast-growing, underlying trends that will necessitate a network connection include:

• Support for the Digital Consumer/Online Vehicle Purchases
• Personalized Services/Context Awareness
• Feature Upgrades/Everything-as-a-Service
• V2x - vehicle-to-vehicle Communication
• Over-The-Air (OTA) Updates

The analogy of the tablet (or perhaps smart TV) is also very analogous to the automobile in the manner that multiple high-resolution displays will be found throughout the vehicle that will support and provide an immersive digital experience reliant on connected service to deliver personalized content based upon user context awareness.  In 2025, I predict you will see more and larger displays in vehicles being announced with many touting 8K resolution.  

I also expect more OEMs will announce V2x, which enables vehicle-to-vehicle and vehicle-to-infrastructure communication, greatly enhancing vehicle safety. This increase in vehicles supporting V2x will be driven both by OEMs citing safety-consciousness and also by NCAP (New Car Assessment Program), a collection of similar government regulations instituted across many countries to specify which technologies must be deployed in a vehicle model on an annual basis to receive a high safety rating. In time, just like seat belts, airbags, or rear-looking cameras, these safety features will ultimately become mandatory in new cars.

In 2025, I expect more OEMs to announce, or perhaps more appropriately, preannounce OTA support in conjunction with SDV which will be based on an underlying centralized E/E architecture (see blog). Cybersecurity will also be a big topic that will be discussed and addressed in 2025, including OEM and auto semiconductor supplier announcements to embrace the ISO 21434 cybersecurity standards (see blog). This will be essential as this fully connected platform will have multiple potential attack points that raise a great threat for cybersecurity hacking.

As mentioned earlier – expect to see more AI in the cabin allowing for greater tailoring to both the driver's and passenger's personal preferences. Also, expect greater integration of the vehicle with the consumer lifestyle – including smartwatches and smart homes. In many cases, these will be announcements with demonstration platforms, however mass deployments will most likely happen in the future.  Because the consumer’s vehicle purchasing decision is increasingly driven by the capabilities enabled by the connected car and provides an opportunity for OEMs to sell lucrative after-market services/subscription-based services, I expect this space will see a lot of pre-announcements and “concepts” as, in this area, automotive OEMs have dire FOMO (fear of missing out).

Autonomous

The promise of fully self-driving cars for consumers is going to continue to see a lot of heat and light in 2025, however, practical deployments will continue to get pushed out in time for several reasons: technical feasibility, legislation, and end price.  

To date, only one major OEM has been certified to be able to support Level 3 ADAS in the US - a far cry from Level 5 full autonomy. Level 3 still requires the driver to be engaged and ready to take over control of the vehicle while the car is in a “semi-autonomous” state. In the case of this one major OEM, there was a significant number of “ADAS disengagements” for given miles traveled – an indication that the ADAS system was overwhelmed, and a driver was required to step in. It’s also important to note that the current legislation in the US limits the roadways where level 3 can be deployed; these roadways are considered to be low risk/suitable for L3 capabilities.  ADAS disengagements on low risk roadways are a reflection of the complexity of the AI problem and the fact that a combination of more AI training and more AI performance (TOPs - Tera Operations Per Second) is required to reduce ADAS disengagements.

While robotaxis today offer full autonomy, they operate in a geofenced area (limited roads and very controlled range) and have exorbitant costs driven by extremely high compute performance processors and sensors. Given their business model, robotaxis can eventually amortize those costs much more easily than the consumer-owned vehicle.

I predict announcements of higher-performance AI processors targeting L3 ADAS with integrated in-vehicle-infotainment features. This is in alignment with the move towards the centralized E/E architecture. Auto OEMs will also be announcing more vehicles with L2+ to L3 support – available “soon”. In support of ADAS, many of the key sensor technology manufacturers will be highlighting next-generation technologies that will allow for lower total system cost and higher accuracy. 3D radar will be a hot topic as will solid-state LIDAR. Also, expect to see demonstrations of ADAS cameras with night vision – directly challenging the need for LIDAR. The lower cost, higher accuracy sensors will play a key role in driving the viability of ADAS for the masses.  

In-cabin driver monitoring systems (DMS) will also see a significant uptick for reasons similar to V2x - where NCAP is increasingly driving this as a mandatory feature.  Legislation is currently being considered in the US, and has passed in the EU, that DMS should be a required feature regardless of ADAS level, to detect drowsy or intoxicated driving. We will see increasing support for occupancy detection systems - additional in-cabin cameras, where again, legislation is being considered to make this capability mandatory to avoid fatalities associated with forgotten children or pets due to heat exposure. These cameras in turn will serve a dual purpose and will also be integrated into the in-vehicle infotainment systems in support of gesture recognition where hand motions can be used to control functions in the car.

Shared

I predict we will see announcements of new entrants in the shared market space (transportation as a service) in addition to partnership announcements between applications providers as Gen X and Gen Z are showing trends toward moving away from vehicle ownership and getting their driver’s licenses at a much later age than previous generations. The cost-conscious generations opt for an Uber rather than taking on the costs associated with vehicle ownership. In general, the concept of having the flexibility to choose the best vehicle for a specific need on-demand via a smartphone is gaining strong popularity.

Furthermore, the aging population will gravitate towards robotaxis to continue to have freedom when they are no longer able to drive. Areas of high population densities will also gravitate toward subscription-based transportation eliminating the need to find parking and pay exorbitant parking fees. As proven demand continues to grow, you can expect to see more entrants in this market either through the introduction of new players or incumbent OEMs finding this market lucrative. To that point, Tesla has continued to signal the intent to enter the robotaxi / shared market for some time. 

Also, expect to see announcements and demonstrations from OEMs offering brand consistency across robotaxi fleets – where the passenger’s personal preferences are downloaded into the robotaxi as soon as the vehicle has been hailed ensuring a seamless and consistent experience between any shared vehicle of a similar brand – similar to the continuity across different types of Apple products and platforms.

An interesting trend, which Mobileye has taken the lead in moving towards, is to pivot from being only a semiconductor solutions provider to the automotive OEMs to now getting into the shared transportation business directly – expecting to collect end revenues directly from shared riders. As the automotive semiconductor suppliers build more complex, complete solutions stacks, it is unclear if others will follow that lead, but I could see how it would become enticing.  That’s a hard one to call for 2025.

Electric Vehicles

Lastly, I predict we’ll see more announcements and introductions of EVs. This is not only driven by EV mandates but increasing consumer preference. EVs also offer significantly lower barriers to entry in terms of R&D and manufacturing costs as compared to vehicles based on the Internal Combustion Engine (ICE). This has already led to many new market entrants, most notably Tesla, but increasingly from Chinese OEMs, as they see a way to penetrate the high-revenue, high-growth automotive market.

Key technology areas of focus and announcements will include:

• Improved battery technologies
• OEMs’ shift to higher voltage architectures
• Increased availability of and extended capabilities of wide bandgap semiconductors.

These announcements will lead to lower costs, longer battery life, greater energy efficiency, and reduced range anxiety. Suffice it to say, that the EVs will also support the aforementioned Connectivity, Autonomous, and Shared trends and capabilities.

In short, the automobile, and the automotive industry, has already dramatically changed as we know it today. While many different acronyms exist that summarize the automotive industry megatrends, CASE works reasonably well. In 2025, we will see more incremental steps and announcements (and plenty of “pre-announcements”) and demonstrations that are aligned with realizing the vision of CASE.  

One prediction I am 100% confident about is that I got some predictions wrong and missed others. Time for a new crystal ball.

Do you hear that sound? The pitter patter of the second half of the decade marching towards us? As we ready ourselves for the close on the first half of this monumental decade, the team at TechArena has been busy exploring those things that have passed, and those yet to come.

As I researched this article, I read back on predictions made in late 2019 about the first half of the decade's advancements. While many predicted AI's continued march, no one called the behemoth that is generative AI and Chat GPT. Many predicted mass advancement of 5G technology, and while 5G proliferation has grown around the globe, we've debated whether its true benefits, such as network slicing and cloud native automation, have been fully delivered.

Others forecasted massive advancements in autonomous driving, and the reality today is far from the 2019 vision. A fearless few called out a rise in remote work. While no one in the mainstream had a global pandemic on their bingo cards, those targeting that trend certainly have much to be proud of.

With this in mind, we dip our toes into the second half of the decade and what's to come. We'll have a series of articles this month from various experts from across the tech landscape weighing in on their relative domains of expertise. To get us started, I'll offer my five top trends for 2025.

1. We'll see the world's first gen AI scandal in 2025: Yes, we did all discuss Air Canada and Scarlett Johansson in 2024, but the tech landscape has not yet seen a major gen AI snafu impacting brands or challenging consumers. With enterprise adoption of AI on the rise in a time of "evolving in real speed" best practices for fine tuning of algorithms, I expect 2025 to usher in a gen AI scandal that will raise the stakes on a broad scale and customer facing adoption.
   
   In the coming weeks, we'll be featuring stories about how the industry is responding to AI safety, and I think these initial efforts will be accelerated in the second half of the decade as more organizations fully realize the value of AI to real business processes, and the threat of inaccurate or uncontrolled adoption of AI agents misrepresenting brands.

2. We'll see a shakeup in the silicon landscape in 2025: Pat Gelsinger's departure at Intel gives us our best shot at completely changing the semi landscape with a breakup or acquisition of the semi giant on many people's minds. But I see broader disruption of the landscape with large cloud players seeking silicon portfolios and talent to lock control of performance advantage in this new accelerator driven computing era. I think we'll see more than one AI accelerator acquisition in 2025, and potentially moves to drive further integration of CPU + Accelerator + connectivity as data center network capability becomes more front-and-center in terms of performance bottlenecks.
   
   
3. We'll be talking about supply chain and sourcing in 2025: With the incoming US administration's focus on tariffs, the complex manufacturing that data center operators rely on will be challenged. While we don't yet know the practical impact of global tariffs, I expect the industry to be challenged with component supply from a very different source than pandemic era shortages. With many manufacturers basing production facilities in Mexico, for example, we may see impact to AI optimized server availability in the US market. And this is, of course, before any potential responding tariffs implemented by other countries in response to US actions.
   
   
4. Agentic AI will consume our conversations in 2025: Agentic computing represents the next wave of gen AI adoption, and this form will drive increased automation of work and decisions into the enterprise. While we started discussing agentic computing this year, I expect many enterprises to begin pilots of agent deployments next year, similar to initial gen AI pilots over the past two years. And while we may share a top level vision of what agentic AI promises, the implementation and compute models deployed for its delivery will come more fully into focus in 2025.
   
   
5. Spatial computing will garner our attention in 2025: While many have talked about the metaverse and virtual reality taking over the center of computing, the reality has been more modest in this space. I see a more pragmatic approach to augmented reality environments with narrow use cases, such as work collaboration and healthcare, to take center stage in 2025, reflecting the staying power of remote experiences in our society. These spatial computing experiences, driven by actual use case requirements and fueled by AI, will seep into our worlds, re-defining a Teams or Zoom meeting into a fully collaborative experience, and giving visualization tools to everyone from doctors to retail platforms to improve the quality of virtual engagements.

Want to hear more about 2025 and the second half of the decade? Watch this space as TechArena experts chime in over the coming weeks on their insights into what's next in tech. For now, I'd love to hear from you on LinkedIn about your views on what's coming in the new year.

In 2015, the now famous Jeep Cherokee cyberattack made the automotive community and car owners alike suddenly aware of the significant liabilities that could be posed by attacks on vehicles’ electronic control systems. In this breach, security researchers remotely accessed the Jeep and were able to gain control over the vehicle functions including steering and braking. They gained access to the vehicle through its entertainment system via the cellular connection responsible for internet services. And while a software patch was provided to address this vulnerability, this attack raised a heightened awareness of the vulnerabilities of the connected car. There have since been other attacks demonstrated with differing levels of severity from manufacturers including BMW, Corvette, Nissan, and Tesla.

With over 500 million connected vehicles on the road today and lines of software in the vehicle exploding from 150 million up to 1 billion by the end of this decade – increasing possible cyber attack points – cybersecurity is increasingly getting a greater industry focus. This is for good reason; just by surveying the different networks in the car and their impact on the control over the vehicle, it’s clear that there is a need to ensure a critical focus is placed on cybersecurity.

Automotive networks - LIN, CAN, FLEX-RAY, and Ethernet provide different forms of connectivity within the car. The different types of networks address the unique performance requirements of the different Electronic Control Units (ECUs). They also provide opportunities for cyber attacks. The ECUs themselves have direct control over various aspects of the vehicle, which includes:

●  Engine Control

●  Transmission Control

●  Steering Control

●  AirBag Control

●  Braking Control

●  Navigation Systems

As one can see, the motivations are very high to ensure robust defense against cyber security attacks.

And again, with the growth in lines of software in conjunction with the move to the Software-Defined Vehicle (SDV), Over-the-Air Updates (OTA) will be commonplace with every update holding a real risk for containing malware and for that malware to go undetected. Exposing a car to the Internet makes it vulnerable to cyber-attacks if software isn’t written properly, which could render the car unstable or dangerous.

In August 2021, the ISO 21434: 2021 international standard was introduced. This standard specifies the engineering requirements for vehicle cybersecurity with the intent to reduce the risk of cyberattacks by embedding cybersecurity best practices in the automotive industry. The focus is on the protection of automotive electronic systems, communication networks, control algorithms, software, users, and underlying data from malicious attacks, damage, unauthorized access, or manipulation.

It’s key to note, the standard does not specify how to implement cybersecurity solutions per se, it specifies best practices to be used in designing a system in a manner similar to Systematic fault coverage associated with the ISO 26262 functional safety standard. Systematic fault coverage doesn’t identify how to implement functional safety, but provides a methodology to ensure industry best practices for safety are used in the design, test, and verification of a device / system and software. Interestingly, the ISO 21434 specification is not a mandate but a recommendation. Per ISO  21434 specification; “automotive suppliers and OEMs should strongly consider integrating ISO 21434 into their current process.”

Functional Safety and Cybersecurity are interdependent. A vehicle cannot be safe if its behavior can’t be predicted or controlled in the desired manner. One of the first tasks in designing to address cybersecurity is to perform a Threat Agent Risk Assessment (TARA) which looks to prioritize specific areas that are critical and have high vulnerabilities to attack. Many of the modeling techniques employed in the defense industry are being employed in TARA. Suffice to say that the area of cybersecurity is very complex and could fill many pages without even scratching the surface on this topic.

Interestingly, as part of the modeling, there is a detailed review of the threat agents – which identifies the different parties that would have motivation to try and hack the connected vehicle. The list is quite long and ranges from car thieves whose motivations are quite clear, to radical activists who are looking for fame and glory and also includes the poorly trained employee who unintentionally designs in a threat. This modeling is then used to develop a cybersecurity strategy and plan.

In conjunction with TARA, there is a common exposure library (CEL) that is used to identify all the possible areas of exposures and vulnerabilities associated with the connected car. These include:

● WiFi

● Cellular Connection

● Bluetooth

● TPMS (Tire Pressure Monitoring System)

● OBD II (On-Board Diagnostics Port)

● USB

● EV Charging Port

● V2x  (Wireless vehicle to vehicle and infrastructure connectivity.

Vehicle to “x” V2x, which is seeing different levels of adoption by geography, allows for vehicles to speak to one another and a smart city infrastructure through wireless connectivity that is loosely based on WiFi.  Because vehicles can talk to one another, events like multi-car pile ups that typically happen in situations where there is poor roadway visibility can be avoided simply by communicating to cars approaching that the car in front is stopped. V2x is typically connected directly to the ADAS system, assuming control over the vehicle, specifically to address these types of situations. One can easily envision how dangerous it could be if this communications network got spoofed, again, underscoring the need for robust cybersecurity.

But it’s not just about gaining control over the vehicle's operation that presents a cyber security risk. There is a considerable amount of personal, confidential data that is now contained in the car – ranging from personal credit card payment information for EV charging, to biometrics data which are being collected as a means to use AI to tune the cabin to the driver’s and occupants’ desires. A recent podcast with Lin Sun Fa, CEO at Emobi – a supplier of digital infrastructure for a secure and seamless EV charging experience – shined a light on some of the different security challenges that exist in EV charging and the level of personal information that can potentially be accessed.

While I have barely scratched the surface on this complex topic and technology, it’s clear that the importance of cybersecurity cannot be overstated, and the importance only continues to grow as Software-Defined, connected vehicles with OTA and a massively growing code base become more common.

Learn how Graid is changing the game for AI and data-intensive workloads, breaking through bottlenecks and allowing users to maximize output from their HPC infrastructure.

Introducing a new technology into a workplace, even a change as minor as a user interface update, can feel like walking a once-known pathway with our shoes on the wrong feet.

I was given a choice recently: Continue evolving in a known routine from well-worn pathways of those who went before me, join the pack regressing into the basics that worked 20 years ago, or jump into a new solution space again.

It’s easier to be a passive observer and supplier than advisor, solution explorer and creator. However, a close mentor of mine said, after working with me on two major technology transformation efforts, “You’re at your best when you’re a bit scared.”

So, I decided to stare down fear and jump into a role leading Intel’s AI Center of Excellence. This will give me the chance to leverage my background in products and technology, while exploring answers to the question I’ve been asked by execs so often throughout my career: “What do we do about this thing?”

Artificial intelligence (AI) is at a very interesting stage of development, with some entertaining extremes. A customer told me once that AI is marketed as end-to-end, but in reality, it’s End 1 and End 2 with no middle at this point.

Think of End 1 as the AI race to the moon – the paper publishing, consultants, visionaries, human augmentation:

• PhD’s building and training new frontier or foundational models, publishing papers, yet facing appetite and spending challenges for the many supercomputing cycles they need access to.
• Top 5 consulting firms, as in every major technology transition before AI, are threatening enterprises will be left behind – next year they must get on the bus - “use our framework to keep you ahead of your competition!”
• Savior/human replacement/threat to humanity – this ‘new’ technology will out-think, out create, out execute their human equivalents on everything from writing novels to self-directed R&D. We can colonize Mars, solve nuclear fusion, and enter a new age of abundance.

End 2 is humans training humans, humans training applications, ‘no programming required’:

• Social media influencers offering AI master classes online. One $40 course for beginners promises thousands of dollars in passive income from applying what you learn about AI agents – don’t get left behind, sign up now!
• AI applications in personal computers leveraging local hardware purchased for individuals, being trained by humans to add to their productivity “soon.”
• Computer programming evolves to “prompt engineering.” Software engineering has never stopped evolving to higher and more human friendly languages over the last 50 years, now you can use English. In exchange for ease-of-use and interfacing with the machines, there are real consequences to swearing at them for not understanding you. Best say ‘please’ and ‘thank you.’
• It’s just predictive statistics and pattern matching on steroids. 

Extreme naysayers may miss that AI is a building block in many of the products and services they already consume, from search engines and e-commerce sites to streaming services. There absolutely is a boom in new infrastructure from AI – this generations’ race to the moon – and it has been such a long time coming to fruition, given the field is mature enough to have dedicated university degree programs.

What excites me the most about new technology is seeking out the new twists formed from scarcity of ideal solutions to an overabundance of non-ideal components, combined with scrappy solution “MacGyvers.”

What challenges and what promise do you see in AI? Comment on LinkedIn to start a conversation.

This special In the Arena episode features co-host Robert Bielby and Emobi CEO Lin Sun Fa as they dive into EV security, advanced charging tech, and the future of automotive innovation.

We had the delight of sitting down with Bob Rogers, CEO and Co-founder of Oii.ai, to discuss leadership, what inspired his career in tech, what trends or emerging technologies he believes will significantly impact the industry, and more.

A veteran in tech and former chief data scientist at Intel, Bob is also an author and changemaker. His company’s AI-powered simulation platform, Optii, ensures that enterprises achieve their product availability service goals with the lowest capital requirements possible.

We learned that Bob is a surfer, an ex-soccer player, a physics nerd who fell in love with the power of computers early on – and a thoughtful leader who believes mutual respect and trust is key to inclusion. Read on to learn more about his journey in life and in tech:

1) Q:  What is a phrase that most defines you as a leader?

A:  I’m an ex-soccer player. That means I know how powerful a team in synergy can be, so I want to create big goals for the team and make sure everyone knows they have an important role to play in achieving those goals together.

2) Q: What inspired you to pursue a career in technology?

A: I was a physics nerd who discovered that it was easier to model complex systems (like ultra-massive black holes in other galaxies) on a computer, rather than to work out the equations the hard way! Once I got a taste of this power, there was no turning back. I also realized early on that it was a great way to get paid for doing work I enjoyed anyway.

3) Q: Share an example of a risk you took that helped shape your career path.

A: I left my physics postdoc at a respected research center to co-found a quantitative futures hedge fund, based on forecasting models I hadn’t developed yet. Looking back, I guess that was a risky move.

4) Q: Tell us about a job that you hated.  

A: Haha. As a 12-year old, I was asked to pick “big” rocks out of a recently rototilled yard.  The problem was, I didn’t know the threshold for what a “big” rock was. As the sizes got smaller, the number of rocks got larger and I spent a lot of time at it. In the end, the boss was irritated that I spent so much time on such a “simple” task. It was a great lesson about making sure everyone understands the definition of success before starting on something.

5) Q: What’s a pivotal challenge you faced? How did you navigate it?

A: Early in my career, I didn’t know the difference between a technology and a product.  I built an amazing technology (a trading platform that had made money eight years in a row) – but when I tried to market it to institutional investors, it took me a year to realize that their specific needs weren’t served by the way I had structured the company.  Great tech, no product. That lost year cost me a lot.

6) Q: Wow – can you share a bit about the lessons you learned about how to effectively productize?

A: Understand what your customer needs. It seems obvious that every investor wants high returns, but that's not enough. My institutional investors needed a futures-based investment vehicle that fit their compliance and legal requirements. That meant a very specific instrument called a "managed futures account," rather than an LP or other vehicle.

More generally, to learn what your customers need, listen to their storytelling, walk a mile in their shoes, and watch them work with your prototypes. You will see where you are on track and where you have drifted off course. Critically, it's not what they say they need, but what you observe they need that will guide you to success. 

7) Q: Tell us about a moment you are most proud of in your career.

A: When I was Chief Data Scientist at Intel, I co-led an amazing program called “Intel Inside: Safer Children Outside.” We built AI automation models to help the National Center for Missing and Exploited Children (NCMEC) process millions of reports of online child exploitation. When we started, there was a 30-day backlog for processing reports and getting crucial information to the authorities. My proudest moment was two weeks after we deployed our AI: I learned that the NCMEC backlog had been completely cleared, and law enforcement was able to receive information the same day the crime was reported. The impact on child safety has been measurable.

8) Q: How do you approach building a company culture that encourages innovation and inclusivity?

I encourage respect and mutual trust among my team members and between them and me. Don’t second guess your team when they take risks, regardless of the outcome. At the same time, to ensure that environment of mutual respect and trust is supported, immediately call out (privately) anyone who breaks the trust and respect paradigm.

9) Q: What mindset do you want to instill throughout your team?

We are changing the world for the better. We might have some moments where we are working harder or longer than we’d like, but we’re doing it for a reason. Also, part of making the world a better place is ensuring that team members have the time and mental space to be present with their families, so these two things have to work hand-in-hand.

10) Q: What trends or emerging technologies do you believe will significantly impact your company or the industry?

AI is only at the beginning, and there will be a next generation of AI coming soon. Right now, GenAI doesn’t know anything… it just talks a good game. When AI knows things for real, and can take a position and reason on it, that will dramatically change the utility of AI in our work and our lives. I’m paying close attention to the tech and building for that future.

11) Q: How do you handle setbacks or failures, both personally and as a leader?

Just keep putting one foot in front of the other and present as positive an interface as possible. My first wife died suddenly at age 51. To help my children get through that, I just had to keep going every day and model resiliency for them. Setbacks in a startup require the same mindset.

12) Q: What attributes do you most admire in others that you wish you had?

Ability to clearly and immediately see the right action, take it, and communicate it, regardless of how angst-y it might be. This is especially true with personnel: You want to give people a chance to course correct, but your instincts are usually right, and the longer you wait to make a personnel change, the more you hurt everyone else in your organization.

13) Q: Name a role model or mentor and a key thing you learned from them.

Shahin Hedayat, my co-founder at Apixio and an incredibly successful startup CEO, is always firm, calm, and clear. He never leaves room for ambiguity and makes sure that every goal is well-defined. He can be firm and kind simultaneously.

14) Q: What practices or habits do you rely on to keep your mind sharp and your energy up amid the demands of being a CEO?

Exercise is crucial. I surf whenever I can, even though I’m a pretty rough surfer (in Hawaii they would probably call me a kook). Inland, swimming and low-key cycling are good substitutes.

15) Q: What advice would you give your 23-year-old self?

Ask more questions and listen. In school, you get the mistaken impression that what you know is the most important thing. In life, being able to ask questions, and knowing what you don’t know, are even more important. It took me a while to learn that, but when I did, it was a game-changer for me.

16) Q: On a personal note, what team(s) do you root for?

SF 49ers, SF Giants, Man City

17: Q: Where would people find you on a typical Saturday?

Home Depot

18) Q: What is the book that you most recommend?

A: You mean, other than my own books? Lol. I loved The Alchemist by Paulo Coelho. Think Again by Adam Grant, and How to Read Water by Tristan Gooley are also thought-provoking.

19) Q: What’s at the top of your playlist?

A: Anything hard rock.

20) Q: What superpower do you have that people don’t know about you?

I’m a “night thinker.” I’m pretty slow to figure things out during the day, but if I think about a problem before I go to sleep, I will often have the entire solution worked out when I wake up the next morning. It’s not 100% reliable, but it’s pretty handy when it works!