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In today's rapidly evolving technology landscape, driven by new disruptor #1 – Artificial Intelligence (AI), a healthy technology ecosystem is crucial for fostering innovation, growth, and sustainability. For the foreseeable future, which will span years, AI will remain the leading disruptor. Like previous technology disruptors, it requires a thriving tech environment that encourages open collaboration among various stakeholders, including startups, established companies, and academic institutions to deliver adoption at optimum speed. A collaborative approach enables the sharing of knowledge, resources, and expertise, leading to the development of groundbreaking solutions that address complex global challenges.

How is a pre-AI company to survive? One approach is to dive in and engage within a strong technology ecosystem, fostering a value exchange between its participants. The value exchange can be broken down into three main pillars: product/service training, product/services innovation access, and co-marketing that drives mutual sales.

1. Training ensures that all participants have the necessary skills and knowledge to mutually contribute positively, ultimately driving market growth. By investing in education and professional development, companies can cultivate a workforce proficient in the latest technologies and capable of driving future advancements. Downstream ecosystems will also thrive as the training moves from technology to solutions messaging in a clear and cohesive story line.  
2. Innovation access allows companies to leverage each other's technological breakthroughs and research. By sharing intellectual property and collaborating on R&D projects, organizations can accelerate the development of new products and services, ultimately benefiting the entire ecosystem. This open exchange of ideas fosters a culture of continuous improvement and adaptability, which is essential for staying competitive in a dynamic world.
3. Co-marketing emphasizes the importance of joint efforts in promoting and distributing new technologies. By working together on marketing campaigns and product launches, companies can amplify their reach and impact, tapping into new markets and customer segments. This collaborative approach not only enhances brand visibility but also strengthens relationships between partners, creating a more cohesive and resilient technology ecosystem. Moreover, by pooling resources and aligning efforts, companies can bring products to market faster, reducing time to market and gaining a competitive advantage.  

Getting back to disruptor #1, Artificial intelligence (AI) plays a significant role in enhancing a healthy technology ecosystem. AI-driven tools and platforms enable companies to analyze vast amounts of data, identify trends, and make informed decisions more efficiently. In a collaborative ecosystem, the use of open AI technologies can significantly enhance cooperation and knowledge sharing. Open AI systems provide greater accessibility, flexibility, and interoperability, allowing companies to integrate AI solutions seamlessly and share advancements with their partners. In contrast, closed systems may limit collaboration and hinder the collective progress of the ecosystem.

An unhealthy technology ecosystem can be seen in environments where collaboration is stifled, and companies operate in isolation. This fragmented approach can result in duplicated efforts, wasted resources, and missed opportunities for growth. Certainly, in the emerging ecosystem of AI-driven platforms, we are already witnessing gravitational pull develop between disparate platforms. For example, closed markets led to the emergence of DeepSeek, an amazingly cheap alternative to the minds of AI excellence from Silicon Valley. However, whether it was truly innovative or capable of fostering a healthy ecosystem remains to be seen…time will tell.

Collaboration with partners and stakeholders is essential for technology companies to thrive. By working together, companies can pool resources, share risks, and leverage strengths to develop innovative solutions at Internet speed. Ultimately, a cooperative and collaborative approach fosters a vibrant technology ecosystem. If you would like to discuss how you can participate or foster your own ecosystem motions, shoot me a note at keate@techarena.ai.

Join us on Data Insights as Mark Klarzynski from PEAK:AIO explores how high-performance AI storage is driving innovation in conservation, health care, and edge computing for a sustainable future.

Discover how OCP’s Open Chiplet Economy is setting hardware and software standards to drive chiplet innovation, enabling scalable, modular solutions for AI and HPC growth.

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Europe laced up its running shoes and hurtled into the AI race today, announcing a €200 billion InvestAI initiative – including a €20 billion fund for AI gigafactories across the EU – during the Artificial Intelligence (AI) Action Summit in Paris.

The EU sprinted into the global AI race on the heels of a flurry of announcements, including China’s unveiling of DeepSeek, an open-source generative AI model outperforming OpenAI's GPT models, and the U.S.'s $500 billion Stargate initiative aimed at maintaining American AI dominance. The convergence of these major initiatives sets the stage for a fierce contest among global powers to lead the future of AI and underscores the critical importance of this disruptive technology.

InvestAI – designed as a public-private partnership – aims to democratize access to AI resources, enabling not just tech giants but also startups and researchers to participate in AI development.

“AI will improve our healthcare, spur our research and innovation and boost our competitiveness,” said Commission President Ursula von der Leyen during the historic announcement. “We want AI to be a force for good and for growth. We are doing this through our own European approach – based on openness, cooperation and excellent talent. But our approach still needs to be supercharged.”

The recent launch of DeepSeek has stirred the global AI community, placing some players on their heels and others seeking for ways to leverage the new model for market opportunity. DeepSeek features reportedly superior performance and innovative training techniques that have brought new efficiency into model development. While this has raised questions about the accuracy of reported costs and the transparency of its training process, its efficiency highlights China’s advancement in AI leadership. The Chinese government has invested heavily in AI, with companies like ByteDance, Alibaba, Tencent and more committing billions to bolster AI capabilities both domestically and internationally.

Meanwhile, in the U.S., OpenAI’s Stargate project, backed by a $500 billion investment from Microsoft, NVIDIA, and other tech giants, reflects America's determination to retain its AI leadership. Stargate’s focus on large language models (LLMs) and cutting-edge AI infrastructure demonstrates the U.S.'s aggressive approach to pushing LLM capability. With hyperscalers leading the charge, the U.S. is betting on its established tech ecosystem to create a geo-political advantage on the world’s stage.

Europe’s InvestAI offers a refreshing approach. While the U.S. and China focus on strategic dominance, the EU is emphasizing openness, collaboration, sustainability, and trustworthy AI. The gigafactories funded through InvestAI, according to the announcement, will foster an environment where companies of all sizes – not just tech giants – can access large-scale computing power to develop advanced AI models. This cooperative approach reflects Europe’s broader regulatory framework, including the recently passed AI Act, which sets global standards for ethical AI development. (Also, check out TechArena's recent AI Ethics Great Debate here.)

AI Infrastructure for the Future

The EU’s investment is designed to fund four AI gigafactories specialized in training next-generation AI models. These facilities are critical for breakthroughs in fields like medicine, climate science, and biotechnology. With each gigafactory housing 100,000 AI chips, Europe’s AI infrastructure will rival similar facilities worldwide.

The European Investment Bank (EIB) will play a key role in financing these projects. The EU budget will help derisk private investments, encouraging public-private partnerships to drive AI advancements.

The Sustainable AI Approach

The InvestAI initiative aligns with the EU’s broader goals of reducing energy consumption and promoting environmental stewardship.

The gigafactories will be required to adhere to the Energy Efficiency Directive, which mandates regular disclosures of energy and water consumption, the use of renewable energy, and efficient cooling systems. This reflects Europe’s commitment to balancing technological innovation with environmental responsibility—a stark contrast to the unchecked growth of energy-hungry data centers in other regions.

Additionally, the AI Act mandates that AI developers document and report on the energy efficiency of their models. While some critics argue that these regulations may slow innovation, they ensure that AI development in Europe aligns with societal values and long-term sustainability goals.

So, what’s the TechArena take? With InvestAI, Europe is making a decisive move to shape the future of AI on its own terms. The initiative reflects a commitment to openness, collaboration, and ethical innovation, distinguishing Europe from approaches in the U.S. and China.

However, challenges remain. Balancing regulatory oversight with the need for rapid innovation will be critical. Europe must also ensure that its investments translate into tangible outcomes, fostering a thriving ecosystem of AI startups, researchers, and industry leaders.

As the AI race continues, Europe’s success will depend on its ability to leverage its unique strengths—a commitment to sustainability, a robust regulatory framework, and a collaborative approach to innovation.

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Chinese startup DeepSeek recently made quite the splash in tech news with their large language model (LLM) DeepSeek-R1. This open-source model is quickly catching up in capabilities to popular models, such as those from OpenAI. DeepSeek claims that DeepSeek-R1 was trained at a far lower cost than other models.

What does it all mean?

Defining Terms: What is a Model?

According to IBM, “an AI model is a program that has been trained on a set of data to recognize certain patterns and make certain decisions without human intervention.” It works by using algorithms against large amounts of data.

A large language model (LLM) is an AI model that has been trained on a huge amount of text from books, websites, and other sources to understand and generate human-like text. You’ve probably interacted with an LLM by asking it questions or giving it prompts. The LLM can respond with relevant information, write essays, have conversations, and more. It's been programmed to understand the context and nuances of language to provide helpful and coherent answers.

IBM explains it like this: LLM models are “defined by [their] ability to autonomously make decisions or predictions, rather than simulate human intelligence."

Examples of popular LLMs include OpenAI’s GPT-3 and -4, Google’s BERT and T3, Facebook’s RoBERTa, and DeepSeek-R1.

What is a GPU?

GPUs, or graphical processing units, are hardware processing cards that were originally developed to enhance graphics for video games and virtual desktops. Someone realized that the intense math required to deliver stellar graphics was similar to the math data scientists did to create AI models. This helped speed up the systems hosting the models, which led to performance improvements.

Not surprisingly, the prospect of training a model that required fewer GPUs shook up the industry! In fact, the industry was so shaken that NVIDIA lost nearly $600B in market value the day DeepSeek made their announcement.

What's Behind the Excitement about DeepSeek?

One major reason for excitement over the new model is that DeepSeek claims to have built an open-source model that does what Open AI’s models do, but at a fraction of the cost. To be precise, the company claimed it only cost them $6 million dollars and 2,046 GPUs to train. For comparison, the cost of GPT-4 was estimated to be between $50 - $100 million.

GPUs are one of the biggest expenses required to train models. Additionally, since everyone is racing to be part of the AI revolution, GPUs are hard to find, as vendors like NVIDIA are having a hard time keeping up with demand.

On October 7, 2022, export controls on the sale of GPUs to China were put into effect. That meant that DeepSeek needed to find a way to train a model without leaning on hardware accelerators. And they figured it out with some ingenious computer science.

Technical Achievements Unlocked

DeepSeek also caused a stir because of technical achievements that allowed the team to train their model with fewer GPUs. According to The Register, DeepSeek R1 was fine-tuned from their V3 model that was released at the end of last year.

The team mixed up how the model was trained.

• Chain of Thought (CoT) prompting: This requires the model to show the steps it goes through to answer a prompt. Showing this “reasoning” makes the model’s responses more reliable, more transparent, and easier to debug.  

• Reinforcement learning. This is a machine learning technique. As the software makes decisions, it’s rewarded for taking actions that work toward your goal. Actions that aren’t useful are ignored.  

• Model Distillation: This is another machine learning technique. It transfers knowledge from a bigger system (the teacher model) to a smaller model (the student model). This gives the student model all of the lessons learned without having to make the training investment made with the larger teacher model.  

Mind the Hype

When you’re evaluating AI models, you have to mind the hype. The most obvious place to start is the $6 million cost DeepSeek claims it took to train this model. That is a 94% decrease!

The independent research and analysis firm Semianalysis looked at the probable total cost to train this model. They don’t believe the $6M price tag, saying: “This is akin to pointing to a specific part of a bill of materials for a product and attributing it as the entire cost.”

They believe that $6M was only for the pre-training number, and that the hardware spend has surpassed $500M over the company history. Additionally, there is the cost of the teams who spent months developing and testing the new ideas and configurations for the new model to consider.

Dangers of this Model

There are a few things to be wary of if you plan to use this model. First of all, since the model was trained with distillation, that means it was trained with synthetic data. Is that data correct? Probably as correct as any LLM can be.

Also remember that DeekSeek is a Chinese company. This means it is subject to its country’s laws and regulations. Because of that, it can’t answer questions about the Tiananmen Square massacre or the Hong Kong pro-democracy protests, for instance.

While DeepSeek, Meta, and OpenAI say they collect data from account information, activities on the platform, and devices they are using, DeepSeek “also collects keystroke patterns or rhythms, which can be as uniquely identifying as a fingerprint or facial recognition and used a biometric.”

Things to Ponder…

The DeepSeek announcement was something to get excited about. A company has figured out how to train their model faster and more efficiently, making it more affordable. However, don’t get caught up in the hype. Dig into statements that seem improbable – they probably are focusing on just one part of the story. If you don’t understand the vocabulary, look up the words or ask an expert. Here is an overview of how LLMs work from a session I presented at VMworld.

Always remember that AI is just computer science, and not magic!

Jim Blakley, of Carnegie Mellon University’s Living Edge Lab, shares insights on edge computing, AI-driven drones, private 5G, industry partnerships, and real-time innovation.

Day two of the Oregon AI Conference brought together a wide range of attendees focused on the role of artificial intelligence (AI) in society. Discussions centered on the ethical implications of AI, how small-to-medium-sized businesses (SMBs) can integrate AI into their operations, and the challenges of automation. An interactive Q&A panel—featuring Mackenzie Bristow (Senior UX Designer at Home Depot), Nick Parish (Content Strategist at Work & Co), and Sebastian Chedal (CEO of Digital Agency Fountain City)—set the tone by engaging the audience in real-world questions. This open format sparked a dynamic conversation centering around the balance of AI’s efficiency with the need for human oversight and accountability.

A recurring question emerged throughout the day: How can AI systems remain both reliable and ethical as they become more deeply integrated across industries? Below are the top takeaways gleaned from the sessions and discussions, illustrating the delicate balance between innovation and responsibility.

1. Beware of Bias
   ‍Financial services offer a prime example of how AI can generate both efficiencies and risks. Julia Carlson, CEO of Financial Freedom, showcased how tools like Quicken and QuickBooks automate financial planning. However, she emphasized that AI-driven platforms can exhibit biases—for instance, overly favoring investments in AI-related companies. This highlighted the importance of human review and transparency in any AI-assisted system, especially when user data might be feeding a broader, often public, knowledge pool. Ultimately, while financial AI can streamline processes, organizations must maintain oversight to ensure recommendations truly serve client interests.‍
2. Automate Tasks, Not Roles
   ‍Pedro Luraschi, Co-Founder of Hal9, explored how chatbots and AI agents can handle routine tasks—like qualifying leads—without removing the vital human element from the process. By leveraging accessible no-code tools, SMBs can quickly implement AI solutions that reduce manual workloads in an environment of labor shortages, though Luraschi cautioned that automating entire roles carries the risk of sacrificing human judgment. Instead, AI should be introduced as an augmentation strategy, allowing teams to focus on higher-level analysis while leaving repetitive tasks to intelligent systems.‍
3. AI and Creativity: Human Intuition Still Matters
   ‍A discussion on whether AI could create a sewing pattern from a photograph reinforced the fundamental differences between AI’s mathematical models and genuine human creativity. Nick Parish contrasted AI’s capacity to imitate human thought patterns through statistical models with the innate intuition that humans bring to creative processes. Similarly, Sebastian Chedal addressed the complexities of AI-generated music and its implications for intellectual property. While AI can recombine existing works, legal and creative gray areas persist, reaffirming that true innovation often relies on the distinctly human ability to synthesize new ideas. The day’s discussions consistently reminded attendees of how human oversight is essential not only for ethical governance but also for sustaining the spark of originality that AI cannot replicate on its own.‍
4. Spatial-Temporal AI: Predictive Insights with the Right Data
   ‍Lindsay Richman, Founder of Innerverse AI, highlighted the power of spatial-temporal AI in predictive modeling. By combining geolocation data with time-based inputs, these models can identify trends that static data sources might miss—ranging from mapping customer behavior to analyzing disease spread. Real-world applications include enhanced navigation, personalized recommendations, and detailed business forecasting. Richman emphasized that the accuracy of these models hinges on high-quality, comprehensive data. For SMBs and personal users, the ability to harness rapid insights from multiple data streams can be transformative, but only if the foundational inputs are well-curated and reliable.‍
5. High-Speed Robotics with Human Safety in Mind
   ‍In a lightning-round presentation, Robert Toppel, CEO and Co-Founder of Electron Robotics, demonstrated a six-axis system designed to pick food at high speeds to address global labor shortages. Toppel drew a memorable comparison between AI-driven robotics and a diligent sheepdog: self-sufficient in many respects, but still dependent on human guidance to steer it in the right direction. Despite its impressive capabilities, Toppel stressed that automated efficiency should never undermine human oversight and safety measures.

Balancing Innovation and Responsibility

By the end of day two, it was clear that while AI offers remarkable possibilities for efficiency and innovation, human accountability remains essential for ensuring that AI systems stay ethical, reliable, and beneficial. From financial services that need oversight to prevent biased investment strategies, to creative applications that rely on human intuition, the conference showcased both the promise and the complexity of AI. Attendees left with a renewed sense of optimism about AI’s potential in driving innovation for SMBs, but they were also reminded of the ethical frameworks and vigilant human supervision required to steer AI in a socially responsible direction. As AI continues to evolve, maintaining this balance of innovation and responsibility will remain a pivotal challenge—and opportunity—for organizations across all sectors.

Join Ace Stryker and Scott Shadley of Solidigm as they explore the vital role of storage in AI performance. From tackling storage bottlenecks to shifting from HDDs to SSDs, they discuss power efficiency in inference, the move to distributed compute, and TCO benefits. Learn how Solidigm’s SSD innovations support evolving AI workloads and the future of AI infrastructure.

Organized and launched in an impressive 40 days, the inaugural Oregon AI Conference took place on February 1 and 2 in the coastal city of Newport—showcasing both the speed at which modern technology can bring people together and the depth of possibility AI holds for small and medium-sized businesses (SMBs). Hosted over a weekend, the event also featured an on-site mobile application, built in just four hours using Glide, underscoring how agile development and AI-driven innovation are fast becoming cornerstones of today’s tech ecosystem.

TechArena was among the enthusiastic participants drawn to Newport to witness firsthand how AI, once seen as a specialized tool reserved for large corporations, is now accessible and highly relevant to SMBs across sectors. From real-time demos of advanced language models to candid discussions of data privacy risks, attendees explored numerous facets of AI’s present and future. The conference highlighted practical examples—from automating repetitive tasks to democratizing analytics.

Several notable speakers, including professors, startup founders, and enterprise managers, offered compelling insights. Dr. Donna Z. Davis, a professor at the University of Oregon’s School of Journalism and Communication, and Andrew Hallberg, Co-founder of HirelyAI and Lead Program Manager for AI at Nike, underscored both the promise of AI and the caution required for its implementation. By the end of the first day, three themes had clearly emerged: a hopeful environment for SMBs, the rapid evolution of AI tools (with particular attention to DeepSeek), and AI’s capacity to foster collaboration and trust.

1. A Hopeful Environment for SMB Leaders
   “AI is not replacing creativity and strategy—it’s scaling them.”
   – Andrew Hallberg
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   ‍A recurring message throughout the conference was that individuals and small- to medium-sized businesses are poised to benefit enormously from AI. Several speakers—including Andrew Hallberg, who leads AI initiatives at Nike and is a co-founder of AI staffing firm HirelyAI—emphasized that the technology’s greatest strength is in augmentation rather than replacement. Hallberg and others highlighted how AI tools can automate repetitive tasks, free up employees for more strategic work, and amplify human creativity in surprising ways.
   Dr. Donna Z. Davis, professor at the University of Oregon’s School of Journalism and Communication, shared how she uses AI to summarize her lectures and encourages students to do the same—provided they remain vigilant about verifying accuracy. Drawing parallels with Wikipedia as a reference source, Dr. Davis reminded attendees that human oversight and critical thinking still matter in every AI-powered workflow. She drove the point home by asking ChatGPT to produce a biography of herself—it hallucinated that, although she was standing in front of the crowd speaking, she had died in 2017.
   From streamlining report generation to building efficiencies in structuring data, many sessions stressed that real-world AI applications need not be limited to tech giants. Dr. Davis summarized it well by saying that “there’s an AI tool for just about anything you’re trying to do.”
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2. The Rapid Evolution of AI Tools, with a Focus on DeepSeek
   “There’s the good, the bad, and the terrifying” – Donna Z. Davis
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   If there was a single AI product on everyone’s minds, it was DeepSeek. Remarkably, although their R1 chatbot launched just a few weeks prior to the conference, countless references—both praising its capabilities and highlighting potential hidden costs—filled the sessions.
   In a discussion of security implications of AI, Janet Lee Johnson of the AI Governance Group pointed out that DeepSeek’s terms of service include provisions for an advertising platform that could potentially leverage user data for marketing. This point drove home a larger theme among speakers that users should be wary of supplying private data into publicly available models.
   Meanwhile, Andrew Hallberg spoke to the ways in which HirelyAI is already using DeepSeek within their internal teams. For a product that was released so recently, its rapid adoption showcased how fast the AI market is evolving, and how critical it is for businesses to stay informed.
   Yet the very fact that DeepSeek’s name surfaced in nearly every talk highlighted the AI industry’s heightened pace of innovation; building a long-term, actionable framework for SMB leaders to think about AI was a hallmark of the event.
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3. AI as an Enabler of Collaboration and Trust
   "With textual interfaces at the center of generative AI, writers become even more important creative collaborators." – Nick Parish, Director of Writing Content and Strategy, Work & Co.
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   ‍Nick Parish shared a personal anecdote about building a machine learning model using TensorFlow based on his own writing, long before ChatGPT became mainstream. The model replicated his voice, generating endings to incomplete pieces, foreshadowing the AI-driven creative tools used today. He used this example to stress that writers and creators are more vital than ever. His closing message urged attendees to stay mindful of their AI usage, reminding them, "Your influence can determine the path any technology takes."
   Throughout discussions and panels, speakers shared the common prediction that AI tools will facilitate new modalities of connection between businesses, and help to build trust between teams and their partners.  

As day one of the Oregon AI Conference came to a close, the 40-day journey of organizing the event itself stood as a testament to how quickly AI initiatives can take off—and how accessible these tools now are to smaller businesses.

In our day-two summary, we discuss how conference leaders guided participants through practical implementations, from data-privacy best practices to shaping internal AI policies. These workshops reaffirmed the day-one point that embracing AI is no longer just for Silicon Valley heavyweights—SMBs can leverage these rapidly evolving tools to stay competitive, spur innovation, and cultivate an environment where technology and human creativity flourish side by side.

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Our own Allyson Klein moderates a powerhouse panel on AI ethics, with panelists representing Loyola University Chicago, Google, MLCommons, VAST Data and Momethesis.

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In this video from Chiplet Summit, Shekhar Kapoor discusses how Synopsys’ transition to a multi-die approach to chiplet development has allowed them to innovate beyond the limitations of traditional monolithic chips.

The In the Arena podcast has a commitment to look to future innovation, and this is why I was so excited to have Winbond’s Jun Kawaguchi in the studio for an insightful conversation. Winbond has long been known as a memory player based in Taiwan, but their development of Post-Quantum Cryptography Flash has ushered in a new chapter of differentiated innovation for the firm. As we stand on the brink of the quantum computing era, the potential vulnerabilities quantum introduces to our current cryptographic systems cannot be overstated. Jun provided invaluable insights into how Winbond is pioneering strategies to safeguard our digital future.

The Quantum Threat Explained

Quantum computing promises to revolutionize industries and re-frame compute capability with its unparalleled processing potential. However, this advancement comes with a caveat: the same power that enables quantum computers to solve complex problems also poses a significant threat to the methodologies used to secure existing compute platforms. Algorithms that currently protect our data could become obsolete, leaving sensitive information exposed.

Jun emphasized that while quantum computers capable of breaking today's encryption aren't yet mainstream, the urgency to develop quantum-resistant solutions is paramount. The industry’s window to prepare is narrowing, and proactive measures are essential to stay ahead of potential threats. Winbond has recognized the gravity of this impending challenge and is at the forefront of developing quantum-resistant security solutions. Jun detailed their multi-faceted strategy, which includes:

1. Research and Development: Investing heavily in R&D to explore and implement cryptographic algorithms that are resistant to quantum attacks. This involves collaborating with global experts and institutions to stay abreast of the latest advancements in quantum-safe cryptography.
2. Hardware-Based Security: Leveraging Winbond’s expertise in semiconductor advancement, the company is integrating robust security features directly into their storage portfolio. This approach ensures that devices are equipped to handle quantum-resistant algorithms efficiently, based on existing required components rather than esoteric solutions.
3. Industry Collaborations: Understanding that cybersecurity is a collective responsibility, Winbond actively participates in industry consortia and standards bodies. By contributing to the development of global standards for quantum-resistant cryptography, the firm aims to foster a unified defense against emerging threats.

The Importance of Early Adoption

Early adoption of quantum-ready security is paramount to the success of keeping data secure. Jun pointed out that data intercepted today could be stored and decrypted in the future once quantum computers become powerful enough—a concept known as "harvest now, decrypt later." This makes it imperative for organizations to implement quantum-resistant measures with urgency. Transitioning to quantum-resistant cryptography is not without challenges. In order to implement now, organizations will need to navigate the following hurdles:

• Performance Overheads: Quantum-resistant security algorithms can tap more compute resources, potentially impacting system performance. Balancing security and efficiency is a delicate task that requires innovative engineering solutions and system level architecture to build in performance headroom for required security cycles.
• Compatibility Issues: Ensuring that new cryptographic methods are compatible with existing systems is crucial to facilitate a smooth transition. This involves extensive testing and validation to prevent disruptions.
• Awareness and Education: Raising awareness about quantum threats and educating both internal and external stakeholders is essential. Many organizations may not yet fully grasp the urgency of the situation, and spreading knowledge is a vital step toward widespread adoption of quantum-resistant technologies.

The TechArena Take

While quantum is still a few years ahead, Jun raised a very important concern about current data threats from future quantum capability. The time is now to secure data stores across environments to prepare for upcoming technology advancement. We were intrigued to see Winbond take this bold step in advancing security capability utilizing flash and look forward to learn more about the upcoming innovations promised in the interview. We think it’s the right time to engage with Winbond to learn more about their roadmap and build a strategy for organizational readiness for the coming quantum revolution. Listen to the full conversation here.

AI advancement is a global pursuit, and Canada has accelerated its core capabilities with a new collaboration with industry leaders Hypertec and VAST Data.

While we have written about the AI's geo-political underpinnings and posturing of late, it's terrific to see a focus on real-world advancement of technology to serve pragmatic application innovation. In the agreement, Hypertec Cloud will boost compute capacity for AI through their proven cloud offerings, enabling researchers and government agencies access to compute cycles to fuel their application development.  

In order to deliver the data pipeline required, Hypertec will utilize the VAST Data Platform, known for its efficiency and scale and delivering seamless management and access across diverse environments.

Part of this agreement will include delivery of up to 100,000 GPUs across North America early this year, growing the companies’ collaboration that already supports mainstream deployments from Together AI, Zyphra and other leading AI adopters. The collaboration taps VAST's large-scale AI cloud infrastructure to support customers who choose to deliver solutions based on diverse data locations and combination of structured and unstructured data sets to train AI models uniquely suited for their applications.  

We were delighted to catch up with Hypertec Cloud at Supercomputing (SC ’24) to learn more about this long-standing collaboration between the two companies. Check out additional insights here, and be sure to check out TechArena's content contributions to VAST's Cosmos community.

Untether AI's Bob Beachler explores the future of AI inference, from energy-efficient silicon to edge computing challenges, MLPerf benchmarks, and the evolving enterprise AI landscape.

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In this video, learn why Arm is investing in chiplets as a way to build dynamic options for compute in the AI era, and hear more about their new Chiplet Standard Architecture Specification.  

To learn more about the CSA, check out Arm’s blog.

The evolution of software has experienced a few consistent patterns over the years, with a few disruptors here and there that drive a new methodology, or new economics. One consistent trend has been that the introduction of a useful technology, such as a virtual machine monitor, often generates significant enthusiasm. However, it frequently ends up relegated to middleware, which either requires a solution higher up the stack (like Broadcom/VmWare VSphere) to maintain market momentum and customer retention, or gets absorbed into the lower layers of the platform or hardware. The base technology is useful, but doesn’t have a direct touch to the customers/users, and ends up bundled with other innovations that do.

With that background, everyone has been watching the battle for domination of worldwide AI capital investments amid claims that “agentic AI” may hollow out the inherent value of SaaS services. Others continue the never-ending hype surrounding new model development to justify ‘build it and they will come’ data center investments. This news cycle, there has been a lot of traffic on Deepseek, small language models, and OpenAI’s SUPER agents against their “Benchmarkgate.” That is a lot of jargon for Wall Street begging AI providers “take my money, please,” and the largest model competition between a handful of players who want an unfair share of that capital/investment. It seems to be independent of the revenue base of paying customers who are still working to find the AI 10x ROI to their businesses. There are other economic challenges – if OpenAI is losing money on every query after seemingly randomly selecting $200/month as their Pro business model, how long can their business model hold before prices must change? Does an enterprise need a model that can answer world history questions, or quotes from Shakespere, or just a trained-on-that company-data model that can accelerate time to value for their specific enterprise processes? If a business does not need a model that can answer world history questions, does it need “superintelligence”?  

There is a clear and practical application for the automation and streamlining that AI, as part of a business process, can bring. The challenge for the underlying model itself is that another process has to invoke it, whether a browser/app (ChatGPT), or the front end of a business process/query (ex:ETL). For consumer facing businesses, LLMs function as middleware that drives revenue growth in advertising and shopping businesses. The helpful system recommending things based on my browsing or shopping history? AI functions and models running in the background. This business isn’t new and radical – different types of applied AI have driven shopping operations for years. In other enterprises, for significant value for AI to be realized, the debate on agentic/ SaaS and the importance of a superintelligence model comes down to what interacts with the AI function – customers or front-end processes. Agentic AI in particular breaks up workflows in ways that abstract which model is running a function or process. The model or more likely, models, underpinning the agentic AI workflows work in the background.

An analyst told me recently that “models don’t matter”, which may be an exaggeration since data bases aren’t sexy anymore, but are critical to getting great results from AI deployments.

One test on whether becoming middleware is a likelihood is how close the funding pitch sounds like the Southpark Underwear Gnome business plan:

Their business plan is broken into three simple steps:

• step 1:  Steal underpants
• step 2: ???
• step 3: Billions and billions of profit.
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While I see signs that models themselves are becoming background functions using the Southpark test, the SaaS/Agentic debate where both options offer user facing features and AI capabilities “in the workflow” is the real battlefield for companies wanting to capitalize on the AI transformation. My theory on which model wins comes down to “users likely do not care how the underlying architecture was constructed”, so the advantage goes to ease of use.

What are your thoughts about where SaaS and Agentic have advantages or disadvantages?

And just like that, the world of artificial intelligence (AI) model training was thrown into turmoil. In case you were vacationing or otherwise occupied late last week, China introduced DeepSeek, a free-to-the-public generative AI model that is outperforming Chat GPT.

While the performance is stunning, what's most notable about DeepSeek is how this elegant solution was purportedly trained with a fraction of the resources required by leading models today. And this news emerged literally days after OpenAI announced a $500 billion investment with Microsoft, Arm, NVIDIA and others to drive U.S. AI superiority and the U.S. delivered the first meaningful threat to China’s TikTok access to U.S. markets.

Chatter across social networks dubbed the makers of DeepSeek geniuses, heralding an end to U.S. AI leadership and calling NVIDIA stock deeply overvalued. Let’s unpack what we know and what we suspect as the dust settles on this massive shock to the AI world.

DeepSeek appears to have taken some innovative approaches to training its algorithm, including utilizing less precise math (eight decimal places vs 32) and processing larger groups of words, driving down precision. But it’s also created more efficient multi-token ingest and allocated training across multiple experts like a group of smaller, smart models working in tandem. All of these examples are innovations that will likely get attention from across the AI community and come at a time when the field was seeking disruptive approaches to delivering AI models more efficiently, something we’ve discussed at length on TechArena in 2024.

What is raising questions among many experts whom I chatted with over the weekend is the full transparency in the cost of training the model. One pegged the true cost at 1.5-to-2 orders of magnitude higher than what the startup has stated, with disclosed costs solely focused on knowledge distillation and fine-tuning of the algorithm. They point to the fact that this version of the model has had the benefit of training of previous iterations, similar to the investment alternatives like ChatGPT, Llama, Gemini and others have based on iteration of versions of models over time. The truth is likely that the cost of the model we’re looking at today is much higher than the $5 million that promoters are claiming. Yet, this should not discount the value of a competitive model and the overall performance it’s delivering.

Of course, the timing of DeepSeek’s release does give credence to this lack of transparency being a shot across the bow of the Stargate announcement, continued tensions on TikTok restriction from the U.S. market and U.S.’s focus on AI as a central policy imperative. And the well-timed emergence of DeepSeek to access U.S. data sources can’t be overlooked, given that China has historically used TikTok to collect data from Americans and this new generative AI model provides a much more powerful way to collect that data and deliver potential misinformation through content.

While the coming weeks will provide more clarity on the full truth of this model’s efficiency, one thing is clear: DeepSeek has absolutely captured the public’s attention, seizing leadership on AppStore downloads vs Chat GPT.

So, what’s the TechArena take?

We see the arrival of DeepSeek as a reminder that while the U.S. hyperscalers grab the headlines for AI model advancement, their Chinese counterparts have been dedicated to developing their own AI solutions for years. Bytedance alone has committed $20 billion in AI investment in 2025 ($12 billion earmarked for U.S. spending), and others such as Alibaba, Baidu, and Tencent have similar large-scale operations established. In this high-stakes realm of the race for LLM superiority, we can expect to see both dizzying announcements of innovation and overstated differentiation at both a corporate and geo-political scale. And while we applaud any advancement to drive efficiency into AI training and are seeking further clarity of the full veracity of what’s been delivered with DeepSeek, we aren’t yet ready to call an end to the Blackwell era before it’s really even begun.

We also see the public’s zeal for utilization of all of these LLM models igniting without a lot of thought about what happens to the data provided to the model owner, whether that be an enormous tech conglomerate in the U.S. or a Chinese startup with ties to the government. In this world of rapid adoption of LLMs, we wonder what defines truth in the future and will there be multiple definitions of truth depending on who controls the algorithm.

Share your thoughts with us on LinkedIn and expect more news to roll out quickly in this space in the coming days.

Innovators from around the globe gathered in Santa Clara this week for the third annual Chiplet Summit to learn the latest in artifical intelligence (AI)/ machine learning (ML) acceleration, the open chiplet economy, advanced packaging methods, die-to-die interfaces, and more. The TechArena team had courtside seats for the event, and with chiplets being the underpinning technology for continued advancements of Moore’s Law, we were keen to understand the state of industry innovation.

We have been talking about an open chiplet economy on the TechArena platform since we named UCIe one of the top innovations in tech in 2022. The speed of design, efficiency of multi-process solution delivery, and opportunity for best-in-breed chiplet integration offers unquestionable opportunity for the market. But while all major compute architectures embrace chiplet designs today, the open economy of chiplet design has been slow to emerge due to missing elements, such as form factor designs, and interoperability testing still needing to be addressed by the industry. Technologists attending the event this week enjoyed opportunities to learn from industry pacesetters about the current state of addressing these gaps as discussions centered on how to create the latest chiplet designs in less time, for less cost, and in a more scalable way.

The event featured a series of keynotes from semiconductor heavyweights, including  Alphawave Semi, Arm, the Open Compute Project Foundation, Synopsys, and Teradyne. While these companies provided incredible insight to the state of the industry, there were a few takeaways that rose above the rest:

1. ‍The industry is embracing chiplet technologies as the foundation for future AI scaling – In a keynote delivered by Tony Chan Carusone, chief technology officer at Alphawave Semi and professor of electrical and computer engineering at University of Toronto, Tony unveiled Alphawave’s vision for the future of AI scaling, connectivity, and custom silicon solutions. He discussed how groundbreaking advancements in chiplet technology are enabling the rapid evolution of AI systems while addressing critical challenges of sustainability, cost, and performance. This included three pivotal challenges driving innovation in AI: the insatiable demand for compute, the diversity of workloads requiring customized hardware, and the increasing complexity and cost of traditional monolithic chip designs. He emphasized how chiplet-based architectures are uniquely positioned to address these issues. By leveraging modular design principles and advanced packaging technologies, chiplets enable faster development cycles, greater scalability, and optimized performance tailored to AI workloads.
   Alphawave Semi showcased its AlphaChip 1600 IO, a multi-protocol chiplet that supports diverse connectivity requirements, including Ethernet and optical solutions, vital for enabling next-generation AI infrastructures.
2. ‍Collaboration is becoming increasingly critical across the semiconductor supply chain – Eddie Ramirez, vice president of marketing at Arm, delivered an insightful keynote outlining the pivotal role of chiplet technology and ecosystem collaboration in addressing the challenges of AI scaling and democratizing computing. With expertise spanning data centers, networking, and edge solutions, Arm is enabling partners to accelerate innovation while optimizing cost and performance, he said. We first covered Arm’s ecosystem programs in this arena in 2023 at the Open Compute Foundation Summit, and it was terrific to see the groundswell of support Arm has developed with partners reflecting a broader trend in chiplet advancement.
   In his talk, Eddie noted that 80% of AI workloads are centering on inference, a shift from a more training-centric focus in 2024, requiring specialized silicon and heterogeneous integration to meet the needs of both data center and edge environments. Chiplet-based architectures provide a solution to the rising costs and complexity of traditional monolithic designs, enabling reusable silicon and faster development cycles. Eddie highlighted examples of Arm’s ecosystem collaborations, including NVIDIA’s Grace Hopper system and Amazon’s Graviton processors. Grace Hopper showcased true heterogeneous integration, combining Arm Neoverse CPUs with NVIDIA’s high-performance GPUs to enhance AI pipeline efficiency. Meanwhile, Amazon’s accelerated delivery of four Graviton processor generations in five years highlighted how chiplets can streamline development and boost performance.
   Eddie also discussed Arm’s NeoVerse CSS compute subsystems as a catalyst for advancing custom AI silicon. By pre-integrating IP and reducing design complexity, the company is empowering partners to bring AI-enabled CPUs to market faster, he said. He pointed to the Arm Total Design initiative and open-source chiplet system architecture (CSA) specification as steps toward standardizing chiplet designs and fostering a more accessible computing ecosystem.‍
3. Multi-Die architectures hold increasing significance as critical to the future of AI-driven semiconductor growth – Abhijeet Chakraborty, vice president of engineering at Synopsys, highlighted the transformative role of multi-die and chiplet technologies in driving semiconductor industry growth. With global semiconductor sales projected to approach $700 billion in 2025, Abhijeet[AK1]  emphasized how advanced chip designs are addressing the surging demand for AI and high-performance computing (HPC) applications. Multi-die architectures are growing in significance as essential for overcoming the limitations of monolithic designs in AI chip development, he said. AI accelerators, tailored for training and inference workloads, require immense compute power, high memory bandwidth, and advanced interconnects. These demands have led to innovations like Synopsys' UCIe-compliant interface IPs, enabling high-speed die-to-die connections and ensuring reliability through monitor, test, and repair capabilities.
   Packaging technology serves as a critical chiplet enabler, with advancements like hybrid bonding facilitating higher interconnect densities and cost-efficient stack-die solutions, he said. Abhijeet also underscored the role of AI in electronic design automation (EDA), leveraging AI for optimization, prototyping, and multi-physics analysis to tackle the complexity of multi-die systems. Looking ahead, Synopsys anticipates rapid growth in multi-die adoption, with over 50% of HPC designs expected to utilize the technology in 2025.

UCIe – Enabling Widespread Chiplet Adoption Across Industries
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As the industry races to keep up with the pace of change, the UCIe (Universal Chiplet Interconnect Express) Consortium has gained momentum tackling chiplet integration challenges. During UCIe’s presentation at the summit, Brian Rea, marketing workgroup chair for the consortium, emphasized that the group is driving the future of chiplet integration by establishing open industry standards and fostering collaborative innovation. Milestones include the Pike Creek project, demonstrating multi-vendor interoperability, and UCIe 2.0, released in 2024, featuring enhancements for 3D integration, interconnect performance, and advanced packaging. These advancements address critical industry needs, such as scalability and seamless integration.

TechArena’s Take
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So what is TechArena’s take on the state of chiplets? Like many industry-wide innovations, growing the foundational standards, IP, and tools are the first steps to massive adoption of technology. What we saw this week at the Chiplet Summit underscores industry advancement on all fronts, and examples of market traction felt more substantial as compared to last year’s keynotes. We expect to see broader application of multi-vendor chiplet solutions for custom chip delivery to fuel the insatiable compute demands of AI in 2025, bringing us closer to an open chiplet economy.

We can’t wait to hear more about standards development to support various aspects of multi-vendor delivery, such as common form factors. We believe the industry is aligned on these challenges being key gaps for urgent collaboration this year. Finally, we expect that 2025 editorial will feature a lot of this and more, as large providers continue to leverage this foundational ecosystem to drive the hundreds of billions in data center buildout expected globally.

In this In the Arena episode, Winbond’s Jun Kawaguchi discusses their industry-leading strategies for tackling next-gen cybersecurity threats, ensuring robust protection for the future.

In a previous article, I used the story of Alex–a busy salesperson targeted by a phishing scam–to illustrate the sophisticated nature of today’s phishing attacks, as cybercriminals work to stay one step ahead of end users. I also shared some general strategies to avoid falling victim to phishing.

While these methods are broadly applicable, understanding phishing’s evolving techniques and deceptive tactics is crucial—because spotting these scams is often harder than it seems. So, what exactly is phishing?

Understanding Phishing
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Phishing is, fundamentally, an identity cyberattack that uses communication (emails, texts, calls) to trick targets into providing access to sensitive data or installing malware. The target takes an action based on mistaken identity and misplaced trust in that identity. Because the target is unwittingly providing access, the malicious actor does not need to use technical means to gain access to the computer system.

The most basic phishing attack occurs when the malicious cyber actor sends a link to the target, who erroneously trusts the link and enters their credentials.  The malicious cyber actor then uses the credentials to commit fraud. A variation of phishing, known as smishing, uses SMS text rather than email to transmit the message.

Spear-phishing is a more sophisticated form of phishing. Unlike generic phishing emails sent to a large group, malicious actors specifically target a particular individual in a spear-phishing attack. By using this personalized information, such as Alex’s friend Jade’s phone number, spear phishing attacks appear credible, and are more likely to be acted upon by the intended targets. In the future, AI-powered synthetic data, such as deepfakes, will become increasingly prevalent in spear-phishing attacks, making it harder to trust even the most trustworthy voices and faces.

Phishing as an Attack Vector
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The big secret of cybersecurity is that it is not especially challenging to get sensitive personal or organizational information, but it is difficult to monetize that access. Phishing offers a straightforward solution. Phishing thrives, because it offers a comparatively low-effort, high-reward pathway to monetization. Cybercriminals can use stolen personal information, such as Social Security numbers, credit card details, and bank account information, to commit identity theft and financial fraud. They can make fraudulent purchases, take over accounts, open new accounts, or even take out loans in the target’s name.

Additionally, creating a reverse proxy can serve as a command and control (C2) channel, enabling the malicious cyber actor to remotely control the compromised system to install malware, pivot to other systems on the network, and launch further attacks.

Phishing attacks can go beyond simple credential theft. They can also function as a delivery mechanism for more sophisticated malware, blurring the line between phishing and other cyberattacks. These types of advanced attacks should be classified as something other than phishing. For example, surveillance tools, such as those from NSO Group, Candiru, Sourgum, and Intellexa, are four of the commercial tool makers who have delivered cyber tool payloads via phishing. Security researchers have linked these companies to sophisticated phishing or smishing cyber tools that use zero-day or zero-click exploit payloads. They can combine the social engineering tactics of smishing with well-executed sophisticated technical exploits to achieve their goals.

Phishing's popularity as a cyberattack technique stems from its ease, flexibility, and low risk for perpetrators. The low barrier to entry is a significant factor; unlike complex malware development or network intrusions, phishing attacks need minimal technical expertise and resources, making them accessible to a broad spectrum of cyber actors. Phishing’s remarkable adaptability amplifies this accessibility. Attacks can be meticulously tailored (spear phishing) to target specific individuals with personalized lures, or scaled up to target entire organizations (whaling) by impersonating high-ranking executives. Even more broadly, business email compromise (BEC) attacks the target’s supply chains. Phishing attackers exploit our human nature. They meticulously study how we think and make decisions, leveraging our cognitive biases to craft phishing lures that trick us into making irrational choices.

The diffuse nature of phishing attacks, often targeting individuals, makes it difficult to trace and prosecute, creating a low-risk environment for cybercriminals. These attacks often go unnoticed, or are difficult to trace back to the perpetrators across international borders, further reducing the perceived risk for malicious cyber actors, and solidifying phishing's position as a highly favored attack vector.

In 2023 alone, phishing attacks accounted for over $2.9 billion in losses, according to the FBI’s Internet Crime Complaint Center (IC3) (2023 Internet Crime Report). When combined with data breaches, credential theft, and the operational expenses tied to MFA and password-based systems, the aggregate cost of phishing losses is significant.

Phishing attacks often have a direct and immediate financial motive. Cybercriminals that use phishing seek to steal sensitive financial data, such as usernames, passwords, and credit card information. This stolen data grants them direct access to victims' accounts, allowing them to withdraw funds or make fraudulent purchases. This direct access eliminates the need for complex and costly money laundering schemes, which aim to disguise the origin of illicit funds. The malicious cyber actor can make fraudulent purchases, transfer funds, or withdraw cash using the stolen credentials. There is no need to obscure the origin of the funds or move them through accounts to hide their tracks. The costs associated with money laundering can significantly reduce the profitability of cybercrime, making direct access to accounts provided by phishing more lucrative. Phishing provides a straightforward pathway to immediate financial benefits, making it a highly efficient and appealing method for cybercriminals seeking rapid low-risk returns.

The increasing use of multi-factor authentication (MFA) and “advanced email security” systems can make it more challenging for malicious cyber actors to use stolen credentials. Phishing attacks that rely on stolen passwords are rendered ineffective with MFA, but MFA implementations come with their own associated costs, including financial, operational, and potential hidden expenses. This is an active area of development, with innovative approaches emerging.

Advanced email security systems, leveraging AI, behavioral analytics, and sandboxing, can identify and neutralize most phishing attempts. Additionally, the adoption of zero-trust security models can detect and block such attacks. Users can overlook this sophistication, because it operates in the background without the user’s knowledge. Companies such as Microsoft, Darktrace, and Proofpoint are at the forefront of these innovations, indicating a broader industry trend toward more secure and resilient systems that challenge the dominance of phishing as the most popular cyberattack technique.

While sophisticated email security solutions have made significant strides in blocking phishing attempts, they are not perfect. Implementing and maintaining advanced email security solutions can be expensive and complex.

See the previous article in this series to learn 10 things you can do to protect yourself from this cyberattack.

Phishing attacks are increasing in sophistication, and the signs of phishing that many people learned early in the lifespan of this criminal activity have changed.

Today, phishing is the most popular cyberattack technique because it offers a straightforward way for cybercriminals to monetize unauthorized access into quick cash.

Here’s an example scenario:

Alex, a busy salesperson with thousands of contacts, received a text from her friend Jade, an Apple developer rep, offering a 20% discount on AirPods Pro, Alex's favorite. The text looked genuine, even coming from Jade's phone number, and her iPhone identified and labeled the text as coming from Jade (caller ID spoofing).

Eager for the deal, Alex clicked the link in the text. (Click!)

The link led to a website that looked identical to the brand's official website. It had the same layout, color scheme, and product images. The website even had a limited-time banner ad for partners – the same ad Alex had seen on Apple’s site recently.

The site displayed a small padlock icon in the address bar; the URL started with https, indicating that the connection was “secure.” Confident that the website was legitimate, Alex entered her ApplePay payment credentials, but the site rejected them. She reasoned that her corporate VPN account could be blocking the purchase, so she entered her credit card details instead.

Once Alex entered her credentials, they were immediately visible to the malicious actors. With access to Alex's account, they made increasingly large fraudulent purchases totaling $2,600, mostly in gift cards, then sold both her account credentials on the dark web, for $4 USD each (yes, that is a going rate), as well as her address and other information valuable in the criminal economy for more sophisticated identity thefts.

The ease with which cybercriminals tricked Alex underscores the effectiveness of phishing engineering tactics. Alex discovered the breach months later, before a flight, when she was unable to access her ApplePay account. She never realized the malicious actors had also compromised her credit card. The advice given to her was to change her password, use MFA, and monitor her credit report.

What Alex Missed

In the scenario above, the padlock gave Alex a false sense of security. The truth is that most phishing sites use TLS (Transport Layer Security), meaning they would have that padlock. Many users, like Alex, see the padlock and assume the site is safe. They do not understand that the padlock only means the transmission is encrypted, not that the website is trustworthy. This misunderstanding is what phishing attackers exploit.

Here’s a little background on this point: Public Key Infrastructure (PKI) provides the foundation for secure communication by managing digital identity through certificates, while TLS leverages these certificates to establish encrypted and authenticated connections between devices. While PKI has been a cornerstone of digital security for decades, it has become increasingly clear that it is not the ideal solution. PKI is often difficult for average users to utilize correctly.

10 Ways to Protect Your Accounts

The story of Alex, a target of a phishing text message, exemplifies the ease with which cybercriminals can trick unsuspecting individuals. Phishing tactics prey on human trust and exploit emotional triggers, making them particularly dangerous.

Here are ten things you can do to help protect your accounts:

• Do not click on links or open attachments in unsolicited emails or texts, regardless of their apparent legitimacy.
• Hover over links to reveal the actual URL and use link expander services to verify their authenticity.
• Scrutinize email addresses and phone numbers for any discrepancies or anomalies.
• Contact the sender directly through a separate, trusted communication channel (e.g., company phone line, internal messaging system, Signal) to confirm the message's authenticity.
• Employ unique, complex passwords for each online account. 
• Utilize an open-source self-hosted password manager to generate, store, and securely manage your passwords.
• Enable MFA for all financial accounts with hardware keys (e.g., YubiKeys).
• Recognize the limitations of training and individual actions. Leverage enterprise-level security measures such as Zero Trust Architecture (ZTA) and advanced email security solutions.
• Consult with people that can help.
• Stay informed and vigilant! By staying vigilant and taking proactive steps, you can reduce your risk of falling victim to phishing attacks.

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Artificial Intelligence (AI) is everywhere. It's at the center of conversations about technology, business, and even our daily lives. But when you see "AI" in the news, advertisements, or policies, do you really understand what’s being discussed?

Here’s why it matters. The way we define and use AI terms isn’t just about tech jargon. It influences how products are marketed, how policy is drafted, and how funding is allocated. If AI is shaping our world, we need to grasp what it means—and what it doesn’t.

This blog dives into why clarity around AI terms is essential, from its economic and political impact to how it’s reshaping technology and culture.

How AI Is Driving Economics and Politics

Every day, people search for “AI” and related terms over 6.5 million times. The AI market is booming—between 2017 and 2024, the number of AI companies more than doubled to 70,000. According to a 2024 Databricks survey, companies are projected to spend between $1 million to $10 million this year on generative AI alone.

The total addressable market (TAM) for artificial intelligence is staggering. Back in 2018, McKinsey pegged the TAM for AI at $13 trillion by 2030. By 2023, generative AI—just one subset of AI—was predicted to add $4.4 trillion annually to the economy.

AI isn’t just an economic game; it's a geopolitical one as well. Countries are racing to lead the AI revolution. For instance, the U.S. Congress has been urged to create a Manhattan Project-level initiative to reach artificial general intelligence (AGI) before nations, such as China, do. Why? Because AI leadership isn’t just about innovation; it’s about economic dominance and global influence.

When AI has this level of impact, understanding what we mean when we say “AI” becomes not just important—but essential.

AI is Changing How Computing Works

At CES this year, NVIDIA CEO Jensen Huang discussed AI's recent history and NVIDIA's significant role in it:

Artificial intelligence has changed how computing works. AI isn’t an ephemeral term, or an application, but an important evolution in computer science.

Artificial intelligence depends on lots and lots of data. Once the data is digitized and stored, you need lots of high-performance computing available to process it. That’s where NVIDIA comes in. Their products can run compute for machine learning and deep learning, the techniques used to train machines to do a task.

We Have a Cultural Bias for the Term AI

“Artificial intelligence” is a very loaded term. It was coined in 1955 by Stanford professor John McCarthy. He defined it “the science and engineering of making intelligent machines”.

Let’s be honest. AI is just a marketing term.

However, our collective consciousness is already filled with imaginations of what AI is. The oldest is HAL 9000 from 2001: A Space Odyssey.

Or maybe your mind takes you to Robocop, the story of crime-eradicating cyborgs.

If you’re a comic lover like I am, maybe you think of Ultron, an intelligent AI that was created by Dr. Henry Pym to keep global peace. But it got weird when Ultron decided there could never be peace among humans.

The list goes on and on. The Jetsons, Star Wars, Wall-E, the Matrix, Blade Runner, and The Terminator all feature some sort of futuristic AI.

These shows are part of our culture. It’s what we’re bound to think AI will be. Combine that with the gold rush to cash in on the AI craze, it’s important to know what people mean when they say “AI”.

Types of AI

If AI is a marketing term, it only makes sense that the types of AI can be separated into different types:

• Narrow AI: We are here. This includes things like voice assistants, facial recognition, recommendation algorithms, language translation, and even generative AI. 
• Artificial General Intelligence (AGI): This is the future. Sam Altman claims the latest version of OpenAI is at this level (although that’s up for debate). This type of AI will have the ability to understand, learn, and apply knowledge in a generalized manner.
  In an interesting sidenote, most of the media examples given above would classify as AGI.
• Artificial Super Intelligence (ASI): This is the future. It is theorized that this form of AI will surpass human levels of intelligence, capable of solving problems and creating solutions beyond what we can even imagine.
  Ultron is the only example above that meets the criteria for ASI. I mean, he was smart enough to hypnotize Pym to forget about him!

Instead of AI types, I like the way Huang marked out the stages of AI:

• Perception AI: Speech recognition, medical imaging, recommendation engines
• Generative AI: Digital marketing, content creation
• Agentic AI: Coding assistant, customer service, patient care
• Physical AI: Self-driving cars, general robotics

From a technology perspective, we have a handle on perception and generative AI. Those were building blocks to get us agentic AI, software that can interact with data and other tools. They can create lists of steps and then perform them with minimal human intervention (via RedHat).

The lessons learned from agentic AI are what will get us to Physical AI.

The More You Know…

Let’s practice seeing all types of AI for what it is – the newest evolution of computer science. Instead of types of AI, let’s see where things are on the evolution of AI as a science scale.

Don’t be afraid to question what you read, that is how you can defeat AI FUD. If the claims sound like they came out of a movie, dig deeper. What is meant by AI? Does that make sense based on where AI technology is today? If not, maybe you’re being sold the dreams of our childhood.

Let’s not be afraid of what the future holds, because AI definitely will open doors to amazing things. But first we’ll have to build the technologies needed to make it real.

In this report, discover the 2025 predictions of TechArena’s Voices of Innovation – industry experts who forecasting what’s on the horizon in AI, data centers, edge, network, sustainability and more.

In this Great Debate, moderator Allyson Klein is joined by a panel of industry leaders to discuss the future of AI in the data center. Jen Huffstetler, Chief Sustainability Officer at HP Inc.; Rebecca Weekly, VP of Infrastructure at GEICO; and Stephanie Schmidt, VP of Strategy at Flex, delve into enterprise adoption of AI in 2025, and the compute infrastructure needed to support its scale-out.

As artificial intelligence (AI) and high-performance computing (HPC) workloads continue to grow bigger, hotter, and more power-intensive, players like DUG – a pioneer in HPC solutions – focus on marrying sustainability with cost efficiency.

DUG, originally a geoscience data processing company, has been perfecting the use of liquid cooling technologies for HPC infrastructure for 15 years. Their latest innovations, including the Nomad—a data center in a shipping container—are designed for scalability and efficiency, offering HPC-as-a-service to clients who value sustainability, affordability, and simplicity over costly on-prem solutions.

The company’s liquid-Cooled HPC-as-a-service offers scalable, cost-effective, sustainable solutions to customers who are looking to access bare-metal HPC machines at a fraction of the cost of building on-premise solutions.

As the demand for AI and machine learning (ML) solutions continues to surge, DUG is observing a transition from traditional HPC-as-a-service to AI-as-a-service and ML-as-a-service, said Executive Vice President Ron Schop.

AI workloads also amplify the need for storage and heat management. DUG has doubled its cooling capacity through advanced Fluid Control Modules (FCMs), Schop said. And as storage demands continue to grow, partnering with storage leaders like Solidigm is critical, bringing large-capacity drives to provide the density required to meet modern AI and HPC needs.

So what’s the TechArena take? DUG brings a commitment to sustainable HPC solutions with an emphasis on creativity and innovation and more than a decade of experience with liquid cooling, presenting an interesting alternative for enterprises weighing new approaches to their data needs.

To learn more about DUG’s sustainable, liquid-cooled HPC and AI/ML solutions, visit dug.com.