IT Brand Pulse Blog

The most useful contribution of Penguin’s session was its system-level framing. CXL does not eliminate the need for fast local memory, and additional capacity has value only when software and workloads can use it effectively. Its promise lies in giving system designers another placement and capacity option.

Anil Godbole traces CXL’s progress from its first specification in 2019 to deployments by major cloud operators. He also explains why memory pooling could become one of CXL’s most consequential uses for AI.

Siamak Tavallaei — Big Data Feeds Big Compute through Memory Register to attend the presentation on September 30 For most of the AI boom, the infrastructure conversation has revolved around compute: more GPUs, larger clusters, faster accelerators and ever-larger scale-up systems. Siamak Tavallaei believes the industry is reaching the point where that conversation has to change. […]

I recently interviewed Varun Chhabra, SVP of Product Marketing for Dell Technologies’ Infrastructure Solutions Group, about the results of our Enterprise AI Infrastructure Brand Leader surveys. When I asked which AI infra categories could become more important, he talked about the data layer. With 5,000 AI deployments behind him, I believe he’s right.

Based on votes from enterprise IT pros, architects, infrastructure engineers, and AI practitioners, the independent, non-sponsored research identifies Market Leaders and Innovation Leaders across 28 enterprise AI infrastructure product categories spanning AI compute, memory, storage, networking, AI operations, observability, and Compute Express Link® (CXL) infrastructure.

While nearly every AI company today builds Large Language Models, Engramme is developing AI models whose primary purpose is organizing, weighting, and retrieving an individual’s lifetime of experiences. Each person receives their own private memory model. Not a shared global model. Not a corporate knowledge base. A personal model built from that individual’s digital life. Gabriel explained that every person’s memories are unique, making individualized memory models essential. He emphasized that these models remain private because they belong to the individual rather than being shared across users.
This is a subtle but profound shift. Language models generate information. Memory models preserve identity.

IT Brand Pulse Takeaway: Current State – EverMind is building an integrated memory ecosystem consisting of developer infrastructure, consumer memory applications, and memory-first autonomous agents. Emerging Trend – The company reflects a broader industry shift away from treating memory as a feature and toward treating it as foundational AI infrastructure.
Future Vision
EverMind’s strategy aligns closely with our vision of persistent, user-controlled memory that spans applications, models, devices, and eventually organizations.

One of the goals of the Personal and Organizational AI Memory (POAM) initiative is to understand not only where the AI memory market is headed, but where it is today. For this installment of our series of articles about companies and people in the AI Memory ecosystem, I spoke with Jonathan Covey of ZetaChain, a […]

This may be the most important takeaway from the interview. The Personal AI Memory vision is largely a future-state vision. Most organizations today are still focused on: RAG, Individual agent memory, Coding workflows, and Short-term context. The industry has not yet broadly embraced concepts such as: Unified Context, Memoromes, Personal Memory Vaults, Memory Distillation, and Cognitive Orchestration.

According to Shawn Shen, CEO of Memories.ai, “If your memory does not belong to you, it is not memory, it is surveillance. A Personal AI Memory app, owned by the individual and running where their data lives, is the only structure that scales. Anything else is a feature inside someone else’s product.” He added, “The smartphone is the closest analogy, but the mechanic is stronger. A phone makes you contactable. A memory layer makes you smarter, your agents more useful, and your day recoverable. Once a meaningful fraction of the population has it, the social pressure to have it follows.”

This solution addresses two fundamental challenges simultaneously: 1) preparing video data for AI training, and 2) enabling efficient licensing between fragmented supply and concentrated demand. Together, these capabilities establish a new category called “AI training data supply chain infrastructure”.

The combined voting results show a market where leadership is established but increasingly contested. Incumbents continue to dominate adoption, particularly in foundational platforms, but AI-native innovation is reshaping buyer expectations and redefining what leadership means. The next wave of winners in Sales and Marketing will not simply be the best tools within a category, but the platforms that unify data, intelligence, engagement, and execution across the entire revenue lifecycle.

To bring clarity and consistency to our AI Brand Leader surveys covering sales and marketing products, we needed a clear taxonomy of the modern landscape. The rapid rise of AI has blurred traditional category boundaries, making it difficult to evaluate vendor leadership using legacy frameworks. This article introduces the Modern AI Sales & Marketing Stack, […]

Captures five major phases in this evolution: Biological Memory, Digital Memory, “Dumb” AI Memory, Intelligent AI Memory, and Superhuman Memory. Each phase represents a step-change in persistence, context, and capability.

Connected TV has become one of the fastest-growing segments of digital advertising, but its effectiveness has been difficult to measure. Without reliable attribution, advertisers cannot fully evaluate the impact of their spending, and publishers cannot accurately demonstrate the value of their inventory. By connecting ad exposure on television devices to actions on mobile devices, Atlas Performance provides the missing link that allows advertisers to evaluate real-world outcomes. At the same time, the system’s clean room architecture ensures that this insight is delivered without compromising user privacy.

Unlike conventional computer vision systems which analyze images or video in isolation, AI visual memory systems are designed to create persistent, structured representations of real-world experience. These systems ingest continuous streams of video, audio, and images, transforming them into searchable, contextual memory frameworks that can be accessed in real time.

an AI primer for power professionals, but not in the traditional sense. It doesn’t attempt to turn grid operators, utilities, or energy technologists into AI engineers. Instead, it reframes AI as a demand shock, a once-in-a-generation force that is fundamentally reshaping power consumption, infrastructure design, and energy strategy. At its core is a simple but critical idea: AI is not just another workload. It is a new economic system powered by computation, and computation is powered by electricity.