Enterprise IT has long been divided into silos. This is because of scarce resources and specialized knowledge required to perform some IT operations tasks. The world of today is much more focused on outcomes and the need for silos is waning. In this episode of the Tech Field Day Podcast, Stephen Foskett, Alastair Cooke, and Tom Hollingsworth discuss how enterprise IT has moved away from silos due to increased resource availability and cross training. They also look ahead to new challenges from advances like AI and quantum computing.

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Technology Silos Are a Thing of the Past

Enterprise IT has long been divided into silos. This is because of scarce resources and specialized knowledge required to perform some IT operations tasks. The world of today is much more focused on outcomes and the need for silos is waning. In this episode of the Tech Field Day Podcast, Stephen Foskett, Alastair Cooke, and Tom Hollingsworth discuss how enterprise IT has moved away from silos due to increased resource availability and cross training. They also look ahead to new challenges from advances like AI and quantum computing.

Silos were very popular in the days when you storage admins and your network engineers had their own spaces to operate and no one really did any cross training. You had your area and you stuck to it. As IT evolved those silo walls started coming down. Storage and compute merged because of virtualization. Wireless and traditional networking have started to become one edge-focused solution. All of those came even before the cloud started battering down the barriers to how we need to consider working with our infrastructure.

Part of the reason for the changes is abundance. We no longer have to conserve resources as we once did. Bandwidth is plentiful. Cloud computing makes CPU and storage effectively unlimited if budgets allow. Engineers no longer need to worry about the minutia of esoteric configurations that optimize dwindling resources. Instead, engineering and development talent have started to focus on outcomes. Applications have become the atomic unit of deployment, while networking and storage have been relegated to components of the overall solution.

That’s not to say that new challenges aren’t on the horizon for IT silos. AI is creating new boundaries based on resources that, while deep, are also very expensive to create and maintain. These new constraints are creating divisions just like the old silos. Another challenge is the need to simplify and abstract enterprise IT technology. Hiding the complexity from DevOps and AIOps teams doesn’t mean that it goes away. Instead it leads to bigger issues when the abstractions fail and the understanding of the siloed nature of IT isn’t there.

The future continues to be uncertain as the power needs of AI and the hardware requirements of quantum computing seem to be limitless. The unpredictability of the deployment of these technologies and the lack of efficiency they demonstrate today mean that we may have eliminated our existing silos only to have set up the creation of many more.

Podcast Information:

Tom Hollingsworth is the Networking Analyst for The Futurum Group and Event Lead for Tech Field Day. You can connect with Tom on LinkedIn and X/Twitter. Find out more on his blog or on the Tech Field Day website.

Stephen Foskett is the President of the Tech Field Day Business Unit and Organizer of the Tech Field Day Event Series, now part of The Futurum Group. Connect with Stephen on LinkedIn or on X/Twitter.

Alastair Cooke is a Tech Field Day Event Lead, now part of The Futurum Group. You can connect with Alastair on LinkedIn or on X/Twitter and you can read more of his research notes and insights on The Futurum Group’s website.

Thank you for listening to this episode of the Tech Field Day Podcast. If you enjoyed the discussion, please remember to subscribe on YouTube or your favorite podcast application so you don’t miss an episode and do give us a rating and a review. This podcast was brought to you by Tech Field Day, home of IT experts from across the enterprise, now part of The Futurum Group.

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The IT industry’s reliance on acquisitions is a necessary driver of innovation, though they often seem to get in the way of competition and progress. This episode of the Tech Field Day podcast, recorded during Cloud Field Day 21, features Ray Lucchesi, Jon Hildebrand, Ken Nalbone, and Stephen Foskett considering whether acquisitions in the IT industry are a necessary evil or a detriment to innovation. Acquisitions are often seen as a double-edged sword, with both positive and negative implications. On one hand, acquisitions can fuel innovation by providing smaller companies with the resources and market access they need to scale their ideas. On the other hand, they can stifle competition, lead to cultural clashes, and sometimes result in the disappearance of promising technologies or products.

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See more from Cloud Field Day 21 on the Tech Field Day website or YouTube channel.

We May Not Like Acquisitions in Tech But We Need them!

The IT industry has long been shaped by the cycle of acquisitions, with large companies absorbing smaller, innovative startups to bolster their portfolios. This practice is often seen as a double-edged sword. On one hand, acquisitions can inject fresh ideas and technologies into established organizations, enabling them to stay competitive in a rapidly evolving market. On the other hand, the process can stifle innovation, as smaller companies with promising technologies are often absorbed and their products either languish or are subsumed into larger, less agile corporate structures. The debate over whether acquisitions are a necessary evil or simply detrimental to the industry remains a contentious topic.

One of the key arguments in favor of acquisitions is their role in fostering innovation. Startups often emerge with groundbreaking ideas but lack the resources or market reach to scale effectively. Being acquired by a larger company can provide the necessary capital, infrastructure, and customer base to bring these innovations to a broader audience. However, this process is not without its pitfalls. Many acquisitions result in a clash of corporate cultures, leading to inefficiencies and, in some cases, the eventual dissolution of the acquired entity’s unique value proposition. This raises questions about whether the industry might benefit more from encouraging organic growth rather than relying on acquisitions as a growth strategy.

Critics argue that acquisitions often prioritize short-term financial gains over long-term innovation. Large corporations may acquire smaller companies not to integrate their technologies but to eliminate potential competition. This practice can lead to market consolidation, reducing diversity and stifling the competitive landscape. Furthermore, the focus on financial returns, driven by venture capital and private equity investments, often pressures startups to position themselves as acquisition targets rather than sustainable, standalone businesses. This dynamic can skew the priorities of emerging companies, emphasizing exit strategies over product development and customer satisfaction.

The role of private equity in driving acquisitions adds another layer of complexity. Private equity firms often seek to maximize returns by cutting costs and streamlining operations, which can lead to a loss of innovation and employee morale within the acquired company. While some private equity firms take a more hands-on approach to foster growth and innovation, others focus solely on financial metrics, potentially undermining the long-term viability of the companies they acquire. This dichotomy highlights the need for a more balanced approach to investment, one that prioritizes sustainable growth and innovation over short-term financial gains.

In an ideal world, the IT industry would thrive on organic growth, with companies building sustainable business models and scaling through customer acquisition and market expansion. However, the reality is that acquisitions are deeply ingrained in the industry’s fabric, driven by the need for rapid growth and the financial incentives of venture capital and private equity. While acquisitions may be a necessary evil in the current landscape, the industry must strive to ensure that they are conducted in a way that fosters innovation, benefits customers, and supports the long-term health of the market. The challenge lies in finding a balance that allows both startups and established companies to thrive without compromising the industry’s overall dynamism.

Podcast Information:

Stephen Foskett is the President of the Tech Field Day Business Unit and Organizer of the Tech Field Day Event Series, now part of The Futurum Group. Connect with Stephen on LinkedIn or on X/Twitter.

Ray Lucchesi is the president of Silverton Consulting and the host of Greybeards on Storage Podcast. You can connect with Ray on X/Twitter or on LinkedIn. Learn more about Ray on his website and listen to his podcast.

Jon Hildebrand is an automation and observability expert. You can connect with Jon on LinkedIn or on X/Twitter. Learn more about Jon by reading his personal blog.

Ken Nalbone is a Senior Solutions Architect at AHEAD. You can connect with Ken on X/Twitter, Bluesky, and on LinkedIn. Learn more about Ken on his personal website.

Thank you for listening to this episode of the Tech Field Day Podcast. If you enjoyed the discussion, please remember to subscribe on YouTube or your favorite podcast application so you don’t miss an episode and do give us a rating and a review. This podcast was brought to you by Tech Field Day, home of IT experts from across the enterprise, now part of The Futurum Group.

© Gestalt IT, LLC for Gestalt IT: Company Acquisitions are a Necessary Evil in Enterprise Tech

There is a significant gap between storage companies and their ability to effectively support AI infrastructure. In this episode of the Tech Field Day podcast, recorded during the AI Data Infrastructure Field Day 1 in Santa Clara, host Stephen Foskett and guests Kurtis Kemple, Brian Booden, and Rohan Puri explore the evolving relationship between storage and AI. The discussion highlights a significant gap between storage companies’ current capabilities and the demands of AI applications. While storage vendors are pivoting to support AI, many lack deep AI expertise, often focusing on cost and efficiency rather than offering integrated, AI-specific solutions. The panel emphasizes the need for storage companies to move beyond being mere data repositories and instead develop end-to-end solutions that address AI workflows, data preparation, and metadata management. They also stress the importance of education, partnerships, and hiring AI specialists to bridge the knowledge gap and drive innovation. The conversation underscores the early stage of this convergence, with a call for clearer strategies, open standards, and more cohesive integration between storage and AI to meet the growing demands of data-driven applications.

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Learn more about AI Data Infrastructure Field Day 1 and watch videos from these presentations on the Tech Field Day website.

How Can Storage Support AI?

The intersection of storage and AI infrastructure presents a complex and evolving challenge. While storage companies are increasingly pivoting toward AI solutions, there remains a significant gap in understanding and integration. Storage has traditionally been viewed as a low-level, technical domain focused on hardware like disks and file systems. On the other hand, AI, particularly in the context of large language models (LLMs) and data analytics, operates at a higher level, requiring nuanced data management and application-specific insights. This disconnect highlights the need for storage companies to move beyond simply offering cost-effective and high-performance infrastructure. Instead, they must develop a deeper understanding of AI workflows and provide solutions that address the specific needs of AI applications, such as data preparation, metadata management, and seamless integration with AI training pipelines.

One of the key challenges is the lack of “solutioning” in the storage industry. Many storage vendors focus on infrastructure performance and efficiency but fail to address how their products fit into the broader AI ecosystem. For instance, while some companies are integrating with GPU technologies to support AI workloads, this approach often stops at the infrastructure level. True integration requires a more comprehensive understanding of AI applications, extending beyond hardware to include data management, insights, and application-level affordances. Without this, storage solutions risk being perceived as generic and interchangeable, reducing their value proposition in the AI space.

Another critical issue is the fragmentation of data sources and the absence of standardized frameworks for integration. Data in AI workflows often comes from diverse sources, including databases, data warehouses, file systems, and cloud storage. These sources are frequently siloed, making it challenging to consolidate and analyze data effectively. While some progress has been made in the database world with open formats and decoupled layers, similar advancements are lacking in the storage domain. The industry needs open standards and protocols that enable seamless data integration across vendors and platforms, facilitating the development of unified AI solutions.

The role of storage companies in AI could evolve in two distinct directions: becoming specialized storage solutions for AI or serving as connectors that enable AI applications to access existing data seamlessly. Both approaches have merit, but they require a clear strategy and a deep understanding of AI workflows. Companies that choose to specialize in AI storage must offer features like automated data preparation, efficient data movement, and real-time insights. Conversely, those opting to act as connectors must focus on breaking down data silos and providing tools that simplify data access and integration for AI applications.

Education and leadership are crucial for bridging the gap between storage and AI. Storage companies need to hire AI specialists and empower them to influence product development and strategy. This requires a top-down approach, with leadership roles dedicated to understanding and addressing the unique challenges of AI. Without this internal expertise, companies risk creating a disconnect between their AI-focused messaging and the actual capabilities of their products. Moreover, fostering collaboration between storage and AI teams within organizations can lead to more innovative and effective solutions.

Finally, the industry is still in the early stages of addressing the intersection of storage and AI. While the rapid growth of data and the increasing complexity of AI workloads present significant challenges, they also offer opportunities for innovation. Storage companies that can adapt to these demands by developing specialized products, embracing open standards, and fostering cross-disciplinary expertise will be better positioned to succeed. As the market matures, we can expect to see a blending of technologies and a shift toward more integrated and user-friendly solutions that cater to the unique needs of AI applications.

Podcast Information:

Stephen Foskett is the President of the Tech Field Day Business Unit and Organizer of the Tech Field Day Event Series, now part of The Futurum Group. Connect with Stephen on LinkedIn or on X/Twitter.

Brian Booden is the Managing Director at DataGlow IT. You can connect with Brian on X/Twitter and on LinkedIn. Learn more about DataGlow IT on their website.

Rohan Puri is an Storage Infrastructure Engineer. You can connect with Rohan on LinkedIn or on X/Twitter. Learn more about him on his personal website.

Kurtis Kemple is the Director of Developer Relations as Slack. You can connect with Kurtis on LinkedIn or on X/Twitter. Learn more about Kurtis on his website.

Thank you for listening to this episode of the Tech Field Day Podcast. If you enjoyed the discussion, please remember to subscribe on YouTube or your favorite podcast application so you don’t miss an episode and do give us a rating and a review. This podcast was brought to you by Tech Field Day, home of IT experts from across the enterprise, now part of The Futurum Group.

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Network engineers are notorious for doing whatever it takes to keep their customers and users happy. No reference architecture is safe from modification. However, these unique designs, commonly referred to as “snowflakes”, create challenges when unforeseen consequences occur. In this episode of the Tech Field Day podcast, Tom Hollingsworth is joined by Dakota Snow, Steve Puluka, and Bob McCouch as they discuss the challenges behind snowflake design and operations. They talk about the best way to build better systems and prevent the challenges caused by uniqueness.

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Learn more about Networking Field Day 36 and the presenting companies on the Tech Field Day website.

Stop Building Snowflakes

Network engineers are notorious for doing whatever it takes to keep their customers and users happy. No reference architecture is safe from modification. However, these unique designs, commonly referred to as “snowflakes”, create challenges when unforeseen consequences occur. In this episode of the Tech Field Day podcast, Tom Hollingsworth is joined by Dakota Snow, Steve Puluka, and Bob McCouch as they discuss the challenges behind snowflake design and operations. They talk about the best way to build better systems and prevent the challenges caused by uniqueness.

The reasons why networks stray into unique territory are myriad. Customers may demand support for a custom application or use case. Organizations may grown in unanticipated ways, including acquisitions. Businesses, always faced with budget constraints, may cut funding or create heterogenous systems with their own challenges. The growth of these networks create an opportunity for designers to focus on completing the requirements and not on proper design guidelines.

These roadblocks create problem for those that want to build networks that can be automated with ease. Inconsistent deployments and poor documentation lead to the inability to properly automate operations and reduce the need for human interaction to accomplish goals. Every special exception to the rules creates a point of contention in your design that forces you to make an even more custom automation solution that doesn’t scale past your compromises.

The key to a proper network design is modular building blocks. By creating standard pieces that can be assembled in recognizable configurations you create consistency. That consistency makes it much better for future troubleshooting and even expansion. That’s because if the future additions to the network are necessary they will follow the same guidelines, which means more standardization and more capability to be automated. That consistency is the key to building a network that doesn’t melt at the first sign of trouble.

Podcast Information

Tom Hollingsworth is the Networking Analyst for The Futurum Group and Event Lead for Tech Field Day. You can connect with Tom on LinkedIn and X/Twitter. Find out more on his blog or on the Tech Field Day website.

Bob McCouch is a Presales Leader and Architect at AHEAD. You can connect with Bob on LinkedIn and on X/Twitter. Learn more about Bob on his personal website, Herding Packets.

Steve Puluka is a Network and Security Engineer as well as an IP Architect. You can connect with Steve on LinkedIn, on X/Twitter, and on Bluesky. Learn more about Steve on his personal website.

Dakota Snow is a Network Operations Director and Host of The Bearded IT Dad. You can connect with Dakota on LinkedIn or on X/Twitter. Learn more about The Bearded IT Dad and Dakota’s content on his website.

Thank you for listening to this episode of the Tech Field Day Podcast. If you enjoyed the discussion, please remember to subscribe on YouTube or your favorite podcast application so you don’t miss an episode and do give us a rating and a review. This podcast was brought to you by Tech Field Day, home of IT experts from across the enterprise, now part of The Futurum Group.

© Gestalt IT, LLC for Gestalt IT: Snowflake Networks are Built to Break

Wi-Fi is the most dominant client connectivity option on the market today. The growth of ubiquitous computing has only happened because of the mass deployment of Wi-Fi. However, Wi-Fi isn’t the only wireless solution and isn’t always the best way to connect devices. In this episode, Tom Hollingsworth is joined by Lee Badman, Troy Martin, and Ron Westfall as they discuss what other options exist and what workflows they can improve.

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Learn more about Mobility Field Day 12 and the presenting companies on the Tech Field Day website.

WiFi isn’t Always the Best Solution

Wi-Fi is the most dominant client connectivity option on the market today. The growth of ubiquitous computing has only happened because of the mass deployment of Wi-Fi. However, Wi-Fi isn’t the only wireless solution and isn’t always the best way to connect devices. In this episode, Tom Hollingsworth is joined by Lee Badman, Troy Martin, and Ron Westfall as they discuss what other options exist and what workflows they can improve.

Wi-Fi is everywhere and the most popular way for users to get on the Internet. However, there are a lot of devices that operate today that don’t interact directly with users. These could be sensor networks or other IoT devices. You could also have areas that don’t work well with Wi-Fi, such as a large public venue. Wi-Fi has certain requirements for things like power and bandwidth that may not play well with your particular application.

There are a lot of alternative wireless technologies that aren’t Wi-Fi. LoRaWAN, Private 5G, Wi-Fi HaLw, CBRS, Zigbee, and many others offer their own unique solutions for a given use case. This could be lower power consumption or even longer range for isolated devices. Given that most IoT devices don’t need to transmit large amounts of data frequently you can see why solutions like those listed above may be better alternatives.

Another thing to consider is that Wi-Fi alternatives don’t need to be used exclusively. You can configured your sensor network to operate over LoRaWAN, your warehouse scanners to use CBRS, and your client tablets to use Wi-Fi 6. This would use your spectrum efficiently and ensure your devices are getting what they need to perform properly.

There is always the risk that these competing standards could cause the wireless space to fracture into different camps much like the wired networking space did many years ago. However, with the vastly different kinds of devices out there that have unique requirements it is more likely that individual companies will develop those competing protocols for their own needs and leave Wi-Fi as the common denominator for everyone.

Podcast Information:

Tom Hollingsworth is the Networking Analyst for The Futurum Group and Event Lead for Tech Field Day. You can connect with Tom on LinkedIn and X/Twitter. Find out more on his blog or on the Tech Field Day website.

Ron Westfall is The Research Director at The Futurum Group specializing in Digital Transformation, 5G, AI, Security, Cloud Computing, IoT and Data Center as well as the host of 5G Factor Webcast. You can connect with Ron on LinkedIn and on X/Twitter and see his work on The Futurum Group’s website.

Troy Martin is founder and director at Trogen Consulting, specializing in mobile and wireless technologies. You can connect with Troy on X/Twitter and on LinkedIn. Learn more about Troy on his website.

Lee Badman is a network architect and independent analyst specializing in wireless and mobility. You can connect with Lee on LinkedIn or on X/Twitter. Learn more on his website.

Thank you for listening to this episode of the Tech Field Day Podcast. If you enjoyed the discussion, please remember to subscribe on YouTube or your favorite podcast application so you don’t miss an episode and do give us a rating and a review. This podcast was brought to you by Tech Field Day, home of IT experts from across the enterprise, now part of The Futurum Group.

© Gestalt IT, LLC for Gestalt IT: WiFi isn’t Always the Best Solution

Generative AI is transforming many industries where people create content. Software development is no different; AI agents are in almost every development platform. But is AI improving application development and software quality? This episode of the Tech Field Day Podcast looks at some of the issues revolving around AI and App Dev with Alastair Cooke, Guy Currier, Jack Poller, and Stephen Foskett. The ultimate objective of a software development team is to deliver an application that fulfills a business need and helps the organization be more successful. An AI that can recommend basic code snippets doesn’t move that needle far. More sophistication is needed to get value from AI in the development process. The objective should be to have AI handle the repetitive tasks and allow humans to focus on innovative tasks where generative AI is less capable. AI agents must handle building tests and reviewing code for security and correctness to enable developers to concentrate on building better applications that help organizations.

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Learn more about AppDev Field Day 2 on the Tech Field Day website, including information on presenting companies, delegates, analysts, and more.

AI Doesn’t Make AI Any Better

The ultimate objective of a software development team is to deliver an application that fulfils a business need and helps the organization be more successful. An AI that can recommend basic code snippets doesn’t move that needle far. More sophistication is needed to get value from AI in the development process. The objective should be to have AI handle the repetitive tasks and allow humans to focus on innovative tasks where generative AI is less capable. A vital first step is making the AI aware of the unique parts of the organization where it is used, such as the standards, existing applications and data. A human developer is more effective as they learn more about the team and organization where they work, and so can an AI assistant. 

One of the ways AI can be used to improve software development is in data normalization, taking a diverse set of data and presenting it in a way that allows simple access to that data. An example is a data lake with social media content, email archives, and copies of past transactions from our sales application, all in one place. An AI tool that reads the unstructured social media and emails, presenting it as more structured data for SQL-based querying. Handling these types of low-precision data is an ideal generative AI task; reporting on the exact data in the sales records is not somewhere we want hallucinations. Generative AI might also be great for working out my address from my vague description rather than demanding that I enter my street address and postcode precisely as they are recorded in the postal service database.  

Software testing is another place where AI assistants or agents can help by taking care of routine and tedious tasks. Testing every new feature is essential to automating software development and deployment, but writing tests is much less satisfying than writing new features. An AI agent that creates the tests from a description of how the feature should work is a massive assistance to a developer and ensures code quality through good test coverage. Similarly, AI-based code review can reduce the effort required to ensure new developers write good code and implement new features well. Reviews for style, correctness, and security are all critical for software quality. Both testing and code review are vital parts of good software development and take considerable developer effort. Reducing these tedious tasks would leave more time for developers to work on innovation and align better with business needs.

The challenge of AI agents and assistants is that we don’t yet trust the results and still need a human to review any changes proposed by the AI. Tabnine reports that up to 50% of the changes suggested by their AI are accepted without modification. That leaves 50% of suggestions that aren’t wholly acceptable. That rate must be much higher before this AI can operate without human oversight.  Ideally, the AI could identify which changes will likely be accepted and flag a confidence rating. Over time, we might set a confidence threshold that requires human review. Similarly, we might take a manufacturing approach to code reviews and tests. Allow the AI to operate autonomously and sample test the resulting code every ten or hundred changes.

Podcast Information:

Alastair Cooke is a Tech Field Day Event Lead, now part of The Futurum Group. You can connect with Alastair on LinkedIn or on X/Twitter and you can read more of his research notes and insights on The Futurum Group’s website.

Guy Currier is the VP and CTO of Visible Impact, part of The Futurum Group. You can connect with Guy on X/Twitter and on LinkedIn. Learn more about Visible Impact on their website. For more insights, go to The Futurum Group’s website.

Jack Poller is and industry leading cybersecurity analyst and Founder of Paradigm Technica. You can connect with Jack on LinkedIn or on X/Twitter. Learn more on Paradigm Technica’s website.

Stephen Foskett is the President of the Tech Field Day Business Unit and Organizer of the Tech Field Day Event Series, now part of The Futurum Group. Connect with Stephen on LinkedIn or on X/Twitter.

Thank you for listening to this episode of the Tech Field Day Podcast. If you enjoyed the discussion, please remember to subscribe on YouTube or your favorite podcast application so you don’t miss an episode and do give us a rating and a review. This podcast was brought to you by Tech Field Day, home of IT experts from across the enterprise, now part of The Futurum Group.

© Gestalt IT, LLC for Gestalt IT: AI Doesn’t Make App Dev Any Better

Artificial Intelligence is creating the kind of paradigm shifts not seen since the cloud revolution. Everyone is changing the way their IT infrastructure operates in order to make AI work better. In this episode of the Tech Field Day Podcast, Tom Hollingsworth is joined by John Freeman, Scott Robohn, and Ron Westfall as they discuss how AI is driving innovation in the networking market. They talk about how the toolsets are changing to incorporate AI features as well as how the need to push massive amounts of data into LLMs and generative AI constructs is creating opportunities for companies to show innovation. They also talk about how Ethernet is becoming ascendant in the AI market.

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Learn more about Networking Field Day 36 and the presenting companies on the Tech Field Day website.

AI is the Enabler of Network Innovation

Artificial Intelligence is creating the kind of paradigm shifts not seen since the cloud revolution. Everyone is changing the way their IT infrastructure operates in order to make AI work better. In this episode of the Tech Field Day Podcast, Tom Hollingsworth is joined by John Freeman, Scott Robohn, and Ron Westfall as they discuss how AI is driving innovation in the networking market. They talk about how the toolsets are changing to incorporate AI features as well as how the need to push massive amounts of data into LLMs and generative AI constructs is creating opportunities for companies to show innovation. They also talk about how Ethernet is becoming ascendant in the AI market.

Modern network operations and engineering teams have a bevy of tools they need to leverage like Python, GitHub, and cloud platforms. AI is just another one of those tools, such as using natural language conversational interfaces to glean information from a dashboard. This can also be seen in the way that AI is having a societal impact on the way that we live and work. The move toward incorporating AI into every aspect of software can’t help but sweep up networking as well.

Large amounts of data are being sent to large language models (LLMs) for storage and processing. Much like the big data crazy of years gone by we’re pushing more and more information into systems that will operate on it to discover context and meaning. Even more than before, however, is the need to deliver the data to the AI compute clusters that need to do the operations. The idea of data gravity is lost when the AI clusters have an even stronger pull. That means that the network must be optimized even more than ever before.

Ethernet is quickly becoming the more preferred alternative to traditional InfiniBand. While there are clear advantages in some use cases, InfiniBand’s dominance is waning as Ethernet fabrics gain ground in performance. When you add in the ease with which Ethernet can scale to hundreds of thousands of nodes you can see why providers, especially those that are offering AI-as-a-Service, would prefer to install Ethernet today instead of spending money on a technology that has an uncertain future.

Lastly, we discuss what happens if the AI bubble finally bursts and what may drive innovation in the market from there. This isn’t the first time that networking has faced a challenge from drivers of feature development. It wasn’t that long ago that OpenFlow and SDN were the hottest ticket around and everything was going to be running in software sooner or later. While that trend has definitely cooled we now see the benefits of the innovation it spurred and how we can continue to create value even if the primary driver for that innovation is now a footnote.

Podcast Information:

Tom Hollingsworth is the Networking Analyst for The Futurum Group and Event Lead for Tech Field Day. You can connect with Tom on LinkedIn and X/Twitter. Find out more on his blog or on the Tech Field Day website.

Ron Westfall is The Research Director at The Futurum Group specializing in Digital Transformation, 5G, AI, Security, Cloud Computing, IoT and Data Center as well as the host of 5G Factor Webcast. You can connect with Ron on LinkedIn and on X/Twitter and see his work on The Futurum Group’s website.

Scott Robohn is the VP of Technology at Cypress Consulting and Cofounder of Network Automation Forum. You can connect with Scott on X/Twitter or on LinkedIn. Learn more on the Network Automation Forum.

John Freeman is an equity analyst at Ravenswood Partners. Connect with John on LinkedIn and learn more about him over on Ravenswood Partners’ website.

Thank you for listening to this episode of the Tech Field Day Podcast. If you enjoyed the discussion, please remember to subscribe on YouTube or your favorite podcast application so you don’t miss an episode and do give us a rating and a review. This podcast was brought to you by Tech Field Day, home of IT experts from across the enterprise, now part of The Futurum Group.

© Gestalt IT, LLC for Gestalt IT: AI is the Enabler of Network Innovation

Edge computing is one of the areas where we see startup vendors offering innovative solutions, enabling applications to operate where the business operates rather than where the IT team sit. This episode of the Tech Field Day podcast focuses on the melting pot of edge computing and features Guy Currier, John Osmon, Ivan McPhee, and host Alastair Cooke, all of whom attended the recent Edge Field Day in September. To accommodate the unique nature of the diverse and unusual locations where businesses operate, many different technologies are brought together to form the melting pot of edge computing. Containers and AI applications are coming from the massive public cloud data centres to a range of embedded computers on factory floors, industrial sites, and farm equipment. ARM CPUs, sensors, and low-power hardware accelerators are coming from mobile phones to power applications in new locations. Enterprise organizations must still control and manage data and applications across these locations and platforms. Security must be built into the edge from the beginning; edge computing often happens in an unsecured location and often with no human oversight. This melting pot of technology and innovation makes edge computing an innovative part of IT.

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Edge Computing is a Melting Pot of Technology

The edge computing landscape sometimes feels like a cross between the public cloud and ROBO, yet edge computing is neither of these things. The collection of unique drivers bringing advanced applications and platforms to ever more remote locations requires a unique collection of capabilities. Edge computing is a melting pot of existing technologies and techniques, with innovation filling the gaps to bring real business value. 

The original AI meme application, Hotdog or Not, has become a farming application, Weed or Crop. An AI application runs on a computer equipped with cameras and mounted to a tractor as it drives down the rows in a field, identifying whether the plants it sees are the desired crop or an undesirable weed. The weeds get zapped with a laser, so there is no need for chemical weed killers as the tractor physically targets individual pest plants. The AI runs on a specialized computer designed to survive hostile conditions on a farm, such as dust, rain, heat, and cold. The tractor needs some of the capabilities of a mobile phone, connectivity back to a central control and management system, plus operation on a limited power supply. Is there enough power to run an NVIDIA H100 GPU on the tractor? I doubt it. This Weed vs Crop AI must run on a low-power accelerator on the tractor. Self-driving capabilities get melted into the solution; a tractor that drives itself can keep roaming the field all day. Freed from the limitations of a human driver, the tractor can move slower and may even use solar power for continuous operation.

There is an argument that the edge is the same as the cloud, a tiny cloud located where the data is generated and a response is required. This often has a foundation in attempts to solve edge problems by being cloud-first and reusing cloud-native technologies at edge locations. From the broader business perspective, cloud and edge are implementation details for gaining insight, agility, and profit. The implementation details are very different. Simply lifting methodologies and technologies from a large data centre and applying them to every restaurant in your burger chain is unlikely to end well. Containerization of applications has also been seen as a cloud technology that is easily applied to the edge. Containers are a great way to package an application for distribution, and the edge is a very distributed use case. At the edge, we often need these containers to run on small and resource-limited devices. Edge locations usually have little elasticity, which is a core feature of public cloud infrastructure. Container orchestration must be lightweight and self-contained at the edge. Management through a cloud service is good, but disconnected operation is essential.

Surprisingly, edge locations also lack the ubiquitous connectivity part of the NIST cloud definition. Individual edge sites seldom have redundant network links and usually have low-cost links with low service levels. Applications running at an edge location must be able to operate when there is no off-site network connectivity. The edge location might be a gas station operating in a snowstorm; the pumps must keep running even if the phone lines are down. This feels more like a laptop user use case, where the device may be disconnected, and IT support is usually remote. Device fleet management is essential for edge deployments. A thousand retail locations will have more than a thousand computers, so managing the fleet through policies and profiles is far better than one by one.

Security at the edge also differs from data centre and cloud security; edge locations seldom have physical security controls. Even our staff working for minimum wage at these locations may not be trusted. The idea of zero trust gets melted into many edge computing solutions. Validating every part of the device and application startup to ensure nothing has been tampered with or removed. Zero trust may extend to the device’s supply chain when sent to the edge location. Many edge platform vendors pride themselves on the ability of an untrained worker to deploy the device at the edge, a long way from the safe-hands deployments we see in public cloud and enterprise data centres. 

Edge computing has a unique set of challenges that demand multiple technologies combined in new ways to fulfil business requirements. This melting pot of technologies is producing new solutions and unlocking value in new use cases. 

Podcast Information

Alastair Cooke is a Tech Field Day Event Lead, now part of The Futurum Group. You can connect with Alastair on LinkedIn or on X/Twitter and you can read more of his research notes and insights on The Futurum Group’s website.

John Osmon is a consultant and a network designer / coordinator. You can connect with John on Twitter or on LinkedIn and check out his writing on Miscreants in Action.

Guy Currier is the VP and CTO of Visible Impact, part of The Futurum Group. You can connect with Guy on X/Twitter and on LinkedIn. Learn more about Visible Impact on their website. For more insights, go to The Futurum Group’s website.

Ivan McPhee is a Senior Security and Networking Analyst at GigaOm. You can connect with Ivan on LinkedIn and on X/Twitter. You can learn more about his work on the GigaOm website.

Thank you for listening to this episode of the Tech Field Day Podcast. If you enjoyed the discussion, please remember to subscribe on YouTube or your favorite podcast application so you don’t miss an episode and do give us a rating and a review. This podcast was brought to you by Tech Field Day, home of IT experts from across the enterprise, now part of The Futurum Group.

© Gestalt IT, LLC for Gestalt IT: Edge Computing is a Melting Pot of Technology

Public Cloud computing is a large part of enterprise IT alongside on-premises computing. Many organizations that had a cloud-first approach and are now gaining value from on-premises private clouds and seeing their changing business needs leading to changing cloud use. This episode of the Tech Field Day podcast delves into the complexity of multiple cloud providers and features Maciej Lelusz, Jack Poller, Justin Warren, and host Alastair Cooke, all attendees at Cloud Field Day. The awareness of changing business needs is causing some re-thinking of how businesses use cloud platforms, possibly moving away from using cloud vendor specific services to bare VMs. VMs are far simpler to move from one cloud to another, or between public cloud and private cloud platforms. Over time, the market will speak and if there are too many cloud providers, we will see mergers, acquisitions or failures of smaller specialized cloud providers. In the meantime, choosing where to put which application for the best outcome can be a challenge for businesses.

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There are Too Many Clouds

Public Cloud computing is a large part of enterprise IT alongside on-premises computing. Many organizations that had a cloud-first approach and are now gaining value from on-premises private clouds and seeing their changing business needs leading to changing cloud use. Whether it a return to on-premises private clouds or moving applications between cloud providers, mobility and choice are important for accommodating changing needs.

In the early days of public cloud adoption, on-premises cloud was more of an aspiration than a reality. Over the years, private cloud has become a reality for many organisations, even if the main service delivered is a VM, rather than rich application services. If VMs are the tool of mobility between public clouds, then VMs are quite sufficient for mobility to private clouds. The biggest challenge in private cloud is that VMware by Broadcom has refocussed and repriced the most common private cloud platform. The change provides an opportunity for VMware to prove its value and for competing vendors to stake their claim to a large on-premises Virtualization market.
Beyond the big three, four or five public cloud providers, there are a plethora of smaller public clouds that offer their own unique value. Whether it is Digital Ocean with an easy consumption model or OVH jumping into the GPU-on-demand market for AI training, there is a public cloud platform for many different specialised use cases. Each cloud provider makes a large up-front investment in platform, their technology, and often their real estate. The investment is only to generate a return for their founders, if the market doesn’t adopt their services, then the provider’s lifespan is very finite. Sooner or later the market will drive towards a sustainable population of cloud providers delivering the services that help their clients.

One challenge to using multiple clouds is that there is little standardization of the services across clouds. In fact, public cloud providers aim to lock customers into their cloud by providing unique features and value. The unique value may be in providing developer productivity or in offering unique software licensing opportunities. Anywhere a business uses this unique cloud value to provide business value, the cost of leaving the specific cloud provider increases. There is an argument that using the lowest common denominator of cloud, the virtual machine or container, is a wise move to allow cloud platform choice. A database server in a VM is much easier to move between clouds that migrating from one cloud’s managed database service to a different provider. If the ability to do cloud arbitrage is important, then you need your applications to be portable and not locked to one cloud platform by its unique features and value.

Whether there are too many clouds is a matter of perspective and opinion. Time will tell whether there are too many cloud providers and whether standardization of cloud services will evolve. Right now, some companies will commit to a single cloud provider and seek to gain maximum value form that one cloud while other companies play the field and seek to gain separate value from each cloud. We are certainly seeing discussions about private cloud as an option for many applications and a concern as the incumbent primary provider is changing approach. Will we see more clouds over time or fewer?

Podcast Information

Alastair Cooke is a Tech Field Day Event Lead, now part of The Futurum Group. You can connect with Alastair on LinkedIn or on X/Twitter and you can read more of his research notes and insights on The Futurum Group’s website.

Justin Warren is the Founder and Chief Analyst at PivotNine. You can connect with Justin on X/Twitter or on LinkedIn. Learn more on PivotNine’s website. See Justin’s website to read more.

Jack Poller is and industry leading cybersecurity analyst and Founder of Paradigm Technica. You can connect with Jack on LinkedIn or on X/Twitter. Learn more on Paradigm Technica’s website.

Maciej Lelusz is the Founder & CEO of evoila Poland. You can connect with Maciej on LinkedIn and on Twitter. Learn more about him on his website.

Thank you for listening to this episode of the Tech Field Day Podcast. If you enjoyed the discussion, please remember to subscribe on YouTube or your favorite podcast application so you don’t miss an episode and do give us a rating and a review. This podcast was brought to you by Tech Field Day, home of IT experts from across the enterprise, now part of The Futurum Group.

© Gestalt IT, LLC for Gestalt IT: There are Too Many Clouds

With the advent of quantum computers, the likelihood that modern encryption is going to be invalidated is a possibility. New standards from NIST have arrived that have ushered in the post-quantum era. You don’t need to implement them yet but you need to be familiar with them. Tom Hollingsworth is joined by Jennifer Minella, Andrew Conry-Murray, and Alastair Cooke in this episode to discuss why post-quantum algorithms are needed, why you should be readying your enterprise to use them, and how best to plan your implementation strategy.

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You Don’t Need Post-Quantum Crypto Yet

With the advent of quantum computers, the likelihood that modern encryption is going to be invalidated is a possibility. New standards from NIST have arrived that have ushered in the post-quantum era. You don’t need to implement them yet but you need to be familiar with them. Tom Hollingsworth is joined by JJ MInella, Drew Conry-Murray, and Alastair Cooke in this episode to discuss why post-quantum algorithms are needed, why you should be readying your enterprise to use them, and how best to plan your implementation strategy.

The physics behind using quantum computers may be complicated but the results for RSA-based cryptography are easy to figure out. Once these computers reach a level of processing power and precision that allows them to instantly factor numbers it will invalidate the current methods of encryption key generation. That means that any communication using RSA-style keys will be vulnerable.

Thankfully the tech industry has known about this for years. The push to have NIST implement new encryption standards has been going on for the past two years. The candidates were finalized in mid-2024 and we’re already starting to see companies adopting them for use. This is hopeful because it means that we will have familiarity with the concepts behind the methods used before the threshold is reached that will force us to use these new algorithms.

Does this mean that you need to move away from using traditional RSA methods today? No, it doesn’t. What it does mean is that you need to investigate the new NIST standards and understand when and how they can be implemented in your environment and whether or not any additional hardware will be needed to support that installation.

As discussed, the time to figure this out is now. You have a runway to get your organization up to speed on these new technologies without the pain of a rushed implementation. Quantum computers may not be ready to break things apart today but the rate at which they are improving means it is only a matter of time before the day when you’ll need to switch over to prevent a lot of chaos with your encrypted data and communications.

Podcast Information:

Tom Hollingsworth is the Networking Analyst for The Futurum Group and Event Lead for Tech Field Day. You can connect with Tom on LinkedIn and X/Twitter. Find out more on his blog or on the Tech Field Day website.

Alastair Cooke is a Tech Field Day Event Lead, now part of The Futurum Group. You can connect with Alastair on LinkedIn or on X/Twitter and you can read more of his research notes and insights on The Futurum Group’s website.

Jennifer “JJ” Minella is Founder and Principal Advisor of Network Security at Viszen Security and the co-host of Packet Protector for Packet Pushers. You can connect with JJ on X/Twitter or on LinkedIn. Learn more about her on her website.

Drew Conry-Murray is the Content Director at Packet Pushers Interactive and host of the Network Break podcast. You can connect with Drew on X/Twitter or on LinkedIn. Learn more about Packet Pushers on their website.

Thank you for listening to this episode of the Tech Field Day Podcast. If you enjoyed the discussion, please remember to subscribe on YouTube or your favorite podcast application so you don’t miss an episode and do give us a rating and a review. This podcast was brought to you by Tech Field Day, home of IT experts from across the enterprise, now part of The Futurum Group.

© Gestalt IT, LLC for Gestalt IT: You Don’t Need Post-Quantum Crypto Yet

The modern enterprise network automation strategy is failing. This is due in part to a collection of tools masquerading as an automation solution. In this episode, Tom Hollingsworth is joined by Scott Robohn, Bruno Wollmann, and special guest Mike Bushong of Nokia to discuss the current state of automation in the data center. They discuss how tools are often improperly incorporated as well as why organizations shouldn’t rely on just a single person or team to affect change. They also explore ideas around Nokia Event-Driven Automation (EDA), a new operations platform dedicated to solving these issues.

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Network Automation is More Than Just Tooling

The focus for most enterprises of “work reduction” when it comes to automation projects has a very short lifespan. As soon as people are satisfied they have saved themselves some time with their daily work they have a hard time translating that into a more strategic solution. Stakeholders want automation to save time and money, not just make someone’s job easier.

Also at stake is the focus on specific tools instead of platforms. Tools can certainly make things easier but there is very little integration between them. This means that when a new task needs to be automated or a new department wants to integrate with the system more work is required for the same level out output. Soon, the effort that goes into maintaining the automation code is more than the original task that was supposed to be automated.

The guests in this episode outline some ideas that can help teams better take advantage of automation, such as ensuring the correct focus is on the end goal and not just the operational details of the work being done. They also discuss Nokia Event-Driven Automation (EDA), which is a new operations platform that helps reimagine how data center network operations should be maintained and executed. The paradigm shift under the hood of Nokia EDA can alleviate a lot of the issues that are present in half-hearted attempts at automation and lead to better network health and more productive operations staff.

Podcast Information:

Tom Hollingsworth is a Networking and Security Specialist at Gestalt IT and Event Lead for Tech Field Day. You can connect with Tom on LinkedIn and X/Twitter. Find out more on his blog or on the Tech Field Day website.

Scott Robohn is the VP of Technology at Cypress Consulting and Cofounder of Network Automation Forum. You can connect with Scott on X/Twitter or on LinkedIn. Learn more on the Network Automation Forum.

Bruno Wollmann is a Network Architect and Networking Expert. You can connect with Bruno on LinkedIn and learn more about him on his website.

Mike Bushong is the Vice President of Data Center at Nokia. You can connect with Mike on X/Twitter or on LinkedIn. Learn more about Nokia on their website.

Thank you for listening to this episode of the Tech Field Day Podcast. If you enjoyed the discussion, please remember to subscribe on YouTube or your favorite podcast application so you don’t miss an episode and do give us a rating and a review. This podcast was brought to you by Tech Field Day, home of IT experts from across the enterprise, now part of The Futurum Group.

© Gestalt IT, LLC for Gestalt IT: Network Automation Is More Than Just Tooling