TLDR: With the continued rapid innovation around LLMs and AI Agents, software as we know is getting a major upgrade. From on-premise software to SaaS, we experienced a major productivity boost for knowledge workers and created a massive industry in the process. As we drive forward into the genAI era, we will see the next generation of software applications, that instead of being delivered to us as a service (SaaS), actually be a service themselves, delivered to us in the form of software. This will be the era of Service-as-Software. Undoubtedly, the latest product releases by OpenAI yesterday, regarding agents / GPTs in particular, are major step in making this a reality.
It’s now almost 1 year A.C (After ChatGPT) and it's clear that it has sparked a new wave of excitement in the startup and tech world. This is especially emphasized in light of OpenAI’s product releases announced yesterday of becoming a basically a full fledged UGC platform where users can create and share agents, with everyone brainstorming what's now possible. Reflecting back on the software ecosystem, I've been particularly drawn to revisiting the original purpose behind creating SaaS platforms. Essentially, they were meant to boost our productivity as knowledge workers, and delivering this software through the cloud simply made everything more convenient and effective, which is how SaaS took off.
Software spend globally, whether it’s hosted in the cloud or on-site, has been on a rapid rise, reaching the massive market size of about ~$900 billion. But, alongside this hefty investment in software, is an even bigger bill for IT services.
As a matter of fact, for every dollar we spend on software, we end up spending 1.5x times more on IT services to get that software operational and successfully deployed. This includes areas like BPO, outsourcing and consulting, which take up most of that extra cost. You could argue that that number is potentially even understated since a lot of companies use FTEs to do the manual work around tuning, maintaining, integration and operational work. Bain & Company suggests that around 37% of IT tasks could be automated using genAI.
When you think about the vast $1.4 trillion annual spend on IT services, it's easy to see the huge chance for new companies to step in. There’s a big market out there, maybe even larger than the software market itself, ready for businesses that can use AI to shake things up.
Automating human labor and turning it into a successful software business is certainly not a new concept in Infra IT. Robotic Process Automation giant UI Path has led the charge in automating every manual task from data entry, invoice processing to supply chain logistics. In cybersecurity, Expel has built a fully transparent Managed Detection and Response (MDR) service that is powered by automation software to help its customers defend against cyber attacks.
With the rapid innovation around AI Agents, software applications as we know them are in process of getting a major upgrade. Agents are programs that can make decisions or perform actions based on its environment, user feedback or experiences. In short, they mimic the subtle human behavioral characteristics that make us humans quite effective at taking actions or fulfilling tasks. What this means in practice is that instead of software simply being delivered to us as a service, agents and LLMs will power the service to be delivered to us in the form of software. Thereby this ushers in the Era of Service-as-Software.
At the end of the day this really plays into the most exciting and scariest opportunity of AI and that is the ability of humans to process, reason and build knowledge while using all three effectively to conduct a task autonomously. Over the course of the last year, we have been investing in various companies in the infrastructure IT & cybersecurity space that play on this trend, but all have very similar characteristics that I wanted to summarize here.
In certain critical sectors, AI's intent isn't to supplant humans, particularly where there's a stark labor deficit preventing roles from being filled. Cybersecurity is a prime example, with a staggering 3.5 million positions lying vacant. The urgency intensifies as we witness a surge in hackers leveraging LLMs, escalating the frequency and sophistication of cyber-attacks. The barrage of alerts from security tools monitoring firewalls, endpoints, cloud assets, and emails can overwhelm even the most sophisticated security dashboards, akin to an unending cascade of Christmas lights. In the trenches are the beleaguered analysts in Security Operations Centers (SOCs), who grapple with the sheer volume of alerts, leaving them stretched thin and increasing the risk of critical breaches slipping through the net.
DropzoneAI, which is pioneering the development of a SOC Tier 1 Analyst powered by LLMs is designing an agent based system to autonomously sift through alerts and field ad-hoc queries, tapping into a host of tools and data repositories to investigate a security alert. Here you can see it conducting a full investigation of an AWS Guard Duty alert that gets fired to Splunk as somebody is trying to potentially conduct a data exfiltration out of S3.
In realms plagued by skill gaps and labor shortages, LLMs emerge not as replacements but as vital enhancements to human capabilities—acting as force multipliers where the need is most acute. Such strategic augmentation underscores AI’s role as a crucial ally in functions where shortages of labor are apparent.
The human capability to navigate through a labyrinth of systems to extract answers is nothing short of remarkable. Consider a developer engaged in the debugging process: the journey often involves traversing from an Integrated Development Environment (IDE) to an Application Performance Management (APM) platform, and then to a log management system to pinpoint the root of an issue. This scenario, where insights are nested within a multitude of platforms—be it in data engineering, DevOps, or cybersecurity—signals a prime niche for AI agents and LLMs. They thrive where tools sprawl and interoperability falters, stepping in when a human intermediary is traditionally tasked with piecing together a coherent narrative from disparate data sources. In DevOps, the aspiration to integrate logs, metrics, and events across various platforms has long been a topic of discussion. Data engineering wrestles with the fragmentation of information across silos, standardizing the data, checking its quality and feeding it into diverse analytical tools. Meanwhile, security analysts leapfrog over an extensive array of instruments—from Endpoint Detection and Response (EDR) systems to firewalls and logs—to thoroughly investigate alerts. The question then arises: might a Service-as-Software company adeptly shoulder the more monotonous tasks of aggregation and cross-tool interaction, thus sparing us to focus solely on critical decision-making? Such a solution could significantly transform the efficiency and efficacy of operations across these complex roles.
LLMs and AI agents are particularly adept at conducting deep-level analyses on a grand scale, thanks to their capacity to parse and make sense of data from a multitude of sources, all while maintaining the ability to offer a personalized touch. In the field of IT support, for instance, these AI systems can draw from a variety of databases, incident logs, user manuals, and forums to diagnose issues and provide solutions. An AI agent can analyze thousands of tickets from an IT support system, identify common problems, and suggest improvements or automated responses for future incidents. This capability not only streamlines the troubleshooting process but also allows for customized assistance; the AI can learn from each interaction with a user, recognizing patterns in the types of issues a particular user faces or the level of technical language they understand. It can then tailor its communication to match the user's expertise, whether it's a network engineer needing deep technical details to resolve a complex server issue or an end-user requiring step-by-step guidance to reset a password. This individualized approach is the result of sophisticated algorithms that adapt and optimize their output, ensuring that as users engage with the AI, the guidance they receive becomes increasingly focused on their specific needs and preferences, even in the context of a broad and complex IT landscape.
In the IT infrastructure and security sectors, there's a groundswell of opportunity to innovate with autonomous agents, propelling us toward a more seamless Service-as-Software (SaS) future. Crafting an agent system that is viable for the long haul and ready for production hinges on a deep integration of systems, workflows and data. Thereby I believe for these agents to truly be disruptive and enduring—ready to be deployed at scale—they must be built with a vertical-first and maybe almost role based first strategy (and potentially scale horizontally from there). This approach isn't just about achieving early wins; it’s about solving specific use cases while also aligning with already existing budget line items in the enterprise.
When considering which ideas to pursue first, the guiding star should be the urgency of labor need—roles that are hard to recruit for would allow agents to be adopted at a higher rate and provide a faster journey towards product-market fit. I couldn't be more excited about what this new paradigm shift will bring, so if you are thinking of building a company in this space, please reach out to me!