Vijay Raina is a distinguished specialist in enterprise SaaS technology and tools, widely recognized for his forward-thinking perspectives on software design and architecture. As a leader in the field, he has spent years analyzing how digital infrastructures influence productivity and how emerging platforms reshape the way businesses interact with their users. In this discussion, we explore a significant shift in the AI landscape: the transition from standalone “destination” applications to integrated “infrastructure” ecosystems. We delve into the hidden costs of the modern workflow, the surprising evolution of messaging platforms as cloud-based operating systems, and why the future of AI SaaS depends on embedding tools directly where the work is already happening. Our conversation covers the staggering data behind context switching, the economic advantages of low-friction onboarding, and the strategic window of opportunity currently open for developers and investors within the Telegram ecosystem.
The modern digital landscape is characterized by an abundance of powerful AI tools, yet many founders are finding that retention remains a persistent challenge despite the quality of their products. How does the architectural failure of building tools as “destinations” rather than “infrastructure” contribute to the massive loss of productivity we see in the current workforce?
The core of the problem lies in the physical and mental distance a user must travel to get a task done, which we often overlook when designing sleek interfaces. When we look at the data, the average digital worker is essentially caught in a cycle of constant reorientation, toggling between various applications and websites nearly 1,200 times every single day. This isn’t just a minor annoyance; it represents a structural failure in how we build software, leading to a loss of almost four hours every week just to get back into the flow of work. Over a year, this adds up to roughly five full working weeks lost to context switching, which accounts for about nine percent of a person’s total annual working time. AI tools that require a separate login, a separate tab, and a separate mental space are asking users to fight their own habits, and most users eventually stop fighting and simply stop using the tool. By building a “destination,” founders are essentially creating a beautiful island that requires a long boat ride to reach, whereas the modern worker is crying out for a bridge that connects directly to the mainland of their daily routine.
While many still view messaging platforms through the lens of simple communication, you suggest that the reality has shifted toward something much more foundational. How has Telegram transitioned into a cloud-based operating system, and what specific behaviors are driving this transformation for its billion-strong user base?
Telegram has fundamentally outgrown its original identity as a messenger and has become an environment where people live their entire digital lives. With one billion monthly active users and a staggering 500 million people active every single day, the platform possesses a level of gravity that standalone apps simply cannot match. What’s truly remarkable is the behavioral datactive users open the app at least 21 times a day, and their average sessions last about 41 minutes. This level of immersion creates a “gravity well” for productivity; when people are already spending nearly an hour of their day inside an interface, it makes sense to bring the work to them rather than pulling them away. We are seeing business channels with over 10,000 subscribers grow by 39% in 2025, which signals a massive shift toward B2B and fintech operations running entirely within the messenger. This infrastructure allows for CRM dashboards and trading terminals to exist right next to a conversation, effectively turning the chat interface into a command center for professional life.
In the traditional SaaS model, the journey from discovery to the “aha moment” is often fraught with friction that causes potential users to drop off. How do Telegram Mini Apps redefine the economics of user acquisition and onboarding for AI developers?
The traditional SaaS onboarding funnel is essentially a gauntlet of friction—email verification, password creation, and payment redirects—that kills the momentum of a curious user before they ever see the product’s value. In the Telegram ecosystem, this entire process is compressed into a nearly frictionless experience because the user is already authenticated and “logged in” to the platform itself. This compression results in user acquisition costs that are incredibly low, typically running between two cents and fifty cents per user, which is a fraction of what you would see on traditional mobile networks. Furthermore, the click-through rates we see here are ten to forty times the industry standard, because the transition from a post or a message to a functional tool is seamless. A product can leverage native authentication to deliver a production-ready asset in under three clicks, which means the “time to value” is measured in seconds rather than minutes or hours. This efficiency isn’t just a convenience; it is a fundamental shift in unit economics that allows a product to scale and retain users who would otherwise be lost to the “mental overhead” of a traditional sign-up process.
There is a significant distinction between tools that offer “generation assistance” and those that provide “total workflow automation.” Using the example of platforms like @morty, can you explain why addressing the entire operational chain is the only way to truly solve the productivity crisis?
Most AI tools on the market today only solve a tiny sliver of a much larger problem, providing a quick output but leaving the user to handle the heavy lifting of the surrounding steps. If you generate a draft in one tool, move it to another for visuals, and then a third for scheduling, you haven’t actually automated the job; you’ve just automated one task and created four new handoff points. The real opportunity lies in end-to-end automation, where the tool manages the entire lifecycle of a project—from trend monitoring and ideation to final distribution and analytics—all without leaving the platform where the content lives. We see this philosophy in action with @morty, which is designed specifically for Telegram channel operators who used to juggle five or six different tools to perform a single job. Every time a handoff is eliminated, you prevent context from being dropped and time from being bled out, which transforms the tool from a simple assistant into a comprehensive operational infrastructure. By automating the whole process, you’re not just giving the user a better result; you’re giving them back the sixty percent of their week that was previously consumed by manual production tasks.
When looking at the financial justification for these integrated tools, the numbers suggest a very compelling case for ROI. What does the data tell us about the long-term economic benefits for companies that embrace AI-driven workflow automation?
The business case for this level of integration has moved from theoretical to factual, backed by significant data regarding return on investment. Marketing automation ROI averages an impressive 544 percent over a three-year period, which translates to earning about five dollars and forty-four cents for every single dollar invested in the technology. We also see that 76 percent of companies achieve a positive ROI within the first year of implementation, with most recouping their initial investment in less than six months. Furthermore, a 2024 McKinsey report highlighted that companies leveraging AI specifically in marketing see 20 to 30 percent higher ROI on their campaigns compared to those sticking with traditional, fragmented methods. These numbers reflect the reality that true cost restructuring doesn’t come from making one step faster, but from eliminating the dead time between steps and allowing human workers to focus on high-level strategy and iteration. For a small media team or a solo operator, the arithmetic is even more direct: when you automate production, you aren’t just saving money, you’re buying the freedom to be more creative and faster in your market response.
The Telegram Mini App ecosystem experienced a period of intense hype and fluctuating user numbers in late 2024 and early 2025. How should founders interpret these shifts, and why is the current “stabilization” phase more important than the initial peak?
It is easy to get distracted by the massive, inflated numbers seen during a hype cycle, but the true value of an ecosystem is revealed when that hype fades. We saw Mini App activity peak at 1.44 billion monthly active users in September 2024, largely driven by viral “tap-to-earn” games that offered high visibility but often lacked long-term utility. The subsequent stabilization to a range of 150 to 190 million users by mid-2025 is actually a very healthy development because it signifies the transition from “tourist” users to “resident” users. The people who remain are using Mini Apps for practical, everyday purposes, providing a much more stable and reliable audience for serious SaaS products. For a founder, the “launch phase” is actually the most dangerous time because positive feedback from early, motivated adopters can mask underlying flaws in the product’s ability to retain more skeptical, mainstream users. The real test of product-market fit isn’t how many people you can get to click a button during a craze, but whether your tool is strong enough to hold someone who is looking for a streamlined way to run their business day-in and day-out.
With only a tiny fraction of global advertisers currently active on Telegram, there seems to be a significant “early adopter” advantage for those building within this ecosystem right now. How do you see this window of opportunity evolving over the next year?
We are currently looking at a massive gap between where the users are and where the advertising and software infrastructure is, with only 0.4 percent of global advertisers currently active on Telegram. This discrepancy creates a rare window of opportunity for teams building serious, defensible products to establish themselves before the distribution advantage begins to compress. The convergence of a habituated user base of one billion people and a format that removes traditional SaaS adoption friction is not something that happens frequently in the tech world. Over the next twelve to eighteen months, the teams that figure out how to scale infrastructure for media and business operations inside this messaging ecosystem will likely become very difficult to displace. They aren’t just building standalone apps; they are building the essential plumbing for how work is done in the next generation of the internet. Once a tool becomes deeply embedded into the daily operations of an organization or a media team, it transitions from being an “option” to being a necessity, and right now, that space is still wide open for those who understand the value of integration.
What is your forecast for the evolution of the AI SaaS market as it becomes more deeply integrated into messaging ecosystems?
My forecast is that we will see a massive consolidation of the “tool stack,” where the most successful AI companies will be those that prioritize integration and presence over sheer feature count. Over the next two years, the dominance of standalone “destination” web apps for common business tasks will likely wane, replaced by a “headless” software model where the AI exists exactly where the conversation happens. We will see the emergence of “Super-Mini-Apps” that handle everything from CRM and billing to creative production within a single chat interface, effectively making the traditional browser tab an obsolete intermediary for many workers. This shift will force a radical change in how we measure software success, moving away from simple page views or daily active users toward “percentage of workflow captured.” Ultimately, the winners in this space won’t be the ones with the most advanced LLMs, but the ones who successfully hide the complexity of AI behind a seamless, three-click experience that respects the user’s time and mental energy. The AI content space doesn’t need more tools; it needs a few, perfectly placed ones that feel like a natural extension of the way we already communicate.
