There is a moment every founder building an AI tool eventually hits. The product works. The demos look great. Users say the right things during onboarding. And then retention quietly falls apart, because the tool, however good it is, exists somewhere outside the actual flow of how people work. They have to go to it. And most of the time, they just do not.
That moment of reckoning is reshaping where the smartest AI SaaS products are being built right now. And increasingly, the answer is inside Telegram.
Telegram is not a messaging app anymore
It is worth saying this plainly because the perception has not caught up with reality. Most people still think of Telegram as a messaging platform with some bots. What it has actually become, for a growing number of digital businesses, is something closer to a cloud-based operating system.
Telegram now has one billion monthly active users, with 500 million active every single day. That alone is remarkable. But the more interesting number is behavioral. Active users open the app at least 21 times a day, with average sessions lasting 41 minutes. This is not a passive presence. This is a platform people are living inside.
Business channels on Telegram with over 10,000 subscribers grew by 39% in 2025. Platforms like Tribute and Blum are now running full scale fintech and B2B operations entirely within the messenger, with no standalone website, no native app download required. CRM dashboards, crypto trading terminals, subscription tools, all embedded directly into the chat interface. The infrastructure was always there. What changed is that serious founders started taking it seriously.
The shift from Telegram as a communication tool to Telegram as a product deployment environment is one of the more significant and underreported developments in enterprise software right now. The teams recognizing this early have a distribution advantage that is genuinely difficult to manufacture from scratch on any other platform.
The problem that AI tools keep refusing to solve
To understand why this matters, spend a day inside the actual workflow of a content team or marketing manager in 2025.
A typical content production cycle looks something like this. Copy gets drafted in one AI tool. That copy gets moved manually into a design platform to create visuals. The layout gets adjusted somewhere else. The final asset gets uploaded into a scheduling tool. Analytics get reviewed in a separate dashboard. Each of these platforms has its own login, its own interface, its own mental overhead. And none of them talk to each other in any way that actually saves time.
A 2022 study published by Harvard Business Review found that the average digital worker toggles between applications and websites nearly 1,200 times per day, spending almost four hours per week simply reorienting themselves after switching tools. Over a full year, that adds up to roughly five working weeks lost to context switching alone, around nine percent of total annual working time.
Nine percent. That is not a productivity footnote. That is a structural failure hiding in plain sight.
In response to a 2023 survey by project management software provider Wrike, 58 percent of knowledge workers said they would like to use fewer apps at work, with researchers describing workers as “crying out for clear, streamlined, and consolidated processes.” Tech Brew
And yet most AI tools have responded to this problem by adding more features, not by reducing the number of places people have to go.
The core issue is not that these tools produce bad outputs. Most of them are genuinely capable. The problem is architectural. They were built as destinations, not infrastructure. And destinations require users to travel to them, which means every time someone needs to use one, they are already leaving somewhere else. That constant movement is exactly where the productivity gains bleed out.
From content generation to total workflow automation
There is a distinction that tends to get lost in how AI tools are marketed, and it matters more than most product conversations acknowledge.
Most of what exists today is generation assistance. You prompt the tool, you get an output, you take that output somewhere else and do something with it. That is useful. It is also only the first step of a much longer operational chain, and it leaves everything else entirely on the user to manage manually.
The more meaningful opportunity is not the better generation. It is automation of the entire lifecycle.
This is the philosophy behind @morty, a Telegram Mini App built by entrepreneur and product development specialist @maxim, whose background spans growth marketing, large scale traffic acquisition, and building viral mechanics for AI services. @morty is designed to automate end to end content management for Telegram channel operators, covering trend monitoring, ideation, draft generation, visual asset creation, scheduling, distribution, and post analytics, all without leaving the platform where the channels already live.
“The problem we kept seeing was that people were using five or six tools to do one job,” Shibun says. “They were generating content in one place, editing in another, scheduling somewhere else, checking analytics in a fourth. Every handoff between those tools was a point where time got lost and context got dropped. We wanted to build something where none of those handoffs exist.”
The traditional SaaS onboarding funnel compounds this problem. Email verification, password creation, payment redirects, API setup, all of it creates drop off before a user ever reaches the moment of actual value. Telegram Mini Apps address this by compressing onboarding friction dramatically, with user acquisition costs running between two cents and fifty cents per user, and click through rates running ten to forty times the industry standard for comparable products on traditional mobile networks. Using native Telegram authentication, a product like @morty can onboard a new user and deliver a production ready asset in under three clicks.
Most founders building AI tools are still thinking about the product in isolation. They optimize the output quality, which matters, but they are not asking the harder question: where does this tool actually live in the user’s day? If the answer is a separate tab the user has to consciously navigate to, then no matter how good the output is, you are fighting the user’s existing habits every single time. Embedding inside Telegram means the tool lives where the work already happens. That changes the adoption dynamic completely.
The economics actually make sense now
The business case for workflow automation tools used to require a lot of faith. You had to believe the time savings would materialize, that adoption would stick, that the hidden costs of switching would not outweigh the gains. That uncertainty has largely been resolved by the data.
Marketing automation ROI averages 544 percent over three years, meaning companies earn roughly five dollars and forty four cents for every dollar invested. 76 percent of companies see positive ROI within the first year, with most recouping their investment in under six months.
According to a 2024 McKinsey report, companies leveraging AI in marketing see 20 to 30 percent higher ROI on campaigns compared to those relying on traditional methods.
For smaller media teams and solo channel operators, the arithmetic is even more direct. A content team managing several active Telegram channels typically requires copywriters, a designer, someone handling scheduling and distribution, and someone reviewing analytics. That combination of roles carries significant monthly costs before overhead is even factored in.
“The ROI conversation with businesses becomes straightforward once you frame it correctly,” Shibun says. “It is not about replacing people. It is about what those people do with their time. If a content manager is spending sixty percent of their week on production tasks that can be automated, and you free that time up, the question becomes what they do with it instead. Usually the answer is better strategy, more creative work, faster iteration. That is where the real business value sits, not in the cost saving on its own.”
The unit economics of content production have not changed dramatically despite the proliferation of AI tools. The reason is that most of those tools reduced the cost of one step while leaving the surrounding steps untouched. True cost restructuring requires addressing the whole process. Products that can credibly claim end to end automation are operating in a fundamentally different commercial conversation than ones offering faster copywriting.
Early traction is not the same as product-market fit
That said, and this is worth being honest about, early traction in this kind of environment can be deceptive.
The Telegram Mini App ecosystem went through exactly this dynamic in 2024. After peaking at 1.44 billion monthly active users in September 2024, largely driven by tap to earn gaming apps, Mini App activity declined and stabilized at a lower but far more consistent range of 150 to 190 million by mid 2025. What remained after the hype faded was a smaller but meaningfully more stable audience using Mini Apps for practical purposes. The easy phase produced inflated numbers. The real phase produced real users.
The same dynamic plays out at the product level. The first users of any well positioned AI tool tend to be motivated, technically comfortable, and forgiving of rough edges. They convert quickly. They give enthusiastic feedback. It feels like product market fit. Sometimes it is. But the harder question is what happens when the product reaches people with lower patience and less tolerance for anything in the workflow that does not run smoothly.
“The launch phase is actually the most dangerous moment for a product like this,” Shibun acknowledges. “Everything feels like it is working. Users are engaged, feedback is positive, the metrics look good. The real test is whether the product is strong enough to hold people who did not come in already convinced. That is the version of the product we are building toward.”
@morty is currently in its release stage and has already begun generating its first revenue, with further monetization scaling expected to follow as the user base expands. Early retention metrics suggest the consolidation argument is resonating beyond the initial novelty phase, which is often where Telegram native products lose momentum.
The window does not stay open forever
Only 0.4 percent of global advertisers are currently active on Telegram. That number is going to change. The question is whether the teams building serious products now use this window to establish something defensible, or whether they arrive late to find the distribution advantage has already compressed.
The broader point for investors and founders evaluating the TON and Telegram ecosystem is not primarily about platform mechanics. It is about the convergence of a massive, already habituated user base with a format that removes almost all of the traditional friction in SaaS adoption. That combination does not come along often.
“What we are building is not just a content tool,” Shibun says. “It is the infrastructure for how media operations will run inside messaging ecosystems going forward. The teams that figure out how to make that work at scale over the next twelve to eighteen months are not just building good SaaS products. They are building something that becomes genuinely hard to displace once it is embedded into how an organization operates. That is the opportunity. And right now, most of it is still wide open.”
The AI content space does not need more tools. It probably needs fewer, better integrated ones built in the places where people already work. That is the bet worth watching.
