Artificial intelligence is no longer limited to research labs and corporate backends. From social feeds to chat interfaces, it’s rapidly being incorporated into everyday consumer platforms, reshaping not only how technology works but also how culture is produced and distributed. The risks are particularly high in India, home to the world’s largest digital population and a rapidly maturing creator economy.
At AI Impact Summit 2026, a panel of leading creators and entrepreneurs revealed what this shift means for impact, scale, and competitive advantage. The discussion moderator is Village ShethCEOs of Monk Entertainment gather together prahar gupta (Host of The Prakhar Gupta Xperience), Entrepreneur, Creator Ishan Sharmatechnology-focused content creator Naman Deshmukh.
While much of the summit focused on technical architecture and model capabilities, this session, titled “From Average to Top 1%: How Creators Can Leverage AI to Achieve the Competition,” focused on the application of how AI is changing the economics and psychology of content creation.
AI’s first consumer touchpoint: Content
Mr. Gupta reframed the discussion early in the session, arguing that for most Indians, artificial intelligence will be experienced through content streams (short videos, social platforms, conversational interfaces) rather than through code repositories or enterprise dashboards.
In his view, India’s size as both a content consumer and a content producer remains underestimated. AI is lowering barriers related to geography, accent, and production budget, and long-standing structural filters are disappearing. Distribution forces are changing.
He described content as a form of soft power and suggested that with enough coordination and intention, Indian creators can dominate global communication channels on a large scale. In that sense, AI is less of a creative assistant and more of an empowering force that expands the reach of a story.
Reverse engineering algorithms
The conversation then turned to one of the most visible and controversial phenomena in short-form media: AI-generated influencer avatars, which include hyper-optimized virtual personas that garner massive engagement.
Sharma said he studied these forms rather than dismissing them. He adapts proven engagement frameworks to his own messaging by analyzing opening hooks, pacing, and structural patterns that promote retention.
His workflow extends beyond content creation. Automated systems analyze his past performances, track audience comments, monitor specific niche trends, and scan the broader cultural conversation. The result is a feedback loop that informs your day-to-day strategy.
He emphasized that AI should be treated as a collaborative system, not as a replacement for search engines. When connected across tools and processes, they begin to act like agents, providing contextual insights rather than individual outputs.
Automation as infrastructure
Deshmukh took the conversation further by arguing that superficial prompting has little lasting benefit. The advantage lies in the construction of the system.
He revealed that key parts of his content engine are automated, from research and scripting to optimization. In one example, he launched an AI-driven Instagram page that surpassed 1 million followers within a few months through a largely automated pipeline.
A custom-trained language model generates a script tailored to his tone. Avatar technology recreates his on-screen presence. Improve your delivery with audio cloning tools. An editor then assembles the final output. Preparations that previously required days can now be performed in a compressed production cycle.
The committee agreed that as models improve, access will become commoditized. Differentiation moves elsewhere.
New moat: Taste and judgment
Sharma and Deshmukh both converged on a central theme. In an age where anyone can generate text, images, video, or music, discernment becomes a scarce resource.
Pattern recognition, once a human advantage, is increasingly falling within the capabilities of AI. But it remains up to human judgment to decide which patterns to pursue, which narratives to amplify, and which audiences to serve.
Gupta extended this idea beyond content to cognition. He talked about assigning clear functional roles to AI systems and using them for research, ideation, and strategic frameworks. For him, AI has collapsed research schedules that once took days into minutes.
But he also warned about saturation. He predicted that over the next few years, a flood of homogenized, algorithmically optimized content, which he called the “grand middle,” could dominate feeds. As AI masters pattern-based dopamine triggers, the center of gravity in digital consumption is likely to become increasingly synthetic.
Paradoxically, it can increase the value of imperfection. In an environment of frictionless output, visible mistakes can indicate reliability. Rather than polishing, flaws can become markers of human existence.
scale and sustainability
Audience intervention broadened the framework and raised concerns about digital clutter, the environmental costs of expanding data infrastructure, and the lack of a robust governance framework. Although the panel did not engage deeply on regulation, the exchange highlighted tensions that are likely to intensify in the future: competition for scale and the need for sustainable systems.
Adoption also remains uneven. Sheth recalled a conversation he had with a ride-hailing driver who asked him what AI actually meant, reminding him that understanding is still in its infancy outside of the urban tech industry.
Deshmukh highlighted the importance of expanding AI literacy beyond tier 1 cities, citing efforts to train government school teachers on AI tools. Mr. Gupta pointed to India’s demographic advantages, English fluency, and skilled workforce as structural strengths in a more level global playing field.
Access is no longer an advantage
In summary, competitive advantage is no longer about having access to AI models. That privilege has been democratized. What separates creators, and by extension, brands and businesses, is the ability to execute. It’s the ability to design workflows, interpret signals, use flair, and build systems that grow in complexity over time. In the race to get from the median to the top 1%, AI is not the destination. It’s an amp.
