Latest news with #smallest.ai
Time of India
08-07-2025
- Business
- Time of India
Rs 1 crore CTC with just 4-year work experience: Bengaluru job offer for techies goes viral. CV not needed
is making waves in the startup world with a bold offer: Rs 1 crore CTC for a hands-on full-stack lead, even if they have just 4–5 years of experience. The role offers a base salary of Rs 60 LPA and Rs 40 LPA in ESOPs, with immediate joining at their Indiranagar office in Bangalore. The job offer was posted by Sudarshan Kamath, founder of on social media platform X. This isn't a managerial role— is looking for a hardcore builder who's worked on scaling systems from zero to high-traffic environments. Preferred tech skills include Python, and The team works from office five days a week with some flexibility. Interestingly, traditional filters like college degrees and resumes don't matter here, the job posting said. The focus is purely on real-world impact and technical depth. Applicants simply need to send a 100-word pitch about themselves, along with links to their best work, using the subject line "Cracked Full Stack Lead" to info@ — kamath_sutra (@kamath_sutra) Who is Sudarshan Kamat? Sudarshan Kamat is the founder of the AI startup He is an alumnus of IIT Guwahati. He dropped out midway from a Master Of Science degree at the UC San Diego to pursue entrepreneurship. Before floating he worked as lead data scientist at Bosch and lead product manager at Vakil Search.
Forbes
28-04-2025
- Business
- Forbes
How Business Leaders Are Unlocking AI's Full Potential
Historically, the development of artificial intelligence (AI) was controlled by a small circle of pioneering research institutions—those with the financial muscle and computational power to experiment at a scale most could not imagine. Future growth with AI Powered innovation, business growth concept. AI adoption for business. Hand ... More interacts with digital interface displaying growth, innovation and AI investment, productivity icons. Yet the landscape is rapidly changing. Companies are projected to spend over $1 trillion on AI initiatives in the coming years, according to Goldman Sachs. But the question remains: Is scale alone the answer? As access to AI technology expands, so too does the opportunity for transformative, decentralized innovation. However, for small, mid-sized, and even enterprise organizations alike, one thing is becoming increasingly clear: those who leverage structured systems, integrated platforms, and disciplined software strategies will be the ones who maximize AI's true potential. In a world where the prevailing narrative equates bigger models with better outcomes, a quiet revolution is unfolding. "Artificial intelligence does not need bigness to have profound impact," argue Sudarshan Kamath and Akshat Mandloi, fellow tech founders of Their conviction is simple yet disruptive: innovation thrives not through brute computational force, but through smarter, more efficient design. In a mere five months, their startup outpaced billion-dollar competitors in third-party text-to-speech (TTS) metrics—while consuming only a fraction of the resources traditionally deemed necessary. Impressive to say the least. But also validates the size of this market. Their success underscores a profound shift: true AI advantage now lies in precision, efficiency, and flexibility—not in reckless scale. The new frontier of AI demands more than just creativity. It demands structure. Without disciplined systems to organize data, prioritize initiatives, govern decision-making, and scale learnings, even the most promising AI initiatives risk becoming fragmented experiments that never achieve real impact. Small and medium-sized businesses cannot simply throw capital at AI projects; they must out-think, out-structure, and out-adapt. Even enterprises, with more resources at their disposal, are quickly realizing that 'platformization'—the use of connected, adaptive software systems that integrate data, workflows, and intelligence—is the only way to move AI adoption from isolated use cases to enterprise-wide transformation. AI innovation, democratized or otherwise, must live within structured ecosystems that enable repeatability, visibility, and continuous improvement. For decades, innovation tools were gated—reserved for the elite R&D labs of global corporations or those backed by heavy venture capital. Today, cloud computing, open-source technologies, and the rise of platforms like EXCELR8, and others are tearing those walls down. Tools that once required millions now require thousands. Knowledge that once required a graduate degree from MIT is now available to anyone with a Wi-Fi connection and the will to learn. Yet access alone is insufficient. Without scalable frameworks—structured systems for integrating AI insights into daily workflows, decision-making, and strategic execution—organizations risk democratizing noise rather than democratizing progress. As Harvard Business Review aptly notes, "Generative AI can support divergent thinking by producing associations among remote concepts." But without operational systems in place, those associations are fleeting at best, lost at worst. Strategic leadership today demands a delicate balance: encouraging experimentation while insisting on disciplined operational models. As technology leader Tom Berger explains, "IT leadership must provide a governance framework with at least some level of standardization, while also empowering users to be brave and experiment." Without such guardrails, well-meaning AI initiatives quickly fall prey to chaos and inconsistency. In this new era, forward-thinking leaders understand that true innovation is not unbounded; it thrives within intelligent, structured systems that amplify human creativity while preserving focus, discipline, and alignment. As Kamath wisely notes, "We encourage users to experiment, but structure is what allows that experimentation to lead somewhere meaningful." Democratizing AI innovation is not merely a moral imperative—it is a strategic one. The greatest breakthroughs often emerge from unexpected places. But to capture those breakthroughs—to scale them, replicate them, and learn from them—organizations must invest not just in talent, but in platforms, software, and operational systems built for intelligent growth. Small startups. Mid-market challengers. Global enterprises. The playing field is being leveled—but the winners will be those who build or leverage structured ecosystems for agility, insight, and continuous learning. In the end, AI's promise will not be fulfilled by sheer computational horsepower. It will be fulfilled by those who understand a timeless truth: Structure doesn't stifle innovation. It sustains it.
