Essays from Dr. Vivek Gupta and the Softsensor team — the thinking behind a single source of truthful intelligence, from 2016 to today.
VibeCleaner turns plain-English intents into safe, auditable, reversible file actions on-device — with empirical findings on where current agentic AI breaks at scale.
Read →Real learning in startups, like the IIM Ahmedabad case method, comes from structured reflection on live, ambiguous problems rather than classrooms or textbooks.
Read →Drawing on ~15,000 applications a year, why startups expect you to apply skills you already have, learn independently, and demonstrate real initiative from day one.
Read →As enterprises run fleets of autonomous agents, the Single Source of Truth gives way to a Cognitive Mesh of decentralized consensus, continuous audit, and model accountability.
Read →Traditional analytics stops at the 'aha' moment; AgentSight closes the loop with governed agents that trace root cause, prescribe, and act, slashing reaction latency.
Read →Why naive prompting fails on structured data, and a 7-layer analytical agent architecture fusing semantic modeling, agentic AI, and governance for trustworthy answers.
Read →From custom-built AI to mass production — foundation models become the chassis, distillation and RL the toolkit, and CIOs the curators of a new model factory.
Read →As intelligence migrates from people to machines, rigid workflows give way to context-aware AI actors — reshaping leadership, truth management, and organizational DNA.
Read →AI personal shopping agents will reshape marketing, product transparency, engagement, pricing, analytics, and brand positioning — favoring data-rich, verifiable value.
Read →A practical multi-model governance playbook for CIOs and CDOs — governance boards, model registries, red-team stress tests, policy filters, audits, and risk monitoring.
Read →Design principles for AI shopping assistants that win on trust, transparency, and expertise — plus social proof, an architecture roadmap, and why it matters for retail.
Read →Combine a decade of RPA investment with AI agents to extend bot lifecycles, add intelligent oversight, and reach a sustainable, higher-ROI automation strategy faster.
Read →How Chief Data Officers can harness Generative AI to enrich data repositories, reimagine data workflows, and build deeper context across enterprise datasets.
Read →A framework for boards and CXOs to navigate GenAI: key truths, balancing data and AI investments, and a practical path forward on adoption.
Read →The 2020 essay that named the challenge: as intelligence migrates from humans to machines, CIOs must manage a single source of truthful intelligence — because intelligence is not factual like data.
Read →How multiple AI personas and cognitive synergy let a single LLM collaborate with itself, simulating an all-hands brainstorm to solve complex problems.
Read →As machine intelligence scales, the human brain may follow the path of human legs in transportation: vital for daily life, but no longer central to our goals.
Read →How LLMs can transform private-equity due diligence: data-room analytics, contract review, and risk management across financial, legal and market workstreams.
Read →As AI agents make consumer choices, 'Top of Mind Recall' may give way to 'Top of AI Recall', reshaping branding, trust, and marketing in an AI-driven market.
Read →The journey to Generative AI for PE portfolio companies still runs through a data-driven culture, a cloud warehouse, and a robust analytics foundation.
Read →The human brain is a marvel — and startlingly primitive next to AI that can be cloned in an instant. On our biological limits in the race against AI.
Read →India's courts carry a 35-million-case backlog. A case for rapidly adopting GPT-4 across legal research, drafting, e-discovery and access to justice.
Read →A framework for valuing crypto: treat each token as a uniquely identifiable, immutable plot of real estate in the metaverse, sold by different chain-builders.
Read →A practical executive framework for adopting AI: Data + Algorithm = Intelligence, and the three categories of AI applications.
Read →Why SDLC and Third Normal Form break down for data and analytics — build systems through a discovery ladder, not pre-defined requirements.
Read →What happens if there is a Shopify of banking? On how APIs and Banking-as-a-Platform attack the fundamental edifice of large retail banks.
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