Aparna Sinha

When One Engineer Is 1000x At Sequoia Capital, early product risk comes down to people. In this clip, Pat Grady and Alfred Lin, with Jack Altman, explain why one exceptional engineer can define a company’s DNA. ▫️ ServiceNow went public with most of its core code written by Fred Luddy ▫️ Airbnb was largely built early on by Nate airbn alone These weren’t 10x engineers. They were closer to 1000x. That early technical ownership shapes everything that follows. I broke this down in the Sequoia Playbook, including 10 systems most firms never had the patience to build, here: https://lnkd.in/e9ubxeyM Where have you seen one builder change everything? Do you agree?

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Fundamental opportunities worth solving for in AI right now: Predictability of reasoning, planning, execution of AI. Efficiency and ease of use of running AI workloads. Multiple system integrations - from DBs and APIs to infrastructure and biz systems. Trust, security, guardrails, authorization and auth. Business/science domain specific data, workflows, and optimization. AI cost understanding, control, and reduction.

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🔥 « 𝗟𝗟𝗠-𝗽𝗶𝗹𝗹𝗲𝗱 » — 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗔𝗜 𝘄𝗼𝗿𝗹𝗱 𝗶𝘀 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝘁𝗼 𝗳𝗲𝗲𝗹 𝗹𝗶𝗸𝗲 𝗮 𝗯𝗲𝗮𝗰𝗵 𝗽𝗮𝗿𝘁𝘆 𝘄𝗵𝗲𝗿𝗲 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗯𝗿𝗼𝘂𝗴𝗵𝘁 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗱𝗶𝗽. 😅 What Yann LeCun, one of the godfathers of AI himself, is really saying is that today’s generative AI and large language models are powerful tools but they’re not the finish line for intelligence — and frankly, treating them like they are is like expecting a Swiss Army knife to build a house. 🧰🏠 👉 The advantage is clear: LLM-driven AI already delivers massive value right now — automating workflows, turbo-charging productivity, and helping humans make better decisions faster. That’s not hype — that’s measurable impact. 👉 But the trap that LeCun warns us about is confusing what works today with what leads to real agent-level AI tomorrow — systems that understand context, reason, plan and interact with the physical world, not just generate text. So yes — the industry loves LLMs because they’re useful today. But if you only stack chips on bigger models and ignore the world-model, reasoning and planning pieces LeCun highlights, you risk building toy robots with PhDs — impressive on paper, but stuck when the rubber meets the real world. 🚗🤖 Bottom line: 💡 AI adoption gives huge competitive advantages now — faster insights, better automation, even new business models. ⚠️ But being “LLM-pilled” without deeper thinking is a trap — it’s like buying a race car and never learning to drive it. 🏎️ Let’s not confuse today’s utility with tomorrow’s autonomy. 👇 https://lnkd.in/eZ7cZqdD

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✨ Announced at #IDPCON2025. ✨ How engineering leaders can adopt AI with confidence. In their keynote at IDPCON, Cortex co-founders Anish Dhar and Ganesh Datta shared a new framework for building AI excellence into engineering organizations — a roadmap for adopting AI safely, systematically, and with measurable outcomes. In his follow-up, Ganesh breaks down how the most successful teams are building that foundation through four key pillars: 1️⃣ AI Readiness — adopting AI on a stable engineering foundation 2️⃣ AI Impact — proving ROI with real delivery and reliability metrics 3️⃣ AI Maturity — standardizing adoption across teams and repositories 4️⃣ AI Governance — turning AI from a black box into a managed business capability At the center of this framework is the Cortex MCP, connecting your AI assistants and IDEs directly to your engineering data — creating visibility, insight, and confidence in every decision. The next phase of engineering leadership isn’t just about adopting AI, it’s about doing it with excellence. Read Ganesh’s full post here 👇 → https://lnkd.in/gtmuQFDP

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Arvind Jain sees context graphs emerging from enterprise search and observability. I agree on the destination and disagree on the starting point. Arvind - a fellow IITD alum who's devoted his career to enterprise search - has built a tremendous business at Glean, and his "capture the how, learn the why over time" framing is sharp. But the foundation needs to be different: 1 - Search sits in the read path, not the write path. By the time activity data reaches the index, the decision context - why an exception was granted, what inputs were weighed, who approved - has already been flattened. 2 - Inferring intent from observed patterns is fundamentally different from capturing decision context in the operational flow. One reconstructs; the other records. 3 - The ~80% accuracy Arvind cites on task understanding is impressive, but when agents affect customers, contracts, and compliance, you need an authoritative record, not a probabilistic inference. (The math of compounding errors is brutal - I’ve argued for 99%+ accuracy in past editions of my Substack.) -- We believe orchestration-layer startups - where the context graph is built by actually executing work - are better positioned: 1 - They sit at the point of decision. When an agent triages an escalation or approves a discount, it pulls from multiple systems and applies policies in real time. That's the moment to capture the decision trace, not reconstruct it later from activity signals. 2 - They create decision traces as first-class artifacts. The orchestration layer captures the full picture: what inputs were gathered, what policies applied, what exceptions were granted, and what state existed at the moment of decision. That's not inferred; it's captured - which means enterprises can answer "why did we do that?" definitively. 3- They can learn across customers. Arvind notes enterprise data can't be aggregated for privacy reasons. But orchestration-layer startups focus on bounded workflows - which means they can refine ontologies within a customer and across deployments without ever sharing raw data. -- PlayerZero, Maximor, and Oliv are great examples of this. PlayerZero builds the context graph by automating L2/L3 support - sitting at the intersection where code, config, infrastructure, and customer behavior collide. Maximor AI captures decision lineage by orchestrating finance workflows where reconciliation logic and exceptions actually live. Oliv AI builds it for sales - starting as a co-pilot that performs specific tasks (update CRM, send follow-up) and capturing the decision traces along the way. There will be multiple context graphs within each org, and enterprise search can certainly be a starting point for one of them. Glean's approach creates value for knowledge retrieval and understanding how work flows. The question is whether the authoritative record of decisions will emerge from observability or from orchestration. We're betting on orchestration.

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