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Open Source Security in the Age of AI

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Machine Learning

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Judge a Transaction by the Company It Keeps — Somya Rai & MD Sharique, EXL

Judge a Transaction by the Company It Keeps — Somya Rai & MD Sharique, EXL

EXL developed an advanced fraud detection system that augments XGBoost with Graph Neural Network (GNN) embeddings. Traditional tabular models struggle to capture fraud signals residing in transaction neighborhoods. The system's innovative design includes meticulous sampling strategies, disjoint monthly graph construction for leakage control, and a GraphSAGE architecture that aggregates mean, max, and standard deviation of neighbor features to detect subtle fraud patterns. Emphasizing explainability for regulatory compliance and analyst trust, it also features a highly optimized serving architecture achieving 3ms P99 latency, pivoting to edge prediction to reduce cache footprint from 60GB to 2-3GB. This led to a 27-33% relative lift in true positive rates and a projected $65 million incremental fraud capture.

From 15% to 90% GPU Utilization: Fix the Data Pipeline, Not the Model

From 15% to 90% GPU Utilization: Fix the Data Pipeline, Not the Model

Tarun Sunkaraneni from Amazon AGI shows how a multimodal SFT pipeline that spent about 85% of its time waiting on data was brought to roughly 90% GPU utilization. He walks through concurrency with asyncio and Ray actors, prefetching, zero-copy transfer via Ray's object store, and a network-card bottleneck that appears only at scale, and argues that the hardest part of training throughput is often keeping GPUs fed rather than optimizing the model.

Small Models, Big Results: Training a Finance Agent for Under $500 — Charles Dickens, Snorkel AI

Small Models, Big Results: Training a Finance Agent for Under $500 — Charles Dickens, Snorkel AI

Snorkel AI and UC Berkeley's Sky Computing Lab show that a 4B Qwen3 model, trained with reinforcement learning for under $500 using the rLLM framework, beats its 235B sibling on expert-validated financial question answering built from SEC 10-K filings. The talk covers the FinQA dataset construction and verification, common failure modes of frontier models, transfer to multi-table and general tool-use tasks, and ablations showing that simple data and binary rewards outperform more elaborate setups, since the real bottleneck is disciplined tool use rather than reasoning depth.

Artificial Intelligence

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Open Source Security in the Age of AI

Open Source Security in the Age of AI

AI is accelerating the discovery of open-source vulnerabilities, but finding flaws is only the beginning. Remediating, testing, validating, and deploying fixes safely across critical infrastructure is where the real bottleneck lies, and securing open source at AI speed requires upstream contributions and coordinated efforts like Lightwell and Akrites.

Why a Tenured Professor Went Back to School for AI (with John Renne)

Why a Tenured Professor Went Back to School for AI (with John Renne)

John Renne, a tenured professor of real estate and urban planning at Tulane University, explains how returning to school for a master's in AI transformed his research on transit-oriented development, from analyzing 4,500 U.S. train stations to a global database of over 110,000, and how computer vision can now rate how attractive a street is. He also covers teaching vibe coding to urban planning undergraduates and making the case for AI-led oral exams over AI detectors.

Why Are We Still Governing AI Agents Like Software? - Tim Tyler, Gravitee

Why Are We Still Governing AI Agents Like Software? - Tim Tyler, Gravitee

Tim Tyler of Gravitee introduces the Agent Accountability framework, which treats AI agents as a digital workforce governed through three stages: Assignment (identity, team, and a named sponsor), Alignment (role, mission, and least-privilege authority), and Administration (oversight scorecards, guardian agents, and graduated recourse when agents drift off course).

Technology

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What will computer chips look like in 2045?

What will computer chips look like in 2045?

Intel's leadership walks through how advanced process nodes are brought to volume, from yield learning and the lessons of 10nm to the shift from planar transistors to gate-all-around, High-NA EUV, backside power, and CFET. The discussion widens to the chip-to-chip bottleneck that hybrid bonding targets, the supply limits on EUV tooling as AI compute demand grows, and the economics that make foundry scale decisive.

Former Intel CEO: Why This is the Best Time to Build Hardware

Former Intel CEO: Why This is the Best Time to Build Hardware

Pat Gelsinger joins a16z's Raghu Raghuram and Guido Appenzeller to discuss how AI is reshaping chip design, why design speed alone will not fix semiconductor bottlenecks, and why power and energy capacity may ultimately limit AI growth. The conversation covers HBM and memory innovation, stacking limits, the shift from copper to optics, consolidation among AI chip startups, and what a VMware for AI agents might look like.

Built Together: Inside the Dell + CoreWeave Engineering Partnership Powering Next-Gen AI

Built Together: Inside the Dell + CoreWeave Engineering Partnership Powering Next-Gen AI

A candid panel between Dell and CoreWeave leaders on co-engineering GB200, GB300, and Vera Rubin rack-scale AI platforms. The discussion covers why early access and stabilization matter in the AI arms race, how the industry moved from server-level to rack-as-computer architecture, and how joint development, standardized Redfish tooling, and shifting diagnostics left have enabled scale. It closes with where rack-scale infrastructure is heading toward pods, denser heterogeneous systems, and the need for automation in liquid cooling and power.

Recent Post

Should You Trust ChatGPT With Your Health?

Should You Trust ChatGPT With Your Health?

Karan Singhal, who leads health AI research at OpenAI, joins a16z's Jay Rughani to discuss how OpenAI is building trustworthy health intelligence into general-purpose frontier models. The conversation covers expert-driven evaluation and disagreement handling, the role of AI as a complement to doctors, integrations with Epic and clinician copilots, personal agents navigating fragmented healthcare, the distinction between medical information and medical care, and how falling costs of medical intelligence could compress the lag in clinical consensus and accelerate treatment discovery.

What Survives When AI Can Build Anything? (with Dr. Richard Socher)

What Survives When AI Can Build Anything? (with Dr. Richard Socher)

Richard Socher, CEO of Recursive and author of The Eureka Machine, makes the case for automating the scientific method with recursively self-improving AI. He covers how AI keeps replacing hand-crafted expertise with scale, the four pillars of his approach, why hallucinations can aid discovery, why RSI still rules out an overnight takeoff, and how science may become an engineering discipline across physics, biology, and economics.

Shipping AI Into 5 Major Banks Without a 12-Month Compliance Wait — Al Chen, Cisco

Shipping AI Into 5 Major Banks Without a 12-Month Compliance Wait — Al Chen, Cisco

Al Chen explains what it takes to get AI observability software through the security and compliance reviews of major banks. He covers build pipelines, agent-driven CVE remediation with verifiable exploit tests, ClickHouse backup and restore verification, PII and MNPI leakage through LLM judges, and the tradeoffs of zero leakage and data sovereignty, with an emphasis on showing evidence to shorten time to production.

Why AI Agents Will Need Their Own Bank Accounts — Lincoln Murr, Coinbase

Why AI Agents Will Need Their Own Bank Accounts — Lincoln Murr, Coinbase

Lincoln Murr, head of AI product at Coinbase, explains agentic finance, covering agents that pay and agents that grow money. He traces the path from AgentKit, an early experiment giving an AI a wallet, to x402, an open payment standard built on the long-unused HTTP 402 code. He also discusses microtransactions as a replacement for the ad economy, agents executing trades from simple instructions, the trust-building lessons from two years of development, and the vision of agents that can pay for their own upkeep.

Assisted Algorithms

Assisted Algorithms

Yuri Gurevich argues that algorithms are inherently deterministic and that probabilistic and quantum algorithms work by delegating non-algorithmic steps such as coin flips and quantum measurements to external oracles or assistants. He presents a theory of assisted algorithms with revised abstract state machine postulates, using a primality test and Grover's search as running examples.

How to Know When an AI Agent Can Work Unsupervised — Evan Kormos, Coinbase

How to Know When an AI Agent Can Work Unsupervised — Evan Kormos, Coinbase

Evan Kormos of Coinbase presents a practical framework for bringing AI agents into policy-driven, regulated workflows. He models processes as DAGs with loops inside each node, argues that a loop is only as trustworthy as its check, and shows how evals and traces create a new confidence mechanism. Agents then move through supervised, sampled, and exception-only stages.

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