AI infrastructure performance increasingly depends on designing processors and memory together from day one. At the Six Five Summit, @MicronTech's Sumit Sadana tells @PatrickMoorhead why customers are engaging on R&D roadmaps 5–7 years ahead, with power, model size, and performance all at stake. The shift? An ASIC-like model where custom memory design becomes a competitive differentiator. $MU

AI scale-across is changing the economics of data center connectivity. With bandwidth demand ~15x traditional DCI, @MarvellTech's Russ Esmacher tells @danielnewmanUV why 1.6T is accelerating, 3.2T is next, and power and cost per bit matter more than ever. $MRVL

AI-related borrowing is skyrocketing. US Dollar-denominated AI-related corporate bond issuance is up to $419 billion so far in 2026, the highest annual figure on record. This represents a +90% YoY increase from full-year 2025 and a +437% increase from full-year 2024. Year-to-date, Alphabet, $GOOGL, Amazon, $AMZN, Meta, $META, Microsoft, $MSFT, and Oracle, $ORCL, have issued a combined $157 billion in corporate bonds, accounting for 37% of the total. This is followed by investment-grade and high-yield data center and computing companies, which have raised $66 billion and $37 billion, respectively, and together account for ~25% of AI-related issuance. Other AI-linked corporate bonds have represented $159 billion, or 38%. The AI revolution is driving an unprecedented wave of corporate borrowing.

AI agents can reason over enormous amounts of information, but much of the world's actual work is still trapped in PDFs, spreadsheets, scans, and other formats that models struggle to reliably understand. Reducto co-founder and CEO Adit Abraham argues that solving this document layer is essential if agents are going to take on consequential work in healthcare, legal, finance, and insurance. Adit joins a16z General Partner Jennifer Li to explain why traditional OCR breaks down on messy real-world documents, how Reducto combines vision-language models with specialized infrastructure to reach high accuracy without sacrificing cost or latency, and why frontier models can silently drop information on long extraction tasks. Reducto now processes billions of pages each month, and crossed its 7 billionth page the morning of this recording. 0:00 Why AI agents need to read PDFs 2:12 From 1990s OCR to vision models 5:00 Training R1 on the messiest docs 7:46 When agents beat humans at extraction 9:45 A penny a page for 7 billion pages 12:47 Why legal and finance lag coding AI 15:01 The stakes of AI errors in healthcare 16:25 Agents that can edit Word and Excel 17:58 Why the labs won't go deep on PDFs 20:14 Frontier models silently drop data 22:21 Benchmarks have become marketing 25:45 Why charts and Excel stump models 28:46 Infra for 10M documents at once 31:03 How "chet pain" gets claims denied 34:32 The paperwork AI should kill first YouTube: @aditabrm @reductoai @JenniferHli

AI can be civilization-defining and still be overpriced. 🫧 On Bits + Bips, @Nick_van_Eck argues frontier labs like Anthropic have no moat, while @coinbase's John D'Agostino calls AI "a massively productive bubble." With @austincampbell and @perkinscr97 🎧