Shanu Mathew
@ShanuMathew93 • 25,799 subscribers
Electrotech, AI power/DCs, Sustainability, NBA, & Rap posts. PM public equities. Prior: HY credit, ibanking. All views expressed = personal. Not advice. DYOR.
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Stress-tested Perplexity Perplexity Finance Computer on a real equity research workflow: Map a representative AI infrastructure supply chain across 70+ companies across multiple tiers with sourced financials, bottleneck analysis, and company classifications. The kind of deliverable that might take a junior analyst days to weeks. It produced ~2,000 lines of structured research across 4 phased reports (~5,300 credits burned in ~75 minutes of work). Every data point labeled Actual, Guidance, or Estimate with inline source citations. Then it built an interactive dashboard to make the information digestible with 4 deep research .md files compiled. A cool feature is when it flagged when its own sources conflict. It caught 8 data conflicts and wrote explicit resolution notes. TSMC CoWoS capacity ranges explained as report timing differences. ASML's Q4 FCF anomaly identified as a billing artifact. Intel Foundry's loss methodology change across fiscal years. That is fundamentally different from silently picking a number and requiring an experienced SME to catch it. Other strong points: hyperscaler capex table aligned across 5 different fiscal year-ends. ORCL RPO correctly flagged as potential overstating near-term conversion (90%+ partner-funded). Power identified as the binding constraint with MSFT carrying $80B in power-constrained backlog. Some interesting analytical observations, not headline summaries. Where it fell short: never ran the numerical supply/demand gap calculation I specifically asked for (but it was a very large ask tbf). Spot checked multiple data points I know to be true but some ones that also looked off (to be expected, especially across varying degrees of company size and disclosure). Put ~30 companies into one Three Curves bucket (defeats the framework). Some debatable calls (but wouldn't expect AI to be good at this today). A few blog-tier sources where you'd want filings is always the shortcoming of a finance-focused search. Verdict: Arguably first-draft quality from a mid-tier sell-side initiation. 80% of the work done pretty well. The remaining 20% is exactly what you'd mark up before an experienced analyst or PM sees it. Not the finished product but a remarkable effort in a short amount of time. A research scaffold you can interrogate with actual domain expertise. This was a pretty genuinely cool result and I am intrigued to keep playing around with the tool. cc: Aravind Srinivas RYY Roshan
Shanu Mathew42,176 просмотров • 2 месяцев назад
Google's NotebookLM one shotted this! It did a really good explaining the study for a general, starter audience. Us power & energy folk should use this more to convey complex topics. I feel like this type of media could help educate political leaders on more nuanced topics.
Shanu Mathew65,421 просмотров • 6 месяцев назад
A highlight from the CSIS Energy Program's Electricity Supply Bottleneck on U.S. AI Dominance event hosted by Cy McGeady! We dug into a variety of topics ranging from the growing computational intensity of the economy, the sustainability and trajectory of datacenter capex, and how the economics and implications of a 'speed to power' race impacts multiple sectors including gas turbine backlogs, regulated utility interconnection queue timing, and the potential of co-located or BTM loads. See below and watch a replay of the discussion here:
Shanu Mathew22,335 просмотров • 1 год назад
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