Try for free
Further Comments
Further Comments

Too Much Money Is Not Enough

29 May 2026 55:25 Damien Riehl & Horace Wu

Listen to episode

About this episode

Send us Fan Mail

Horace and Damien record an unedited and unfiltered episode, opening with Horace’s stressful overnight apartment move in New York while planning a two-month summer trip to Australia. 

We discuss legal tech execution versus announcements, discussing a link about Kirkland & Ellis’s reported $500 million AI commitment and whether it reflects real technology spend or largely lawyer hours, while noting firms and Fortune 100 legal departments are building internal AI teams. They explore AI raising the floor and ceiling of legal work, GPU scarcity, consumption/token pricing versus seat pricing, and proposals like taxing tokens. They discuss the Pope’s AI-focused writing as a warning about tools that can help or diminish humanity, then cover market shifts including Claude for Legal, Microsoft’s legal Word add-in, open source momentum, and agentic “law firm” projects like Lavern AI, emphasizing that data, KM, and practical execution matter more than hype.

00:00 Welcome and Moving Trauma
03:42 Kirkland AI Spend Debate
09:12 AI Floor vs Ceiling
12:00 Universal Basic Services
13:44 Nvidia Bubble and Pricing
16:54 Taxing Tokens and Models
18:21 Pope on AI and Humanity
21:45 Legal Tech Market Check
22:20 Open Source Vibe Coding
23:31 Quality Security and Stitching
25:21 Linux and Court Tools
26:42 Open Source Custom UX
27:38 Users Don’t Know Yet
28:59 Designers vs Atoms
31:12 Agentic Legal Open Source
32:25 Humans Still Sell
34:41 Jevons and GPU Scarcity
38:11 Symbolic AI Saves Tokens
42:10 Three Paths to Efficiency
46:17 AI Literacy Dunning Kruger
49:06 Precision Recall and KM Power
52:28 Royalties for Know How

More AI podcast episodes

Browse all →

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 Further Comments. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.

Still have questions?

Contact Support