In this episode of Terminal Value, I'm joined by Cy Khormaee, founder and CEO of Aegis AI, an AI-native email security company that recently raised a $36 million Series A led by Battery Ventures, to discuss why email security is becoming an AI-versus-AI problem, how personalized threats are bypassing legacy rules, and what changes when AI begins handling the intelligence layer of cybersecurity.

We cover how email security evolved from reputation-based appliances to machine learning and rules, why LLM-based reasoning can investigate each message in context, how Aegis uses specialized multi-agent models instead of frontier APIs, why proprietary email data creates a compounding advantage, how automated incident response can shrink exposure from hours to milliseconds, what security buyers need to trust autonomous systems, why AI-native architecture can challenge incumbents, how the category could expand from protecting the corporate inbox to protecting the person, and why Cy hires for slope in an uncertain future.

0:00 Hook: Why email security is becoming AI versus AI
0:40 Intro to Cy Khormaee and Aegis AI
1:45 How email security evolved from reputation and rules to AI
4:55 How AI is making phishing attacks more personalized and harder to detect
9:35 How Aegis uses multi-agent reasoning to stop email threats
12:50 Why rule-based security breaks as threats change faster
15:45 How proprietary email data creates a compounding AI advantage
18:00 How automated response changes accountability and earns trust
25:10 Why Aegis builds specialized models instead of relying on frontier models
31:45 How AI-native architecture can overcome incumbent data advantages
39:25 From securing email to protecting the person
41:15 Why Cy hires for slope in an uncertain AI future
42:50 Closing takeaways

In this episode of Terminal Value, I'm joined by Dylan Robbins, founder and CEO of Lucra, which recently raised $20 million led by ARK Invest Venture Fund to build competitive loyalty infrastructure, to discuss why traditional points programs often struggle to drive adoption, how games and immediate rewards can make loyalty feel worth using, and why the real moat sits in the payments, identity, fraud, and compliance stack underneath the experience.

We cover Lucra's shift from a consumer app to an embedded B2B platform, how the company sells to venues and proves payback, why customers that try to build the stack themselves often come back, how Crown brings mobile games into the days between venue visits, how Bracketology extends the model into reality TV, what loyalty could look like when rewards are narrower and immediate, and why Dylan thinks the AI funding boom may be crowding out other kinds of innovation.

0:00 Hook: Why building loyalty in-house costs more than brands expect
0:45 Intro to Dylan Robbins and Lucra
1:43 Why traditional loyalty programs struggle to drive adoption
4:17 How Lucra replaces points with immediate rewards
6:23 From customer goals to integration, payback, and ROI
11:20 Market size, pricing, and the path to self-serve integration
15:22 Lucra’s three growth pillars: IRL, mobile games, and reality TV
17:26 Why brands eventually buy instead of build
21:12 How Crown connects physical venues to mobile games
25:27 How Bracketology expands Lucra into reality TV
30:09 What loyalty could look like in five years
31:54 Why the AI funding boom may be hurting startup innovation
34:05 Closing takeaways

In this episode of Terminal Value, I'm joined by Viral Shah, co-founder and CEO of JuliaHub, the company behind Dyad — a physics compiler paired with AI agents for engineering and simulation — to discuss why physical product engineering has been so hard to automate, what an AI agent actually needs to model the real world, and whether hardware development can start to look much more like software.

We cover the "engineering V" and why every step runs in its own siloed tool, why the real TAM is the trillions spent designing products rather than the billions spent on simulation software, how Dyad works as a compiler that fuses disparate physics into one simulation, why calibration — not modeling — is the hard part, how Instron cut crash-test-rig calibration from over a day to under a minute, why trusted reusable model libraries make hardware feel like software (text, Git, CI/CD, agents), how agent pricing shifts from per-seat licenses to token consumption, why the harness can matter more than the frontier model, why engineering shows high expected but low observed AI impact today, and why Viral believes hardware engineering is going to become like writing software.

0:00 Hook: Why hardware engineering could become like software
0:35 Intro to Viral Shah, JuliaHub, and Dyad
1:25 How engineers move from product requirements to validated designs
4:41 Why engineering software is fragmented across specialized tools
12:16 Where Dyad fits and why JuliaHub built a physics compiler
19:17 How real-world data calibrates simulation models
23:21 Building trusted models that can be reused like software libraries
26:17 Where Dyad is being used across aerospace, semiconductors, and infrastructure
29:10 How agentic engineering changes seat-based software pricing
31:37 Why the agent harness can matter more than the frontier model
35:44 Why AI adoption in engineering still lags software
38:13 Why Viral Shah believes hardware development will become more like software
41:04 Closing takeaways on Dyad and the shift toward computational engineering

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.