BOTTOM LINE UPFRONT
In this episode of AI & PE: The Future of Value Creation, Kyle Roemer sits down with Adam Silverman to discuss the structural shifts AI is driving across software development, investment research, and private equity workflows. The message is clear: AI is no longer experimental. It’s becoming core infrastructure.
1. AI-native software development compresses time and cost
Software economics have fundamentally changed.
Coding agents like Claude Code, Codex, and Cursor are dramatically shrinking build timelines. What once required one to two years can now move from prototype to production in months.
What’s changing:
- Engineers shifting from writing code to architecting and quality-controlling AI output
- Prototypes built in days instead of quarters
- Senior technical talent focused on system design rather than low-level tasks
The result is exponential productivity per engineer. Firms that aren’t embedding these tools into development workflows risk overpaying for slower output, internally and with external partners.
2. AI usage shifts from reactive assistant to proactive operator.
The next wave isn’t better chat responses, but ambient AI running continuously in the background.
Instead of manually prompting tools, ambient agents monitor news, data rooms, earnings releases, research feeds, and portfolio performance in real time – updating insights automatically.
We’re beginning to see:
- Deal materials refined as new documents enter data rooms
- Market events feeding directly into financial models
- Historical opportunities resurfacing when new catalysts emerge
- Daily research briefings generated without manual kickoff
For lean investment teams, this creates structural leverage, with small teams competing with the analytical coverage of much larger firms.
3. Data accessibility determines advantage.
As foundational models become widely accessible, differentiation moves upstream.
Everyone will have powerful models. Not everyone will have clean, structured, and proprietary historical data.
Partnerships like Perplexity and Blue Matrix point toward institutional-grade AI research grounded in high-quality datasets. But the real advantage sits inside firms:
- Years of diligence memos
- Portfolio performance history
- IC discussions
- Operational KPIs
Firms that capture, clean, and integrate this data today will compound advantage as models improve. Those that don’t, will struggle to extract value from the information they already own.
The takeaway:
The gap between firms that are leaning into what’s cutting-edge in AI and firms that are waiting is starting to widen.
Teams using these tools practically and as part of their operating model are moving faster: they’re building internal software in months instead of years, tracking markets without adding headcount, and turning historical data into something actionable.
For firms delaying AI implementation: act now and build AI into your infrastructure, or risk falling further behind.
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