🚨 The Cost of NOT Using o3: You're Burning $100k–$400k in Margins & Speed

With OpenAI’s o3 and o3-pro price cuts yesterday(80–87% drop), firms that don’t adopt LLMs for research, investor relations, and diligence automation will spend 6–10x more for the same outputs, or waste headcount doing what AI now handles in seconds.

👉 Not using o3 is now a seven-figure mistake over the life of a fund. That is some crazy sh*t.

If you’d like to learn how to embed o3 in all of your fund’s workflows and close $27,866 every day of the year, click here.

---

🔥 Head-to-Head: Doing It vs. Not Doing It

📊 Example: Mid-sized investment firm (20-person team, $2B fund, 15 LPs in motion)

🧨 Annualized:

• With o3: ~$12,000

• Without o3: $120,000–$180,000
  👉 Delta: $108k–$168k/year in avoidable OPEX

Now extrapolate that over a 5-year fund life. Yep, you're looking at $500k–$800k in margin leakage if you skip this.

---

📉 The Cost to LPs of You Staying Manual

✂️ Lower Fund Margins

• More $$ spent on humans and agencies = lower Net Management Fee margin

• Worse operating leverage, even at scale

🐢 Slower Diligence = Missed Opportunities

• LPs ask questions. You take 3 days to respond. Your competitor using o3 responds in 2 hours, with citations and visuals.

• LPs remember who helped them think faster.

💀 Shrinking Differentiation

• Fund managers talk about “value add.” But if your stack looks like it's from 2019, you're not impressing LPs with 2030 money expectations. Think about that.

---

🧠 Bottom Line: You Either Embrace This, or Fall Behind

If you’re not investing $1k/month into LLMs, now that OpenAI’s o3 is priced for scale—you are:

• Paying 10x more to do the same work manually or via contractors

• Losing deals and LPs to faster-moving peers

• Forfeiting operating leverage that could boost fund-level IRRs

There is no longer a price barrier. There’s only an execution barrier.