Dear friends, colleagues and mentors,
AI has changed what an investment team can cover and how quickly it can form a view. We can get up to speed on unfamiliar markets, connect evidence across a data room and pressure-test a business plan with a depth that previously required considerably more time.
These are meaningful gains. But early adoption is rarely the most efficient or sustainable. Last month, I wrote about becoming a “super investor.” This month, I want to consider how we make that practical and affordable.
Use the right tool for each job
You can ask AI to add up a column. Your calculator already does it reliably, at negligible cost.
The same logic applies throughout investment analysis. Data engineering collects and organizes leasing evidence. Statistical methods measure trends and compare buildings. AI helps interpret conflicting signals, connect local developments and test whether the evidence supports the thesis. Investment professionals decide what to believe and how to act.
Hand the whole assignment to AI and you may pay repeatedly for work that simpler tools handle more efficiently. Divide the work well and the analysis can become more reliable and economical at the same time.
Make the economics work
Take an analyst, an associate, and a senior associate who each spend 100 hours bringing a deal to preliminary IC. On our assumptions, the salary and bonus attributable to that work total roughly $40,000.
Now add $20,000 of AI usage.* That is another 50% on top of the team’s compensation, before software fees and third-party data licenses.
It can be money well spent. Catching a material risk, sharpening a business plan or reaching a sound “no” sooner could be worth far more. However, we should understand where that value comes from and design the work to deliver it consistently and cost-efficiently, as many deals never close.
Why we are building Reml
At Reml, we want to expand what real estate investors can understand and achieve through purpose-built augmented intelligence.
That means combining data engineering, bottom-up research, rigorous financial and statistical analysis, AI reasoning and professional experience. Our market research builds from evidence on individual properties and local conditions rather than relying on third-party databases.
Shared evidence and preserved assumptions help each round of analysis build on what the team already knows. The aim is deeper investment intelligence with less repeated effort and a clearer relationship between cost and value.
Our one-minute film introduces that approach.
AI should be a powerful tool in everyday investment practice. Thoughtful design can make it effective and affordable enough to use across the business, rather than a luxury reserved for selected deals.
Yours Curiously,
