Short-Term Wins are still Wins!
The Hidden Alpha: Reasoning Models + Great Prompts
Last week we went into a deep dive about how to create long-term strategic advantage with commercial real estate. This week we’re going to look at short term tactical advantages.
As readers of this newsletter you probably are aware that OpenAI released its long-awaited upgrade to ChatGPT - Version 5, last week. And the incredible hullabaloo it created. People were expecting the moon and when the undeliverable was not delivered they got very animated. At times you’d think the world was about to end.
The advantage is bigger than we thought.
There was though one short tweet (X if you really must) that Sam Altman put out that contained some extraordinary, and important, information:
“The percentage of users using reasoning models each day is significantly increasing; for example, for free users we went from <1% to 7%, and for plus users from 7% to 24%.”
I was not surprised that almost no free users were ever using the ‘reasoning’ models but rather staggered that only 7% of ‘Plus’ paid users were. These models, notably o3, were so much more powerful than the default model, 4o, yet were going almost unused.
The biggest thing about GPT-5 is that for the first time hundreds of millions of people will have access to frontier reasoning models and, at last, will be able to see what these things can do! This IS a big deal.Paid users, though, had access, but 93% were not using them. And perhaps this will remain the case for a majority at least.
So it seems likely that the gap between capability and day-to-day utilisation remains huge—and that gap is exactly where your personal and organisational alpha sits.
Most teams still use AI like search. They dabble in chat, copy/paste the output, and hope for the best. Which is such a waste because the edge comes from using reasoning-capable models with excellent prompting discipline, applied to high-leverage work. This is where GPT-5 shines—and where a small set of patterns will yield outsized results.
For now, this is a huge short-term win for you, your team, and your company—if you act. Below is some practical advice about how to make the most of this ‘capability gap’.
From “using AI” to “using it well”
I use a simple stack to explain why prompt quality matters:
WHO / WHAT / HOW
- WHO: Do you want the AI to act as? Adding context is important. Be clear who you want to be interacting with.
- WHAT: Do you want the AI to do? Think clarity, context and constraints. LLMs cannot read your mind - you need to tell them what you want.
- HOW: Do you want the output to be returned? Length, format, structure. How do you want information presented?
These fundamentals turn a vague ask into a brief. They’re the front door to all good prompting.
The Importance of Patterns
And on these foundations you overlay certain ‘Patterns’ - types of prompt addressing particular circumstances.
Examples of these patterns include ‘Cognitive Verifier’, ‘Self-Refine’, ‘Self-Consistency’, ‘Flipped Interaction’, or ‘Outline-to-Synthesis’. These are reusable “moves” that force better thinking.
You then add ‘Verification’. Ask for a plan first, then a critique of that plan, then a final answer. This dramatically improves accuracy and defensibility for board-facing work.
And ‘Evidence handling’. For research, require citations, uncertainty labels, and explicit counter-arguments.
And finally, a ‘Deliverable spec’. Decide the end product upfront: one-pager, IC memo, risk log, table, or slide deck. The model writes to the format you define.
When you combine these habits in GPT-5, the model stops being a clever autocomplete and becomes a co-pilot for judgement work. The difference really is chalk and cheese, and normally amazes people when they first see it all in action. Expect this to go mainstream in the coming months.
All things
#SpaceasaService
Exploring how AI and technology are reshaping real estate and cities to serve the future of work, rest, and play.
Cohort 12 starts 5 September #GenerativeAIforRealEstatePeople
The only AI course built by CRE pros, for CRE pros—from leasing agents and asset managers to workplace strategists.
In 3 weeks you’ll:
- Break down 20+ live case studies across the built environment
- Get hands-on with ChatGPT, Claude, Gemini, Perplexity & Midjourney
- Plug in 20 real-estate prompt frameworks to speed leasing, underwriting, ops & placemaking
- Leave with a playbook to spot high-ROI use cases and drive adoption on your team
“A must-take for anyone serious about AI in real estate.” — Prof. Suleiman Alhadidi, Vanderbilt University
“Insightful and truly engaging.” - Satbir Bassra, Senior Product manager, British Land, UK
Five GPT-5 patterns that change outcomes
Let me walk you through five GPT-5 patterns that change outcomes (with CRE use-cases).
1) Cognitive Verifier — Plan → Critique → Answer
Where to use it: IC memos, board papers, credit submissions, asset strategies.
Why it works: It reduces blind spots and shortens review loops by building a self-check into the workflow.
Example prompt: “Act as a senior investment committee adviser. First outline your plan to evaluate [insert asset/market information - more is better]. Pause. Critique your plan for missing assumptions, weak evidence and risks. Only then produce a one-page recommendation with mitigations.”
2) Self-Refine — Draft → Self-Review → Redraft
Where to use it: proposals, quarterly packs, client comms, investor letters.
Why it works: Quality rises across controlled iterations without human micro-management.
Example prompt: “Draft the [insert required document details - again more is better]. Self-review against these criteria: accuracy, clarity, evidence, senior readability. Produce a revision plan, then redraft and list the top five changes and why.”
3) Self-Consistency — Multiple paths → Converge on best
Where to use it: valuation sensitivities, strategy options, scenario planning.
Why it works: Running independent reasoning paths reduces idiosyncratic error; you see consensus and dissent clearly.
Example prompt: “Run three independent analyses of [insert deal details]. Use different assumptions/paths. [you could define this if you wish]. Show consensus, the strongest dissent, and a recommendation with what evidence would overturn it.”
4) Flipped Interaction — Socratic stress-test
Where to use it: lease strategies, transformation roadmaps, go-to-market plans.
Why it works: The model interrogates you, forcing clarity on constraints and hidden assumptions.
Example prompt: “Act as [insert the role of your interlocutor]. Ask sequenced questions to pressure-test my plan [insert plan details]. Don’t proceed until I answer. Be firm but fair. Summarise gaps and offer corrections.”
5) Outline-to-Synthesis — From bullets to a defensible plan
Where to use it: converting outlines + leases/EPCs/notes into an asset plan or ESG roadmap.
Why it works: You expand structure without losing the brief; sections stay evidence-linked.
Example prompt: “Using the outline and attached sources, expand into a six-page asset plan. For each section: decisions, evidence, owner, KPI, and 90-day actions. Include a one-page executive summary.”
And More!
And beyond these five it is worth considering the ‘Vision & “Reasoning-Plus” research capabilities of these models.
Vision. Ask the model to critique masterplans or dashboard screenshots and list “unknowns to resolve before pre-app”. You’ll be surprised how competent frontier models (GPT-5, Claude, Gemini) are at reading visuals in context.
Agentic research. Today’s models can plan, gather sources, synthesise, and self-check. Use this to compress the first 60-70% of a company brief or market note, then spend your time on judgement and negotiation. In particular Gemini and ChatGPT both have ‘Deep Research’ modes, and ChatGPT has a separate ‘Agent’ mode where it can actively visit websites and your own computer network (with permissions of course).
This is where you get an advantage others aren’t taking.
Where the edge shows up in Commercial Real Estate
And here’s how these capabilities directly translate into CRE workflows.Underwriting & valuation. Multi-scenario cashflows, covenant testing and comps coherence, packaged as a defensible memo. (Verifier + Self-Consistency.)
Leasing strategy. Trade-offs (rent-free vs capex), anchor-tenant sequencing, adversarial planning for negotiations. (Flipped Interaction.)
Asset management. Quarterly plans that reconcile OPEX, tenant feedback, and BMS data into a single action map. (Outline-to-Synthesis + Verifier.)
ESG/retrofit. Pathway design with standards cross-walks, capex phasing and risk logs. (Outline-to-Synthesis + Self-Refine.)
Workplace strategy. Hybrid policy, utilisation analysis, change-management comms. (Self-Consistency + Flipped Interaction.)
The Window is Open (for now)
The above are just a starter set of tools you can leverage. There is almost no aspect of real estate where the application of excellent prompts will not yield strong results.
Yet, to repeat - ALMOST NO-ONE IS TAKING ADVANTAGE
Over time (I guess - 12-18 months) the default setting will shift towards reasoning; rate limits will rise, friction will fall, and more people will use these tools. But capability ≠ competence. Habits, libraries and governance are durable moats. If your top five workflows don’t yet have standard reasoning templates, you’re leaving results on the table. The easy pickings are there.
OVER TO YOU
A Personal challenge: pick one high-stakes task this week and make reasoning-by-default your norm—using a verifier step and a defined deliverable. Measure cycle time and revision count.
Team move: nominate three pattern owners to maintain a shared prompt library measure internal use of reasoning.
If you want to institutionalise these wins before your competitors catch up, the next #GenerativeAIforRealEstatePeople cohort starts on the 5th September and includes the full GPT-5 Prompt Library, with live practice on the patterns above.
The advantage is real—and still underpriced.
Short-Term Wins are still Wins!
But the window WILL close, so act now.
Thank you for reading Trillion Dollar Hashtag. Here’s to rethinking the future of real estate together.
Best regards,
All things
#SpaceasaService
Exploring how AI and technology are reshaping real estate and cities to serve the future of work, rest, and play.