GPT-5: What is new?

Most model upgrades feel incremental. GPT-5 class models clear several practical thresholds that change how investment teams prepare, review and decide. Here are the shifts that matter on live transactions, with plain examples of what they unlock.

1) Document capacity that matches real deal packs

What changedHandles 600+ pages in a single session (roughly 400 with GPT-4). That is enough to read a full contract stack with all appendices at once.

What it means in practice

• One pass over SPAs, disclosure letters, bond indentures, side letters and schedules.

• Cross-references actually cross. Covenants, carve-outs and definitions link across the entire pack.

• Review packs shrink. Findings arrive consolidated instead of scattered across multiple summaries.

2) Clearer reasoning in your deal papers

What changedOn complex financial reasoning benchmarks GPT-5 scores 18 per cent higher. It follows logic through waterfalls, earn-outs and covenant tests more reliably.

What it means in practice

• It surfaces deal-structure risks and compliance gaps that hide in footnotes.

• It explains its steps, which makes the analysis easier to audit.

• First drafts for committee papers are clearer, with assumptions and edge cases listed.

3) Built-in fact-checking before it writes

What changedThe model can run live data checks mid-workflow, validating numbers, market trends and counterparties before producing outputs.

What it means in practice

• Headline KPIs are checked against filings or trusted data providers before they appear in a slide.

• Market comparables refresh during a negotiation.

• Automatic flags appear when a counterparty name, ownership or sanction status does not match.

4) From suggestion to execution

What changedGPT-5 runs multi-step automation chains end to end. Examples include due-diligence checklists, red-flag summaries and partner vetting, with minimal human input.

What it means in practice

• A standard DD pack arrives pre-read with issues ranked by risk and effort to resolve.

• IC pre-reads include a clean change log since the last draft.

• Follow-ups trigger themselves, such as requests for missing schedules or data-room gaps.

What changes on the desk

Task	Before	With GPT-5 pilots
Contract set review	Split across sessions, context gets lost	Full set in one pass, cross-linked findings
Risk analysis	Heavy manual effort under time pressure	Structured red-flag list with evidence and severity
Fact-checking	Ad hoc browser tabs	Inline validation and citations before output
IC memo drafting	Starts from a blank page	Reasoned draft with assumptions and open questions

A realistic note on limits

The model works best with clear questions, good data and human judgement at the edges. Teams that invest an hour in simple prompting habits and a shared checklist tend to see the largest benefits. Teams that skip training create frustrations with a shinier tool.

Bottom line

Capacity, reasoning, validation and execution have all moved forward. The result is fewer copy-and-paste hours and more time for judgement.

If you would like a structured pilot plan or example prompts for deal work, we are happy to share what has worked across engagements.