Principal AI Operator
How I would approach the role: get the portfolio moving on internal efficiency, software-production speed, and AI-enabled product growth, so more companies reach maximum valuation at exit.
Peter framed this as an office-of-the-CEO capability under the AI-transformation umbrella. The job is to get roughly 130 current portfolio companies moving to the right on three vectors:
AI across GTM, finance, customer success, and operations.
Engineering maturity, moving companies up the L0 to L5 curve.
Credible, defensible AI built into the product itself.
The scoreboard is the exit. Companies with a defensible AI story plus proven internal efficiency are getting maximum valuations, and buyers now score the percentage of revenue directly attributable to AI. My job is to move as many companies as possible into that category, and to keep doing it as PSG acquires new (behind) companies.
PSG already has the thesis and the network: Peter's "rising tide lifts all boats," the CEO forums, the twice-yearly survey. What it lacks is someone who goes into a company, gets a skeptical CEO and team to actually move, ships production AI, and then codifies what worked so the next company skips the learning curve.
I run this as a repeatable program, not a set of heroics:
Peter's own lens: prioritize by capital at risk and plan status. I would build a simple two-by-two and sequence against it.
High capital at risk AND off-plan or at competitive risk. The swing factors for the fund, and where AI can change the outcome.
High capital at risk, on-plan, but low internal AI maturity. Quick efficiency and margin gains that show up at exit.
Smaller checks or already AI-forward. Give them the playbook and light-touch support.
Overlaid on the portfolio map (see the Portfolio tab): 133 current companies, heavily Cloud Business Apps, then Big Data & Infrastructure and Security. The survey signal is the opening: product-side, ~70 percent are in decent shape; internal-side, only ~15 percent have built anything real and ~70 percent have done nothing. The internal-efficiency vector is the biggest, most winnable gap, and it maps directly to burn and EBITDA.
Read the commitment schedule and the two most recent portfolio surveys. Build the prioritization map with the deal teams and Peter. Meet the two ex-CTOs and the GTM and finance operating leads so I complement rather than collide with them. Pick three to five lighthouse companies from the "lean in first" quadrant. Run a one-page AI diagnostic per company: where AI defends the moat, cuts cost, lifts the product, and how ready the CEO and team actually are.
With each lighthouse CEO, pick two or three high-leverage use cases (bias toward internal efficiency: largest gap, fastest to show in burn). Stand up a production pilot in weeks, not quarters. Insist on leadership-by-example: the CEO and C-suite personally using the tools, because Peter's data shows that is the single biggest predictor of whether a company moves. Start drafting the playbook from what actually works.
Turn lighthouse wins into v1 of a reusable playbook: diagnostic template, use-case library, vendor-selection guidance, hire-vs-promote and team-assessment frameworks, and a governance and evaluation baseline. Define the metrics I will report and the cadence. Bring first results to a CEO forum so the network sees peers winning, not a mandate pushed down.
The unit of work per company:
For engineering, I would use Peter's L0 to L5 maturity model as the shared language and set each company a realistic next-rung target, recognizing most are fifteen-year-old monoliths with weak test coverage and real engineering resistance, not greenfield.
A small, honest set so Peter can roll it up:
This is a program to build, not a solo act. Where surface area is large, I would use a small partner ecosystem and selective internal hires rather than trying to touch 130 companies personally.
I would solve the two structural gaps the survey already surfaced:
So an individual's agent does not walk out the door when they leave the company.
So wins compound across the portfolio instead of being rebuilt each time.
I would stay tightly coordinated with the ex-CTOs (engineering and product) and the GTM and finance operating teams, owning the office-of-the-CEO layer that today has no owner.
Peter's thesis is that the biggest AI risk is ignoring it, and that value comes from taking risk off the table. So I treat guardrails as a value-creation lever, not a compliance tax: clean AI governance is now something buyers diligence, so getting it right lifts exit multiples while it protects the fund.
Match the strength of the control to the blast radius, so low-risk work moves fast and only high-risk work gets heavy gates.
Drafting, summarizing, research. A human reviews before use. Light governance, high speed. Where most of the portfolio should move now.
Writes to a system of record, touches customer data, or informs a decision. Approval gates, verification against source, audit logging.
Acts without a human, or is customer-facing in the product. Formal evals, monitoring, rollback, and sign-off before scaling.
Validated on lighthouse companies and adopted across the portfolio.
More companies with both a defensible AI product story and proven internal efficiency.
Office-of-the-CEO capability with the right mix of partners and hires.
Being embedded with CEOs surfaces more than AI: a track record of improving the businesses in the other ways PSG operations exists to deliver.
All companies from PSG's public site. Industry is a best-fit estimate from each company's description, not PSG's official taxonomy. A small number of recent investments may not yet appear on the public site.
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