DEV Community: Blake Aber

Model Risk Governance: A Practical Framework

Blake Aber — Tue, 11 Aug 2026 14:51:28 +0000

Model risk governance turns scattered spreadsheets and black-box algorithms into a system someone is accountable for.

Blake Aber · Predicate Ventures · 2026

What model risk governance actually covers

Models drive lending decisions, capital calculations, fraud detection, and pricing. Each one carries the risk of being wrong—wrong by design, wrong in implementation, or wrong because it was applied to a situation it was never built for.

Model risk governance is the discipline that names an owner for that risk and defines how models get built, checked, and retired.

The reference point for most institutions is SR 11-7, the Guidance on Model Risk Management issued by the Federal Reserve jointly with the OCC. It describes an effective framework as covering three areas: model development, implementation, and use; effective validation; and sound governance, policies, and controls.

Those three areas are the outline for everything below.

The three components of a model

Before you can govern a model, you have to agree on what one is.

SR 11-7 defines a model as three parts: an information input component, a processing component, and a reporting component.

Inputs are the data and assumptions that feed the model. Processing is the method that turns inputs into estimates. Reporting is how the output reaches the people who act on it.

This definition matters commercially. It means governance applies to the assumptions and the presentation, not only the equation in the middle. A model can be mathematically sound and still fail because it was fed stale data or because its output was reported in a way that misled the people relying on it.

When firms scope their model inventory, this three-part definition decides what counts. Tools that only produce a number from a fixed formula may not qualify. Tools that estimate, forecast, or approximate almost always do.

Development, implementation, and use

The first area is the model's working life.

Development covers how the model is designed and tested. It includes the rationale for the method chosen, the quality of the data, and documentation good enough that someone other than the builder can understand it.

Implementation is the move from a developer's environment into production systems. This is where errors hide. A model that behaved correctly in testing can break when it is connected to live data feeds or coded into a different platform.

Use is the ongoing application of the model to real decisions. Governance here asks whether the model is still being applied to the conditions it was built for, and whether users understand its limits.

Each stage produces a record. The record is what a validator and an examiner will read.

Validation as an independent check

Validation is the second area, and it is the part most firms underbuild.

Effective validation is independent. The people checking a model should not be the people who built it, because builders are poorly positioned to see their own blind spots.

SR 11-7 recommends a periodic review of each model, at least annually and more often when circumstances warrant, to confirm it is working as intended.

Annual is a floor, not a target. A model tied to volatile markets or rapidly shifting customer behavior may need review every quarter. A stable model in a slow domain may justify the annual minimum. The framework asks the firm to decide the frequency and defend it.

Validation is not a one-time gate at launch. It is a repeated test that a model still fits the world it operates in. Markets move, portfolios change, and a model that was accurate two years ago may now be quietly biased.

What validation should confirm

A useful validation answers a few plain questions. Does the model do what its documentation claims? Are its inputs still appropriate? Do its outputs match reality when checked against outcomes? Are its known weaknesses being managed?

When the answer to any of these is no, the finding goes to the model owner with a timeline for resolution. Findings that sit unresolved are themselves a governance failure.

Governance, policies, and controls

The third area is the structure that holds the other two together.

Governance assigns roles. Someone owns each model. Someone validates it. Someone approves it for use and sets the policy for how often it is reviewed. The board and senior management are responsible for the framework as a whole.

Policies define the rules in writing—what qualifies as a model, how the inventory is maintained, what documentation is required, and how exceptions are handled. Controls are the mechanisms that enforce those rules, including access limits, change management, and monitoring of outputs.

Without this layer, development and validation become one-off efforts that decay. With it, they become a repeatable process an institution can staff, audit, and improve.

The regulatory baseline is settled and current

Model risk governance is not an aspiration a firm invents on its own. Regulators have set a shared standard.

The FDIC adopted the 2011 supervisory guidance as FIL-22-2017, extending the SR 11-7 approach across the institutions it supervises.

More recently, the OCC, Federal Reserve, and FDIC issued revised interagency model risk management guidance, rescinding prior issuances including OCC Bulletin 2011-12. The Federal Reserve published its version of the revised guidance as SR 26-2.

The practical takeaway is that the three-area framework—development and use, validation, and governance—remains the reference standard, now expressed through the current interagency guidance. Firms building or refreshing a program should map to the version in force for their regulator rather than the superseded letters.

Building a program that holds up

A credible model risk governance program is legible. An outsider should be able to open the inventory, pick a model, and trace its development record, its last validation, its owner, and its review schedule.

Start with the inventory. You cannot govern what you have not listed, and most firms discover they own more models than they thought once they apply the three-component definition.

Then set validation independence and cadence. Assign owners. Write the policy. Wire in the controls that catch a model drifting or a change slipping through untested.

The cost of this work is real. The cost of a mispriced portfolio, a failed capital calculation, or an examiner finding is larger, and harder to schedule. Governance is how a firm pays the smaller bill on purpose instead of the larger one by surprise.

AI Risk Management Playbook: A Working Guide

Blake Aber — Mon, 10 Aug 2026 10:20:46 +0000

A working AI risk management playbook turns abstract governance principles into decisions your teams can make on a deadline.

Blake Aber · Predicate Ventures · 2026

Why a playbook, not a policy

Most organizations already have an AI policy. Few have a playbook.

A policy states intent. A playbook states procedure: who decides, on what evidence, by when, and what happens when the answer is no.

The gap matters because AI risk does not announce itself. It surfaces at the moment a model is repurposed, a dataset shifts, or a vendor changes its terms. When those moments arrive, teams need a sequence to follow, not a statement of values to reread.

This playbook uses the four functions of the NIST AI Risk Management Framework as its spine. NIST released AI RMF 1.0 on January 26, 2023, and it has become the common reference for enterprise AI governance in the United States. The four functions—Govern, Map, Measure, Manage—give you named stages to assign owners and checkpoints.

Govern: set the standing rules before anyone builds

Governance is the layer that outlasts individual projects. It answers the questions you should not relitigate every time a new use case appears.

Start with three artifacts.

An inventory

You cannot manage what you have not counted. Maintain a live register of every AI system in use, including third-party tools and features embedded in software you already license. Each entry records the owner, the purpose, the data it touches, and its risk tier.

A risk tiering rule

Not every model deserves the same scrutiny. Define tiers by potential harm—effect on people, on legal exposure, on revenue. A copywriting assistant and a credit-decision model belong in different tiers with different approval paths.

A decision authority

Name who can approve deployment at each tier. Low-tier tools may clear at the team level. High-tier systems require sign-off from a named committee with legal, security, and business representation.

Governance also sets escalation. When a model behaves outside expected bounds, who is paged, and who can pull it from production? Write that down before you need it.

Map: understand the system in its context

Mapping is the diligence stage. Before measurement, you establish what the system is supposed to do and where it can go wrong.

Document the intended use in plain language, then document the foreseeable misuses. A model built to summarize support tickets can be fed adversarial input or asked questions it was never validated for. Naming those paths early tells your testers what to probe.

Map the data lineage. Where does training and input data originate, who owns it, and what constraints attach to it? Data provenance is where many compliance failures begin.

Map dependencies. If your system relies on a third-party foundation model, its behavior can change without your involvement. Record which external components you depend on and what you would do if one degraded or disappeared.

Generative systems carry a distinct set of concerns—hallucination, data leakage, prompt injection, and content provenance among them. NIST addressed these directly in NIST-AI-600-1, the Generative AI Profile released July 26, 2024. Use it as a checklist when the system in question generates text, images, code, or audio.

Measure: test against defined thresholds

Measurement converts concern into evidence. The goal is to replace opinions about a model's safety with results.

Set thresholds before testing. Decide in advance what accuracy, bias, and reliability numbers you will accept. Thresholds set after seeing results tend to bend toward the result you want.

Measure across several dimensions.

Performance: Does the system meet its stated task quality on representative data?
Fairness: Do outcomes differ across groups in ways you cannot justify?
Reliability: Does performance hold when inputs drift from the test set?
Security: Can the system be manipulated through its inputs?

Measurement is not a launch-day event. Models degrade as the world moves away from their training data. Schedule re-measurement at intervals tied to risk tier, and trigger it whenever inputs or dependencies change materially.

Record the results where an auditor could find them. If you cannot show your work later, you did not measure—you guessed.

Manage: act on what you learned

Management is where the playbook earns its name. Measurement produces findings; management assigns responses.

For each identified risk, choose one of four responses and document it: accept, mitigate, transfer, or avoid. Accepting a risk is legitimate—if it is a decision, made by someone with authority, and recorded.

Mitigation is the common path. It includes technical controls such as input filtering and output review, and process controls such as human sign-off for high-stakes decisions. Match the control to the harm you mapped.

Build monitoring into production, not around it. The system should report on its own behavior—drift, error rates, unusual inputs—so problems surface in dashboards rather than in complaints.

Keep a response plan for failures. When a model produces harmful output, the sequence should be known: contain, assess, notify, remediate, record. Rehearsed response separates an incident from a crisis.

Making the playbook durable

The framework underneath this playbook is not static. NIST plans to review the AI RMF with formal community input no later than 2028, and the technology will move faster than that. Treat your playbook as a document that gets versioned, not one that gets finished.

Two practices keep it alive.

First, assign an owner. A playbook without an owner drifts into obsolescence within a quarter. Someone should be accountable for updating it as tools, regulations, and incidents accumulate.

Second, run it on real cases. A playbook that has never been used against a live deployment is theory. The first few times you apply it, you will find gaps—steps that are unclear, owners who were never told, thresholds nobody agreed to. Fix them while the stakes are low.

The organizations that handle AI risk well are rarely the ones with the longest policy documents. They are the ones whose teams know what to do when a model does something unexpected—and can show why they made the call they did.

Why VC Firms Invest in AI Startups Now

Blake Aber — Mon, 10 Aug 2026 10:20:45 +0000

The share of venture dollars going to AI has doubled in three years, and the concentration is now impossible to ignore.

Blake Aber · Predicate Ventures · 2026

The scale of the shift

AI firms made up 61% of all global VC investment in 2025, up from 30% in 2022, per the OECD. That is a doubling of share in three years.

The dollar figures match the percentages. VCs put $192.7 billion into AI startups in 2025 through Q3, according to PitchBook data reported by Bloomberg. That pace put 2025 on track to be the first year where more than half of all VC dollars went to AI.

When a single category crosses half of an entire asset class, it stops being a theme and becomes the market itself.

Why VC firms are concentrating on AI

Three forces explain the concentration.

The first is model performance. Capabilities that were research demos in 2022 became shippable products by 2025. Investors fund what customers will pay for, and paying customers arrived.

The second is defensibility questions. Firms are trying to identify which AI startups own something durable versus which are thin wrappers on someone else's model. That question sends money toward companies with data advantages, distribution, or infrastructure ownership.

The third is fund math. When one category compounds faster than the rest of a portfolio, partners feel pressure to keep pace. A fund that skips AI risks underperforming its peers on paper, even before returns are realized.

Where the money actually goes

The headline share hides an uneven distribution.

AI firms working on IT infrastructure and hosting attracted the most VC investment, reaching $109.3 billion in 2025, per the OECD. That single infrastructure layer accounts for $109.3 billion, more than half of the $192.7 billion figure.

The pattern tells you something about how VC firms invest in AI startups. The largest checks flow to the companies building the compute, hosting, and foundational systems that everything else runs on. Application-layer startups compete for the remainder.

For founders, this means the fundraising experience differs sharply by layer. Infrastructure companies raise large rounds against large capital needs. Application companies raise against traction and margins.

How to read the concentration as an investor

A market where one category takes 61% of dollars carries two readings, and both are worth holding at once.

The optimistic reading: AI is a general-purpose capability, and capital is repricing entire industries around it. Under this view, the concentration reflects genuine opportunity rather than crowding.

The cautious reading: capital concentration this fast tends to compress returns. When most funds chase the same category, entry valuations rise and future multiples fall. The winners may be real while the average outcome disappoints.

Neither reading requires predicting a crash. Both suggest discipline on price and a clear thesis about which layer of the stack a given check belongs in.

Questions that separate the checks

Before committing to an AI startup, a firm should be able to answer a few things plainly.

What does this company own that a model provider could not replicate? Data, distribution, workflow lock-in, or regulatory position are the usual candidates.

What is the gross margin after inference costs? Some AI products carry compute costs that erode the margins investors expect from software.

Which layer is this, and does the check size match the layer? Infrastructure and application companies have different capital profiles, and mismatches show up later as down rounds.

What this means for founders raising now

The environment is generous to AI founders in one sense and demanding in another.

Capital is available. With more than half of VC dollars aimed at AI, a credible AI startup has more funding options than a comparable non-AI company.

But availability raises the bar. When many companies pitch similar capabilities, investors filter harder on defensibility and unit economics. A founder who cannot articulate what stays proprietary as models improve will struggle, regardless of how much capital is in the market.

The practical move is to raise against a specific advantage rather than against the category. "We do AI" is no longer a differentiator when 61% of the market does too.

The durability question

The honest uncertainty is how long this share persists.

A doubling from 30% to 61% in three years is not a trend that can continue at the same rate, because the ceiling is 100%. At some point the share stabilizes or reverts.

What happens next depends on returns. If early AI investments return capital at rates that justify the concentration, allocators keep the weighting. If they do not, capital rotates and the share falls.

The OECD and PitchBook figures describe where money went, not where it will earn. That distinction matters. Investment share is a measure of conviction, and conviction is priced before it is proven.

The takeaway

VC firms invest in AI startups at a level without precedent in the asset class. The $192.7 billion figure and the 61% share both point to the same fact: AI is now the center of gravity for venture capital, not a segment of it.

The useful response is neither enthusiasm nor skepticism but specificity. Know which layer a company sits in. Know what it owns. Know whether the check size fits the capital profile.

The firms that do well in this cycle will be the ones that treated a 61% market share as a reason for discipline rather than a reason to relax it.

Operational Due Diligence: A Practical Guide

Blake Aber — Tue, 04 Aug 2026 16:08:12 +0000

Operational due diligence measures whether a fund can run its business, not whether it can pick investments.

Blake Aber · Predicate Ventures

What operational due diligence covers

Investment due diligence asks whether a manager can generate returns. Operational due diligence asks whether the firm can hold the assets, price them honestly, move cash safely, and keep records that survive an audit.

These are separate questions. A fund can post strong numbers while running a back office that would collapse under a redemption wave or a regulatory inquiry. Allocators who skip the operational review learn this the hard way.

The discipline grew out of failures where the strategy was fine but the plumbing was not. Fraud, misvaluation, and cash controls that let one person move money without a second signature — these are operational failures, not investment failures.

Why allocators run it separately

Most institutional allocators keep operational due diligence in a different team than investment analysis. The separation is deliberate.

Investment analysts want the deal to close. They build relationships with managers and grow attached to a thesis. Operational reviewers hold veto power precisely because they carry no incentive to say yes.

At many pensions, endowments, and funds of funds, the operational team can kill an allocation the investment team loves. That structure exists because the two functions pull in opposite directions, and the allocator wants both pressures represented before capital moves.

The core areas under review

Valuation

How does the fund price its positions? Liquid securities are simple. The scrutiny goes to illiquid or hard-to-mark assets, where the manager has discretion over the number that determines its fees.

Reviewers check whether valuation involves an independent party, whether the methodology is documented, and whether the manager can override the process. A fund that marks its own illiquid book with no external check is a warning sign.

Cash controls

Who can move money, and who approves it? The standard is separation of duties: no single person should be able to initiate and authorize a wire.

Reviewers trace the wire process end to end. They confirm that outgoing payments require dual authorization and that account changes trigger callback verification. Most large fund frauds trace back to a manager who controlled cash movement without oversight.

Service providers

The administrator, auditor, custodian, and prime broker form an external check on the manager's claims. Their quality matters.

Reviewers confirm that the administrator is independent and calculates the fund's net asset value rather than rubber-stamping the manager's figures. They verify the auditor is a recognized firm and that the audit opinion is clean. A name-brand administrator on paper means little if the manager still produces the numbers internally.

Compliance and legal

Who handles regulatory filings? Is there a compliance officer, and does that person have authority independent of the portfolio managers?

Reviewers examine the firm's regulatory history, past examinations, and any litigation. They read the fund documents for terms that shift risk to investors — gates, side pockets, suspension rights — and confirm those terms are disclosed and used consistently.

Technology and business continuity

What systems run the trading, accounting, and reconciliation? Are they enterprise tools or spreadsheets held together by one employee?

Reviewers ask about disaster recovery, data backups, and cybersecurity. A fund that cannot operate if its office loses power, or that stores investor data without protection, carries operational risk regardless of returns.

How the review runs

Operational due diligence usually starts with a document request. The manager sends financial statements, service provider agreements, compliance manuals, valuation policies, and organizational charts.

An on-site visit follows. Reviewers want to see the office, meet the operations staff, and observe how the firm actually functions rather than how the pitch deck describes it. Meeting the chief operating officer and the compliance officer in person reveals whether those roles exist in substance or only on the org chart.

Reference calls close the loop. Reviewers contact the administrator, the auditor, and sometimes former employees. Independent confirmation of the manager's claims carries more weight than the manager's own account.

What separates a pass from a fail

No fund is perfect. The question is whether weaknesses are structural or manageable.

A small fund running lean is acceptable if the controls that matter are in place. A large fund with poor cash separation is not, regardless of size. Reviewers weigh severity, not the length of the deficiency list.

The hard veto categories tend to be consistent: no independent administrator, no clean audit, valuation controlled entirely by the manager, and cash movement without separation of duties. Any one of these can end an allocation.

Softer findings — outdated policy documents, thin staffing, dependence on a single key person — usually generate conditions rather than rejections. The allocator asks the manager to fix the issue or accepts a monitoring arrangement.

Ongoing review

Operational due diligence is not a one-time gate. Funds change. Staff leave, service providers get swapped, and controls that held at onboarding can erode.

Allocators re-run the review periodically, often annually for material positions. They watch for turnover in the operations team, changes to the auditor, and any restatement of past figures. A manager who switches administrators quietly, or whose chief financial officer departs abruptly, warrants a fresh look.

For managers raising capital

Managers who understand what allocators test can prepare for it. The operational review is winnable when the firm treats its back office as seriously as its investment process.

Hire an independent administrator early. Engage a recognized auditor. Document the valuation policy and follow it. Build cash controls with real separation of duties before an allocator asks.

The managers who fail operational due diligence rarely do so because they cannot afford controls. They fail because they treated operations as overhead and never built the structure a serious allocator expects to find.

A clean operational review does not win an allocation on its own. But a failed one ends the conversation, no matter how good the returns look.

AI Governance Consulting: What to Buy and Why

Blake Aber — Mon, 03 Aug 2026 12:25:30 +0000

AI governance consulting is worth buying when it produces controls people follow, not documents auditors file.

Blake Aber · Predicate Ventures · 2026

What the market is actually selling

Most AI governance consulting engagements promise the same three deliverables: a risk register, a policy set, and a control framework mapped to a recognized standard.

The quality gap is enormous. Two firms can deliver identical-looking artifacts, and one leaves you with rules that shape how models get built and shipped while the other leaves you with a PDF nobody opens.

The difference is not the framework. It is whether the framework was translated into decisions your teams make every week.

The standard everyone maps to

The reference point for most engagements is the NIST AI Risk Management Framework, released January 26, 2023. AI Risk Management Framework | NIST

The AI RMF organizes work into four functions: Govern, Map, Measure, and Manage. It is voluntary and sector-neutral, which is why consultants like it — it gives structure without prescribing controls that might not fit your business.

NIST followed the framework with the Trustworthy and Responsible AI Resource Center on March 30, 2023, to help organizations put the RMF into practice. AI Risk Management Framework | NIST

If you are deploying generative models, ask specifically about NIST-AI-600-1, the Generative AI Profile released July 26, 2024. AI Risk Management Framework | NIST It addresses risks specific to generative systems, and a consultant who has not read it is selling you 2023.

Where the fee earns out

Governance consulting pays for itself in three places.

Inventory

Most organizations cannot list the AI systems they run. Shadow deployments, vendor features that quietly added a model, and pilots that became production all escape the register.

A good engagement starts by finding what exists, not by writing policy for what should exist. The inventory is the deliverable that surprises executives most, because it usually reveals systems nobody approved.

Risk tiering

Not every model needs the same scrutiny. A demand-forecasting tool and a credit-decision model carry different consequences, and treating them identically wastes effort on one and under-controls the other.

Consultants earn their fee by building a tiering scheme that routes high-consequence systems to review and lets low-stakes tools ship fast. This is the Map function of the AI RMF applied to your actual portfolio.

Control design

The last place value shows up is in controls that fit how your teams already work. Human-review gates, evaluation thresholds, documentation requirements, and monitoring — these only work if they sit inside existing engineering and procurement processes.

A control that requires a new committee meeting will be skipped. A control embedded in a deployment checklist will hold.

How to tell operators from slide decks

The commercial risk in this category is buying advice that never touches production. Here is how to screen.

Ask who implements. If the answer is "we hand off to your team," you are buying a plan, not a system. Firms that stay through implementation have more incentive to design controls that people can follow.

Ask for a sample control, not a sample policy. Policies are generic. A control — the specific gate, the specific threshold, the specific owner — reveals whether the firm thinks about enforcement or just about language.

Ask how they handle model changes. Governance is not a one-time certification. Models get retrained, prompts get edited, vendors push updates. A consultant who cannot describe change management is selling a snapshot of a moving system.

Ask what they measure. The Measure function of the AI RMF is where most engagements go thin, because measurement requires access to model behavior, not just to org charts. Firms that avoid the topic usually cannot do it.

Scope you should insist on

A defensible engagement covers four things.

Governance structure — who owns AI risk, who signs off, and where accountability sits when a model fails. This maps to the Govern function and is where the RMF spends most of its guidance, because structure precedes everything else.

System inventory and tiering — the map of what you run and how much each system matters.

Control library — the actual gates, tied to your development lifecycle rather than to a separate compliance track.

Monitoring and review cadence — how you catch drift, incidents, and vendor changes before they become disclosures.

If a proposal skips inventory or monitoring, it is selling policy and calling it governance.

Build versus buy

Some of this work is worth doing in-house. Inventory, once built, is maintained more cheaply by the teams who own the systems. Control enforcement belongs to engineering permanently.

What consultants add is the initial structure and the outside read on where your exposure sits. They have seen more failure modes than any single company accumulates, and that pattern library is the thing you are actually paying for.

The trap is renting the pattern library forever. Structure the engagement so the framework, the controls, and the review process transfer to your staff. If the consultant becomes a permanent dependency, the governance program is theirs, not yours.

What to pay for

Price the engagement against the deliverables that outlast it: an inventory your team can update, a tiering scheme they understand, and a control set embedded in existing processes.

Do not price it against the length of the policy document. A short policy that governs behavior beats a long one that decorates a shared drive.

AI governance consulting is worth buying when it leaves your organization able to answer three questions without calling the consultant back: what AI do we run, how much does each system matter, and what happens when one of them fails. A firm that gets you there has earned the fee. A firm that hands you a framework and leaves has sold you homework.

AI-Focused VC Funds: Where the Capital Went

Blake Aber — Mon, 27 Jul 2026 12:25:46 +0000

AI firms took most of the world's venture capital in 2025, and the money clustered at the top.

Blake Aber · Predicate Ventures · 2026

The share is no longer marginal

AI-focused funds now operate in a market where their category defines the whole. AI firms captured 61% of global venture capital in 2025, according to the OECD.

That figure reframes what an AI-focused strategy means. A fund that concentrates on AI is no longer taking a sector bet against the rest of venture. It is positioning inside the largest allocation flow in the asset class.

The scale is visible in the annual totals. Global VC investment in AI firms has climbed steadily, and PitchBook data through Q3 2025 put year-to-date AI funding at $192.7 billion.

Generative AI drives the totals

Much of the 2025 growth came from generative AI. The OECD's full report on AI venture investment attributes a large portion of the year's funding to generative AI firms specifically.

For an AI-focused fund, that concentration inside the category matters as much as the category's share of the whole. Capital is not spread evenly across machine learning applications, infrastructure, and tooling. It is weighted toward model developers and the companies building on top of them.

That weighting shapes where a fund can find entry points and where it will compete hardest for allocation.

The mega deal problem

The headline number hides a distribution problem. AI VC in 2025 concentrated heavily in mega deals over USD 100 million.

The top of the market is a small set of very large rounds. The OECD identifies the top five AI mega deals of 2025 as a meaningful slice of total deal value.

This creates two distinct games. One is access to the largest rounds, which is a capital and relationship contest that most funds cannot win. The other is early-stage sourcing, where a fund's edge comes from picking companies before the mega-round dynamics apply.

An AI-focused fund needs to know which game it is playing. A small fund that markets itself on AI exposure but has no path into the concentrated top of the market is selling access it does not have.

Deal size has moved

The average check has grown with the category. The OECD reports a significant increase in the mean AI VC deal size from 2014 to 2025.

Rising mean deal size compresses the returns math for funds that enter late. When rounds are larger, ownership at a given check size falls, and the entry valuation carries more of the risk. Funds that write early checks and hold through the growth of a company keep more of the outcome.

Geography is concentrated too

The capital is not only concentrated by deal size. It is concentrated by country. The United States accounted for the majority of AI VC deal value in 2025.

For an AI-focused fund, geographic concentration is both a constraint and a signal. Funds outside the US market face a smaller pool of the largest deals. Funds inside it face more competition for the same companies.

A fund's location and network determine which side of that concentration it sits on. A regional AI fund and a Bay Area AI fund are not running the same strategy even if their marketing language matches.

What this means for fund selection

For limited partners evaluating AI-focused funds, the market data suggests a few questions.

Where in the deal-size distribution does the fund operate? A fund claiming AI exposure through mega-round participation is buying into rounds that already carry high valuations. A fund sourcing early is taking a different risk with different ownership.

Is the fund's AI focus a thesis or a label? With AI at 61% of global venture, a generalist fund and an AI-focused fund may hold similar exposure by default. The label only adds value if it comes with sourcing, technical diligence, or portfolio support that a generalist lacks.

How does the fund handle the generative AI concentration? A strategy weighted entirely toward model developers competes for the same small set of companies as every large fund. A strategy that finds applications and infrastructure adjacent to those models has a wider field.

The risk inside the numbers

A category that takes 61% of venture capital carries concentration risk for the whole asset class. If AI valuations correct, the funds most exposed will be those that entered late and large, at the top of the deal-size curve.

The mega-deal structure amplifies this. When a small number of very large rounds define the market's deal value, a repricing of those companies moves the aggregate. Funds holding late-stage positions in the top rounds have the least protection.

Early-stage AI funds carry different risk. Their exposure is to company selection and survival rather than to entry valuation. That does not make them safe. It makes their risk legible in a way that late mega-round exposure is not.

Reading a fund against the market

The 2025 data gives LPs a backdrop to test any AI fund's pitch against.

A fund that describes AI as an emerging opportunity is describing a market that already commands most of venture capital. A fund that promises access to the largest AI companies should show the relationships that make that access real. A fund that claims an early-stage edge should show sourcing that operates below the mega-deal tier.

The category's dominance means the interesting question is no longer whether to invest in AI. It is which position in a concentrated, top-heavy market a given fund actually holds. That position, not the AI label, determines the return.

Post Acquisition Integration: A Field Guide

Blake Aber — Mon, 20 Jul 2026 11:50:31 +0000

Most deals are won at signing and lost in the months that follow.

Blake Aber · Predicate Ventures · 2026

The gap between the deal and the return

Buyers spend months on diligence and price. They spend far less time on what happens after the wire clears.

That imbalance shows up in results. The purchase thesis assumes synergies, retained talent, and combined systems. Integration is where those assumptions get tested against reality.

The volume of deals makes this more pressing. Global M&A deal value in 2025 rebounded to the second-highest total on record, up 36% versus 2024, according to Bain & Company. More transactions means more integrations that either return capital or destroy it.

Why integration difficulty has risen

The kind of deals being done shapes how hard integration is.

Bain reported that scope deals reached a record share of large 2025 transactions, as companies focused on topline growth and new capabilities. Scope deals are harder to integrate than scale deals. You are combining different customers, product lines, and operating rhythms rather than consolidating the same one.

Private equity added to the pressure. McKinsey reported that private-equity-led deal value grew sharply in 2025, outpacing the broader market. PE owners work on defined hold periods, so integration timelines are compressed by design.

Size compounds the challenge. PitchBook reported that megadeals drove 2025 growth, with billion-dollar-plus transactions generating a majority of global M&A value. Larger deals carry more employees, more contracts, and more systems to reconcile.

Start integration before close

The worst integrations begin the day after signing. The best ones begin during diligence.

Diligence should produce more than a valuation. It should produce a list of what will be combined, what will stay separate, and who owns each decision. Treat the data room as the first draft of the integration plan.

Name an integration leader early. This person is not the deal lead. Deal leads are rewarded for closing; integration leaders are rewarded for what happens over the following year. The two skill sets rarely sit in the same person.

Define the operating model before close. Will the acquired company run standalone, fold into the parent, or something between? That single choice drives every downstream decision about systems, reporting, and headcount.

The first 100 days

The opening period sets the tone for everything after it. Employees, customers, and suppliers are all watching to see whether the new owner is competent.

People decisions come first

Uncertainty drives attrition. The people most likely to leave are often the ones you most want to keep, because they have options.

Decide who stays, who leads, and how compensation works, then communicate those decisions quickly. Silence is read as bad news even when the news is neutral.

Retention packages matter for key staff, but they are not a substitute for clarity about roles. People stay for a defined job, not only for a bonus.

Protect the revenue

Customers do not care about your integration plan. They care whether their contract, their contact, and their pricing hold.

Assign clear account ownership on day one. A customer who does not know who to call is a customer a competitor can reach.

Hold pricing and terms steady through the transition unless there is a strong reason not to. Changing commercial terms during integration signals instability at the worst possible moment.

Set a small number of priorities

Integration teams try to do everything at once and finish nothing. Pick the few outcomes that justify the deal and sequence the rest behind them.

If the thesis was cost synergy, name the specific costs and the dates. If it was cross-selling, name the products, the accounts, and the owners.

Systems and data

Technology integration is slower and more expensive than most plans assume. It is also where synergy estimates quietly erode.

Map the systems on both sides early. Overlapping ERP, CRM, and finance tools each carry migration cost and risk.

Resist the urge to consolidate everything immediately. Some systems can wait; forcing a migration before the business is stable creates outages that cost more than the savings.

Data is the harder problem. Customer records, financial history, and product catalogs rarely match cleanly between two companies. Reconciling them takes time and dedicated staff, and it blocks reporting until it is done.

Measuring whether integration worked

Synergy targets set at signing tend to be forgotten by the second quarter. That is how value leaks without anyone noticing.

Track the synergies as line items with owners and dates. If a target was $20 million in cost reduction by a given quarter, it should appear in a report every month against actuals.

Watch attrition among the acquired staff. High turnover in the first year usually means the integration plan asked more than the organization could absorb.

Watch customer retention with the same discipline. Revenue that walks out during integration is the clearest sign the deal is underperforming its thesis.

What separates good buyers

The record-setting deal environment favors buyers who treat integration as a discipline rather than an afterthought. PitchBook reported that 2025 was the most active M&A year on record by both count and value, with deal count up 12.4% year over year. McKinsey reported that 2025 global deal value finished the year up 43%, exceeding the ten-year average.

More activity does not mean more success. It means more chances to get integration wrong.

The buyers who repeat well share a few habits. They plan integration before signing. They put a dedicated leader in charge. They decide the operating model early and communicate it fast. They protect people and revenue in the first hundred days. They track synergies as concrete line items.

None of these habits are complicated. They are simply harder to sustain than to describe, which is why the gap between the deal price and the return persists across cycles.

Acquisition Integration: What Drives Deal Value

Blake Aber — Mon, 20 Jul 2026 11:50:30 +0000

Integration is where deal value is made or lost—and the clock starts at close.

Blake Aber · Predicate Ventures · 2026

The deal is the easy part

Signing a purchase agreement generates headlines. Capturing the value that justified the price generates returns. The gap between the two is acquisition integration, and it is where most deals succeed or fail.

The environment favors buyers who move. Global M&A activity in 2025 rose 40% in value to an estimated $4.9 trillion, putting it on track to be the second-highest year on record for deal activity (Bain). More deals means more integrations competing for the same management attention.

Speed is the strongest predictor

The single clearest signal in the research is timing. A deal is 2.6 times more likely to succeed, and delivers 40% more total returns to shareholders, when synergy targets are met within the first two years post-close rather than taking more than four (McKinsey).

Early momentum compounds. Companies that outperformed the market index over the life of a deal captured a run rate equal to 50 percent of their public synergy target in the first year alone (McKinsey).

The problem is that the runway before close keeps shrinking usable time. The median lag between signing and closing has stretched to about 6.4 months, a 25% increase over 20 years ago, and nearly one in six transactions now takes over a year to close (McKinsey).

That delay is not idle time. It is a window in which integration planning can happen—if the acquirer uses it well.

Plan before you can act

Antitrust and regulatory rules limit what two companies can share before close. Merging teams cannot pool commercial data or coordinate as one organization while the deal is pending.

The consequence of ignoring those limits is severe. The European Commission can fine companies up to 10% of revenue for gun-jumping, and it imposed that maximum on Illumina for closing its acquisition of Grail without approval (BCG).

Clean teams—separate groups of employees or third parties authorized to review sensitive information under legal protocols—let acquirers build detailed integration plans during the pre-close window without breaking the rules. When close finally arrives, the plan is ready to run rather than starting from scratch.

Protect the base business

Integration attention often flows to cost cutting and org charts. Meanwhile the acquired revenue quietly erodes.

Acquirers typically see sales decline eight percent in the quarter after announcing a deal (McKinsey). Customers hear about the change, competitors call them, and salespeople worry about their jobs. Left unmanaged, that dip becomes permanent.

Protecting existing revenue is the first job of integration. Retention plans for key accounts and salespeople, clear commercial ownership, and fast decisions on product overlap all matter before any synergy math pays off.

Synergies are bigger than the model

Most deals are priced against a synergy estimate built during due diligence. Treating that number as the ceiling leaves value on the table.

Looking for sources of value beyond what justified the deal—what McKinsey calls opening the aperture—can increase synergies by 30 to 150 percent above due-diligence estimates (McKinsey). The diligence model was built under time pressure with limited access. Once the two companies are combined, the real opportunities become visible.

Revenue synergies are the hardest to capture and the most sensitive to leadership. Between 70 and 80 percent of mergers that met or exceeded their revenue-synergy goals had strong senior-leadership involvement from the CEO down to sales (McKinsey). Cost synergies can be driven from a spreadsheet. Revenue synergies require executives to show up and set direction.

The case for doing this repeatedly

Integration skill is a muscle, and companies that use it often get stronger. The advantage of frequent acquirers is widening.

Bain found the gap in total shareholder returns between frequent acquirers and inactive companies was 130% between 2012 and 2022, up from 57% between 2000 and 2010 (Bain).

A disciplined, repeatable approach outperforms occasional large bets. The median excess return for companies using programmatic M&A was 2.1 percent over ten years, meaning they beat their peer groups by at least 20 percent in total shareholder return (McKinsey).

The reason is straightforward. Serial acquirers build integration into an operating capability—standard playbooks, dedicated teams, known metrics—rather than reinventing the process each time.

What a working integration looks like

The patterns in the research point to a short list of practices that separate deals that create value from those that erase it.

Start planning before close

Use the pre-close window and clean teams to build a plan that can execute on day one, without crossing regulatory lines.

Set a fast synergy timeline

Aim to hit synergy targets inside two years, and structure early wins in the first twelve months to build momentum and credibility.

Defend the revenue base

Assume an eight percent sales dip is the default outcome and build retention and communication plans to prevent it.

Put leaders on revenue synergies

Cost synergies can be delegated. Revenue synergies need visible, sustained involvement from senior leadership.

Look past the diligence model

Treat the deal thesis as a floor. Once combined, hunt for value the original model could not see.

The bottom line

Acquisition integration is not the administrative work that follows a deal. It is the phase where the price paid becomes either a return or a loss.

The data is consistent across sources: speed, protected revenue, engaged leadership, and repeatable discipline are what turn a signed agreement into a successful acquisition. Deals fail slowly, in the months after close, long after the announcement fades.

Building Agentic AI Systems: A Practical Guide

Blake Aber — Tue, 14 Jul 2026 15:18:23 +0000

Agentic systems earn their keep when they can plan, act, and recover without a human in every loop.

Blake Aber · Predicate Ventures

What an agentic system actually is

An agentic AI system is software that pursues a goal across multiple steps, choosing its own actions along the way. It differs from a single model call in one respect: it decides what to do next based on what it observed last.

That feedback loop is the whole game. A model that answers a question is a function. An agent that reads a ticket, queries a database, drafts a fix, and verifies the result is a process.

Most production agents share four parts: a planner that breaks work into steps, tools that let the model act on the world, memory that carries state across steps, and a control layer that decides when to stop.

Start with the task, not the framework

The common failure is picking an agent framework before defining the task. Frameworks encode assumptions about how much autonomy the model gets, and those assumptions often fight your problem.

Begin by writing down the task boundary. What inputs arrive, what output counts as done, and what actions are allowed. A support agent that can issue refunds is a different risk profile than one that only reads knowledge base articles.

Then ask how many steps the task takes. Single-step tasks rarely need an agent at all. A prompt with the right context will outperform a loop that adds latency and failure modes.

Reserve agentic designs for work that genuinely branches: where the right second action depends on the first result, and you cannot enumerate the paths in advance.

Tools are the interface to the world

A model without tools can only produce text. Tools convert intent into effect: a search API, a database query, a code executor, a payment call.

Design tools the way you design any API. Give each one a narrow purpose, a clear name, and strict input validation. A tool named update_record that accepts arbitrary SQL is a liability. A tool named set_order_status that accepts an order ID and one of three states is safe.

Return structured results the model can reason about. When a tool fails, say why in plain language the model can act on. "Order not found" lets the agent try a different lookup; a stack trace does not.

Keep the tool count small. Every additional tool widens the space of wrong choices. Models select tools more reliably from a list of six than from a list of sixty.

Memory and state

Agents need to remember what they have done. Without memory, a loop repeats itself or forgets a partial result.

Separate two kinds of memory. Working memory holds the current task: the plan, the steps taken, the observations returned. It lives for the duration of one run. Long-term memory holds facts that persist across runs: user preferences, past resolutions, learned constraints.

Do not stuff everything into the context window. Context is expensive and models degrade when it fills with stale detail. Store the full history externally and feed the model a summarized, relevant slice.

Retrieval matters here. When an agent needs a fact from long-term memory, fetch it by relevance rather than pasting the entire store. The quality of what you retrieve sets a ceiling on the quality of what the agent decides.

The control loop

Every agent runs a loop: observe, decide, act, repeat. The control layer governs that loop, and it is where most reliability comes from.

Set a step budget. An agent that can loop forever will, usually on the day you least expect it. Cap the number of iterations and the total token spend per run.

Define stopping conditions explicitly. The agent stops when it produces a valid final output, when it exhausts its budget, or when it hits an error it cannot handle. Ambiguity in the stop condition produces agents that spin.

Add verification before the final answer commits. A second check — a validator model, a schema test, a rule engine — catches the confident mistakes that a single pass misses.

Handling failure

Agents fail in ways single calls do not. A tool times out. A step returns unexpected data. The model picks a valid action for an invalid reason.

Plan for each. Wrap tool calls in retries with backoff for transient errors. Give the model a path to report that it cannot complete the task, so it stops instead of fabricating a result.

Log every step: the input, the chosen action, the observation, the decision. When an agent behaves oddly, the trace tells you which step went wrong. Debugging an agent without traces is guessing.

Set guardrails at the boundary, not inside the prompt. A prompt instruction not to delete data is a suggestion. A permission check in the tool that refuses the delete is a control.

Multi-agent versus single-agent

The instinct to split work across many specialized agents is strong and often premature. Each agent boundary adds a handoff, and handoffs lose information.

Use a single agent with several tools until you have evidence it cannot cope. One planner reasoning over one task is easier to debug than a committee passing messages.

Move to multiple agents when responsibilities are genuinely separate and the interface between them is narrow. A researcher agent that returns findings to a writer agent works when the contract between them is a clean document, not a running conversation.

Shipping and measuring

An agent that works in a demo and an agent that works in production are different systems. The gap is edge cases, and edge cases only appear at volume.

Build an evaluation set before you scale. Collect real tasks, define what a correct outcome looks like, and score runs against it. Track completion rate, step count, and cost per task.

Roll out behind a human check first. Let the agent propose actions while a person approves them. The approval data becomes your evaluation set and your evidence for when to remove the human.

The measure of an agentic system is not how impressive its reasoning looks. It is whether it finishes the task, at acceptable cost, more often than the alternative. Design for that number and the rest follows.

Agentic AI Systems: How They Work and Where They Fail

Blake Aber — Mon, 13 Jul 2026 18:18:24 +0000

Agentic AI systems act on goals instead of answering prompts—which is exactly why they are harder to trust.

Blake Aber · Predicate Ventures

What an agentic system actually is

Most AI tools respond. You send a prompt, they return text. The interaction ends there.

An agentic AI system does something different. It takes a goal, breaks it into steps, and executes those steps against real tools and data—often across multiple cycles without a human in between.

The distinction matters. A chatbot that drafts an email is a model. A system that reads your calendar, checks a customer record, writes the email, and sends it is an agent. The second one takes actions with consequences.

That shift from output to action is the entire reason the category exists, and the reason it carries risk.

The core loop

Every agentic system runs a version of the same loop: perceive, plan, act, observe, repeat.

The model perceives its current state, usually as text describing the task and available context. It plans a next step. It acts by calling a tool—a search, an API, a database query, a code execution. It observes the result. Then it decides whether the goal is met or another step is needed.

This loop is what separates agents from single-shot models. A single call cannot recover from a bad result. An agent can read the error, revise, and try again.

The quality of an agentic system depends less on the model and more on how tightly this loop is built. Loose loops wander. Tight loops finish.

The four components under the hood

Planning

The system needs a way to decompose a goal into steps. Some systems plan everything upfront. Others plan one step at a time and adjust as they learn. Step-by-step planning handles surprises better but costs more calls.

Tools

An agent is only as capable as the tools it can call. A system with access to search, a code interpreter, and a database can do real work. A system with none can only talk. Tool design—clear inputs, clear outputs, predictable errors—determines how often the agent succeeds.

Memory

Long tasks exceed what fits in a single context window. Agents use memory to carry state forward: what they have tried, what worked, what the user wants. Poor memory means the agent forgets its own progress and loops.

Control

Something has to decide when to stop. Without limits, an agent can spin indefinitely, burning tokens on a task it cannot complete. Step budgets, cost caps, and human checkpoints keep the loop bounded.

Where these systems break

Agentic AI fails in specific, repeatable ways. Knowing them is the difference between a demo and a deployment.

Compounding errors. Each step carries a chance of a mistake. Chain ten steps together and small error rates multiply. A system that is 95 percent reliable per step is roughly 60 percent reliable over ten steps. Long tasks amplify weakness.

Looping. An agent that cannot tell it is stuck will repeat the same failed action. Good systems detect repetition and change strategy or stop.

Tool misuse. The model may call the wrong tool, pass malformed arguments, or misread a result. Tools that fail loudly and clearly help the agent recover. Tools that fail silently poison the rest of the run.

Goal drift. Over many steps, an agent can lose sight of the original objective and optimize for a proxy instead. It completes something, just not the thing you asked for.

None of these are exotic. They show up in nearly every production system, and most engineering effort goes into containing them.

Why reliability, not intelligence, is the constraint

The models are already smart enough for many agentic tasks. The bottleneck is consistency.

A task that succeeds 70 percent of the time is unusable for anything with real stakes. A human still has to check the output, which erases the time saved. The value of an agent comes from trusting it to finish without supervision, and trust requires reliability that most systems do not yet reach.

This is why the strongest agentic deployments are narrow. A system scoped to one workflow—processing a specific document type, handling a defined support category, running a fixed data pipeline—can be tested, measured, and hardened until it clears the reliability bar. Broad, open-ended agents rarely do.

Narrow scope is not a limitation to apologize for. It is the current path to systems that actually work.

How to evaluate an agentic system

When assessing a system, ignore the demo and ask about failure.

Measure completion rate on real tasks, not curated examples. Track how often a human has to intervene. Watch the cost per completed task, since agents that loop are expensive as well as unreliable.

Check what happens when a tool returns an error or an unexpected result. A system that handles the unhappy path well is closer to production than one that only shines when everything goes right.

Ask where the human sits. The best current designs keep a person at the checkpoints that carry irreversible consequences—sending money, deleting records, contacting customers—while letting the agent run freely on low-risk steps.

What this means for building

The teams shipping useful agentic systems share a pattern. They pick a task narrow enough to measure. They build tools with clear contracts. They instrument the loop so they can see where it fails. They set hard limits on steps and cost. And they keep humans on the decisions that matter.

The teams that struggle chase generality. They want one agent that handles anything, and they get a system that handles nothing reliably.

Agentic AI is a real capability, not a marketing category. It works when the loop is tight, the scope is defined, and the failure modes are managed. It fails when any of those are ignored.

The technology will improve. The discipline required to deploy it will not change. Build narrow, measure honestly, and treat reliability as the product.

Commercial Due Diligence: A Buyer's Field Guide

Blake Aber — Mon, 13 Jul 2026 18:18:23 +0000

Commercial due diligence tests whether the story behind a deal survives contact with the market it depends on.

Blake Aber · Predicate Ventures

What Commercial Due Diligence Actually Answers

Financial due diligence tells you whether the numbers are real. Commercial due diligence tells you whether they will still be real in five years.

The distinction matters. A clean audit confirms historical performance. It says nothing about whether the market that produced those results is growing, shrinking, or about to be reshaped by a competitor the seller failed to mention.

Commercial due diligence examines the demand side of a business. It asks who buys, why they buy, whether they will keep buying, and what could make them stop.

The output is a judgment on the investment thesis. Either the market supports the return you underwrote, or it does not.

The Four Questions Behind Every Assessment

Most commercial due diligence work reduces to four questions.

How big is the market, and where is it going? Total addressable market matters less than the direction and rate of change. A large market in decline is worse than a small market compounding at twenty percent a year.

Where does the target sit within it? Market share is a starting point. The harder question is whether that share reflects a durable advantage or a temporary lead that competitors can erase.

How loyal are the customers? Revenue that renews without effort is worth more than revenue that must be re-won every year. Retention, concentration, and switching costs define the difference.

What could break the thesis? Every deal has a small number of assumptions that carry most of the risk. The job is to find them and test them.

Sizing the Market Without Fooling Yourself

Seller-provided market figures deserve suspicion. They are usually built to support the price.

A sound market estimate works from the bottom up. Count the customers who could plausibly buy, estimate what they spend, and reconcile that against top-down industry data. When the two approaches disagree by a wide margin, the gap itself is a finding.

Growth rate matters more than absolute size for most investors. A market growing faster than the broader economy gives a business room to expand without taking share from rivals. A flat market forces every gain to come from someone else, which invites price competition.

Segment the market before trusting any aggregate number. A company may operate in a large category while serving a niche that behaves nothing like the whole.

Reading Competitive Position

Competitive position is where optimistic theses tend to fail.

Start with the structure of the market. A few large players with high margins signals barriers to entry. A crowded field with thin margins signals commoditization, regardless of what the pitch deck claims.

Then locate the target inside that structure. Ask what the business does that competitors cannot easily copy. If the answer is "nothing specific," the margins are borrowed and will be reclaimed.

Pricing power is the clearest evidence of position. A company that can raise prices without losing volume holds real advantage. One that discounts to keep customers is defending, not leading.

Watch for competitors that do not yet show up in the seller's framing. Adjacent players, new entrants, and substitute products often pose more threat than the direct rivals everyone tracks.

Customer Evidence Beats Management Narrative

Management will describe a business as they wish it to be seen. Customers describe it as it is.

Customer interviews are the most useful part of commercial due diligence and the most often shortchanged. A structured set of conversations reveals why customers chose the target, what would make them leave, and how they rate alternatives.

The patterns matter more than any single quote. When several customers independently cite the same weakness, that weakness is real. When praise is vague and complaints are specific, treat the complaints as the signal.

Revenue concentration deserves direct attention. If a handful of accounts drive most of the revenue, the health of those relationships determines the outcome of the deal. One departing customer can invalidate the entire model.

Retention data completes the picture. Cohort analysis shows whether customers stay and expand or churn and shrink. Reported retention that management cannot reproduce from raw data is a warning.

Stress-Testing the Thesis

The purpose of the work is not to produce a report. It is to decide whether the price makes sense.

Good commercial due diligence isolates the two or three assumptions that carry the return. Perhaps the thesis requires a certain growth rate, a specific retention level, or the success of a new product line. Name those assumptions and test each one against evidence.

Build the downside explicitly. If growth comes in at half the plan, does the deal still work? If the largest customer leaves, how much of the equity is exposed? A thesis that only functions under favorable conditions is a bet, not an investment.

Separate what the analysis found from what it could not verify. Honest reporting of the unknowns is more valuable than false confidence. Buyers can price uncertainty; they cannot price hidden assumptions.

Timing and Scope

Commercial due diligence usually runs alongside financial and legal workstreams, on a compressed schedule. Two to six weeks is typical, depending on deal size and access.

Scope should follow the thesis. A roll-up in a fragmented market needs deep competitive mapping. A subscription business needs granular retention analysis. Spending equal effort on every question wastes the limited time available.

Access shapes what is possible. Sellers control the flow of information, and the most revealing data often arrives late. Building customer contact into the process early prevents a scramble at the end.

What Good Work Produces

The deliverable is a clear position on the deal, supported by evidence a skeptical partner can check.

It should state whether the market supports the thesis, where the target stands against competitors, how durable the revenue is, and which assumptions carry the most risk. It should also say what remained unknown and why.

The test of quality is simple. Six months after close, does the business behave the way the analysis predicted? Work that survives that comparison earns its cost many times over.

Commercial due diligence does not remove risk from a transaction. It replaces vague optimism with a priced set of bets, which is the most any buyer can ask before writing the check.

Private Equity CEO Predicts AI Will Reshape Deals

Blake Aber — Mon, 06 Jul 2026 10:42:32 +0000

When a private equity CEO predicts AI will change the business, the useful question is which parts, and on what timeline.

Blake Aber · Predicate Ventures

The prediction, stated plainly

A private equity CEO predicts AI will move from a marketing line into the core of how firms operate. The claim is not that software replaces investors. It is that the work of sourcing, diligence, and portfolio management gets faster and cheaper per unit of output.

That framing matters. Predictions about AI often collapse into two camps: everything changes, or nothing does. The more grounded view sits between them, and it maps to specific tasks rather than whole job functions.

Where the change lands first

The earliest effects show up in tasks that are repetitive, text-heavy, and already well-documented. Private equity has plenty of these.

Deal screening is one. Firms review far more targets than they buy, and most of the early filtering is reading. Financial summaries, market notes, and management materials all pass through analysts before anyone commits real time. Models that read and rank these documents shorten the first pass.

Diligence is another. Contract review, customer concentration checks, and comparison against precedent deals are structured enough for machines to draft first and humans to verify. The output is a starting document, not a final call.

What stays human

Judgment about people, incentives, and timing does not automate well. Whether a management team executes under pressure, whether a market holds, whether a price reflects real risk. These decisions rest on reading situations that resist clean data.

The prediction assumes AI handles the reading and drafting while partners keep the decisions. That division holds only if firms treat model output as a draft to challenge, not an answer to accept.

Portfolio operations, not just deal-making

The more durable claim concerns portfolio companies after the deal closes. This is where a private equity CEO predicts AI will produce measurable returns.

Many portfolio companies run functions that AI touches directly. Customer support, sales operations, finance close cycles, and marketing content all carry labor cost that models reduce. A firm that owns dozens of companies can apply the same playbook across all of them.

That repetition is the point. A single company adopting AI captures a local gain. A private equity firm rolling the same approach across a portfolio captures the gain many times, and it can measure results across a controlled set of businesses.

The margin math

Private equity returns depend on buying a business, improving its operations, and selling at a higher multiple or higher earnings. AI enters on the operations side by lowering cost or raising output without proportional hiring.

If a portfolio company cuts service cost while holding quality, margin improves. Improved margin supports a higher exit price. The prediction reduces to a claim that AI adoption becomes a standard lever in the operational improvement toolkit, sitting alongside pricing changes and procurement.

The risk in the prediction

Every adoption story carries a cost story, and this one has three.

The first is measurement. Firms that count AI savings before checking quality tend to reverse course. A support system that closes tickets faster but leaves customers unhappy trades a visible metric for an invisible loss. Honest measurement requires tracking outcomes the model does not optimize for.

The second is integration cost. Dropping a model into an existing workflow rarely works on the first attempt. Data has to be cleaned, staff has to be trained, and processes have to be rebuilt around the new tool. The savings arrive after the investment, not before.

The third is uniformity risk. When many firms apply similar AI playbooks, the operational advantage narrows. If every buyer can raise margins the same way, the improvement gets priced into acquisition multiples, and the excess return fades. Early movers gain; the field catches up.

Reading a CEO's prediction critically

When a private equity CEO predicts AI will drive returns, the statement serves two audiences at once. Limited partners hear a firm that stays current. Portfolio managers hear a directive to adopt.

Both readings are reasonable, and both can be true while the specifics stay vague. The useful test is whether the prediction attaches to measurable claims: which functions, what cost reduction, over what period, verified how.

Predictions without those attachments are positioning. Predictions with them are operating plans. The distinction tells you whether a firm has done the work or is describing an intention.

Questions worth asking

An investor or operator evaluating such a prediction can push on a few points.

Which portfolio companies have adopted AI, and what did results look like against a baseline. How does the firm separate AI-driven gains from other operational changes. What did integration cost, and how long until payback. What quality metrics guard against savings that hide losses.

Answers to these reveal whether the prediction rests on evidence or enthusiasm.

What this means for the sector

The direction of the prediction is likely correct. AI reduces the cost of document-heavy and repetitive work, and private equity contains a lot of both. Adoption spreads because the economics favor it.

The timeline and magnitude are the open questions. Task-level gains arrive quickly and are easy to demonstrate. Portfolio-wide, verified return improvement takes longer and depends on execution that varies widely between firms.

The firms that gain most will treat AI as an operational discipline with measurement attached, not a theme to announce. They will track quality alongside cost, invest in the unglamorous integration work, and accept that the advantage erodes as competitors adopt the same tools.

A private equity CEO predicts AI will change the business. The prediction holds. What separates the firms that benefit from the ones that merely talk about it is the willingness to measure, and the honesty to report what the numbers actually show.

DEV Community: Blake Aber

Model Risk Governance: A Practical Framework

What model risk governance actually covers

The three components of a model

Development, implementation, and use

Validation as an independent check

What validation should confirm

Governance, policies, and controls

The regulatory baseline is settled and current

Building a program that holds up

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Why a playbook, not a policy

Govern: set the standing rules before anyone builds

An inventory

A risk tiering rule

A decision authority

Map: understand the system in its context

Measure: test against defined thresholds

Manage: act on what you learned

Making the playbook durable

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Why VC Firms Invest in AI Startups Now

The scale of the shift

Why VC firms are concentrating on AI

Where the money actually goes

How to read the concentration as an investor

Questions that separate the checks

What this means for founders raising now

The durability question

The takeaway

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Operational Due Diligence: A Practical Guide

What operational due diligence covers

Why allocators run it separately

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Compliance and legal

Technology and business continuity

How the review runs

What separates a pass from a fail

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For managers raising capital

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Post Acquisition Integration: A Field Guide

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Start integration before close

The first 100 days

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