{"id":145492,"date":"2026-08-19T23:00:10","date_gmt":"2026-08-19T23:00:10","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/145492\/"},"modified":"2026-08-19T23:00:10","modified_gmt":"2026-08-19T23:00:10","slug":"the-perfect-proposal-paradox-and-garcias-law-in-ai-enabled-acquisition","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/145492\/","title":{"rendered":"The perfect proposal paradox and Garcia\u2019s law in AI-enabled acquisition"},"content":{"rendered":"

I have been thinking a great deal about the changing landscape of federal acquisition. After nearly 40 years in and around this business, I am both excited and concerned.<\/p>\n

I am excited by the continued focus on operator and mission outcomes. The enduring push to pilot, iterate and integrate remains the real secret sauce for delivering mission capability at the speed of technology change.<\/p>\n

My concern is the evolving process of matching needs to solutions. We do not have a single clean scoreboard for every major and non-major acquisition, but we all see the recurring pattern: Programs exceed cost, slip schedule and\/or miss the performance mark. In many cases, the government lacks the aggregated data, oversight visibility and performance reporting needed to confidently determine whether promised capability was delivered on time, on cost and at the expected level of performance.<\/p>\n

That is why current acquisition transformation efforts matter. Done well, they can improve speed to capability, sharpen performance metrics and better align people, processes and authorities around mission outcomes.<\/p>\n

]]><\/p>\n

But I remain cautious about an emerging \u201cask, answer, assess and award\u201d cycle increasingly enabled \u2014 and potentially dominated \u2014 by artificial intelligence.<\/p>\n

I believe we are approaching what I call\u00a0\u201cThe Perfect Proposal Paradox.\u201d<\/p>\n

The future could look like this: The government uses AI to develop acquisition requirements and evaluation criteria. Contractors use AI to generate proposals optimized to those requirements, iterating until every word, graphic and claim aligns with what the evaluation model is likely to reward. The government then uses AI to evaluate and score those same proposals.<\/p>\n

The result?<\/p>\n

A perfectly written, perfectly proposed and perfectly scored proposal that may be perfectly wrong.<\/p>\n

\"\"<\/p>\n

\u201cThe Perfect Proposal Paradox\u201d\u00a0is the phenomenon where content is created, answered and graded without sufficient human understanding, judgment or factual validation. AI lowers the barrier to entry and enables proposals to become increasingly optimized for evaluation scoring, while becoming progressively less reliable as indicators of actual delivery capability.<\/p>\n

]]><\/p>\n

A recent Brown University ECON 1170 case<\/a> offers a useful warning sign. A take-home midterm reportedly produced an average score near 96, with many perfect or near-perfect scores. Previous in-class exams reportedly averaged in the 65-to-80 range. When the final was administered in person, without the same take-home conditions, the average reportedly fell dramatically. While the facts should be handled carefully and individual cases matter, the lesson is clear: A perfect score may tell us more about the assessment environment than the actual knowledge, skills, abilities and capabilities of the submitter.<\/p>\n

\"\"<\/p>\n

As AI improves the perfection of proposals and evaluations, the risk increases that acquisition systems reward alignment over truth.<\/p>\n

But why is this?<\/p>\n

There is some very interesting analysis published in the 2026 AI & Society paper \u201cMore problems of large language models in comparison with human knowledge\u201d<\/a> by Harry Collins, Simon Thorne, Paul Newbury and Robert Evans. Collins and team coined what they called \u201ca response imperative.\u201d\u00a0 In the use of AI large language models (LLMs), they described that a LLM \u201cknows\u201d only that an answer is required. They assert LLMs are structurally predisposed to produce an answer even when evidence, experience or discernment might prove otherwise \u2013 a \u201cresponse imperative,\u201d not a \u201ctruth imperative.\u201d<\/p>\n

I could not agree more.<\/p>\n

So, my axiom is simple:<\/p>\n

Proposal perfection is not proof of performance.<\/p>\n

And two corollaries follow:<\/p>\n

]]><\/p>\n

Corollary 1:\u00a0AI makes it cheaper to look capable before it makes anyone more capable in reality.
\nCorollary 2:\u00a0The more perfect the proposal, the more rigorously its claims must be validated.<\/p>\n

\"\"<\/p>\n

This matters because AI will likely increase the number of bids, reduce traditional barriers to entry and make written differentiation harder. Contractors will be able to iterate versions after versions of potential responses, all in short windows. \u00a0I contend that iterations are not necessarily illustrations of clearer truths.<\/p>\n

Proposals will sound sharper. Compliance will look cleaner. Scoring may appear more precise.<\/p>\n

But will the offeror actually understand the mission?<\/p>\n

Can the team really deliver?<\/p>\n

Are the people real and available?<\/p>\n

Is the price executable?<\/p>\n

Does stated past performance actually prove the claim?<\/p>\n

The answer is not less AI. It is more evidence.<\/p>\n

As AI\u2019s response imperative drives more complete proposals and more precise evaluations, the government must counterbalance it with a truth imperative: evidence, oral defense, demonstrations, pilots and validated performance.<\/p>\n

That is why I champion\u00a0Commercial Solutions Openings (CSOs). A CSO is an accelerated path to need, discussions and show-me-proof process.<\/p>\n

\"\"<\/p>\n

CSOs directly challenge the declarative nature of traditional proposals. They better inform the narrative by forcing the proposed solution to meet reality. They also align with the spirit of acquisition reform because they emphasize:<\/p>\n

Concise concept papers
\nOpen technical dialogue
\nCommercial and innovative solutions
\nPhased evaluation
\nDemonstrations
\nPrototypes
\nPilots
\nEvidence before large-scale commitment<\/p>\n

A CSO is not inherently an Other Transaction Authority (OTA) agreement. It is primarily a competitive solicitation and selection method. The cited authority determines the resulting award instrument, which may be Federal Acquisition Regulation (FAR)-based or may use another authorized pathway.<\/p>\n

Instead of asking \u201cWho wrote the best 30- or 75-page technical volume?\u201d the government can ask a better question: \u201cWho understands the problem, has a credible solution and can prove it in a real environment?\u201d<\/p>\n

\"\"<\/p>\n

Let\u2019s not dilute the great energy and vision driving acquisition reform. In the age of AI, truth must be demonstrated \u2014 not merely generated.<\/p>\n

Written proposals alone will not move the mission needle in the direction our warfighters need.\u00a0 Let\u2019s see, touch and assess what\u2019s under the hood before we expend critical resources on the \u201cperfect proposal!\u201d<\/p>\n

Greg Garcia is the chief technology officer and chief experience officer for Inserso Corp. He spent more than 23 years as a Senior Executive Service technology executive in the Defense Department, including both the Air Force, Army, and deployed. He served as the deputy CIO\/G6 and chief data officer of the Army, the U.S. Army Corps of Engineers CIO, director of the Army Information Technology Agency \u2013 the joint services delivery agency for Pentagon IT.\u00a0\u00a0<\/p>\n

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