Robert L. Kay

Analysis should show that a design will work before manufacture, and that prediction should then be checked against the built hardware.

• Testing alone falls short: nobody has the budget to test every possible scenario, and a failure found after the fact becomes a manufacturing and customer problem.
• Kay puts the distinction plainly — explaining performance after the event is science; predicting it beforehand is engineering — and holds other people's tools to the same standard they apply to their own work.

Kay describes a timer circuit built for a munitions system: a two-inch-diameter board with a custom integrated circuit that had to survive a ten-year shelf life and detonate at the right moment, alongside an earlier discrete version with fifteen resistors standing on end to fit the tiny space. They spent nine months designing and testing it, ran it in an environmental chamber from -40 to +125C with worst-case component tolerances, and report that both manufacturing runs matched the paper analysis at a yield better than 0.1%.

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They trace their doubt to Turing's proof on the halting problem, which means the behaviour of these systems cannot be reliably predicted in advance, and add a practical complaint: no published, valid engineering analysis supports the AI tools and devices on offer.

• As a business owner, Kay wants to know who is responsible when an off-the-shelf AI product, or a part of one, fails in a way that raises liability or safety concerns.
• Their position is that testing alone is not enough when the budget does not stretch to every scenario, and that a buyer who does not ask to see the engineering homework absorbs the liability.

The same post that presents the munitions timer board as a model of predicted-and-verified engineering applies that standard to AI. Kay says the new venture will need something akin to AI, but finds no published engineering homework to justify trusting it — and asks who is left holding the bag if it fails.

Kay intends to use AI-like tools in the new venture while questioning what would make them trustworthy. Buyers should ask what evidence supports that trust.

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Culture does not appear on the financial spreadsheet, so a buyer can strip it without seeing what is being destroyed.

• Warning signs stay hidden because a quality management system can look flawless on paper while functioning only as a shield against liability.
• Kay names two mechanisms.
• "Leadership Drift" moves operations to a cheaper facility and separates management from the workforce, allowing problems that were once resolved quickly to fester.
• "The Margin Squeeze" knowingly deprioritises component quality and rigorous testing to hit quarterly targets, putting the end user behind short-term profit.
• Kay calls the first incompetence and the second a betrayal, and replaces the audit question — "Did we pass the audit?" — with "Did we compromise the design?"

Kay recounts the experience of a friend, Stephen Hauser, who built an industrial design firm over more than forty years of medical device work and sold it on retiring.

• The acquiring company then damaged client relationships built over decades and dismantled the environment that had motivated the team.
• Kay quotes Hauser's own account of the sale and points to Boeing — where leadership moved away from the factory floor and quality became a metric on a spreadsheet — as a public example of the pattern.

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They contrast engineers who acted on ethics to save Apollo 13's crew with what is described as "Space as a Service," where the commercial space effort is handed to executives raised on maximising shareholder value.

• In this account, mission statements on lobby walls often mask a lack of technical and corporate diligence, and teams tend to mirror their management: if leadership treats the truth as something to optimise, that becomes the working rule below.
• Kay explicitly accepts the profit motive — putting profits over ethics is acceptable for something like hairpins — so the disagreement is about the product category and the stakes, not business itself.

Kay lists recent recalls and lawsuits as cases where rigor was pushed aside for the stock ticker: the J&J heart pump failures, the Purdue Pharma crisis, and the Philips CPAP recalls. They return to Boeing's move of the C-suite away from the factory floor and close by asking whether a culture of rigor can survive as space is commercialised.

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Consumers buying from a clean-label brand expect the ingredient list to be honest and consistent, but delivering that depends on a quality team working quietly to uphold standards, catch counterfeit raw materials, and stop batch-to-batch formulation drift before a product ships.

• There is no applause for a batch that did not fail.
• Kay frames this with a line from Charles E.
• Kelly, a WWII Medal of Honor recipient: many men deserved medals, but no one saw what they did, or no one survived to report it.
• They extend the idea from battlefield recognition to corporate quality teams.

Kay describes the quality assurance team behind a clean-label food and beverage brand — the people who prevent a formulation shift or counterfeit ingredient from reaching a customer — and asks readers to name an area of unseen discipline in their own industry. They repeat the question across posts.

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It must be built on replicable, verifiable data generated locally at every node of a supply chain, so people who depend on that supply chain can check it themselves rather than take an assurance on faith. Kay presents Prismal as a venture for quality, operations, and supply-chain leaders in food and beverage.

Kay announces Prismal's first official seed fundraising pitch at a Hive Innovation In Motion presentation, describing the venture as a tool for independently rebuilding eroded trust. In a later post, they describe building rapid spectrometry technology for the quality teams that safeguard food and beverage products.

Read original · Original 2

They are willing to stake personal credibility on those judgments, recommending named individuals publicly rather than in passing. Their preferred manager drives product design, schedules, and manufacturing without compromising their standards.

Kay writes that 360 Systems was a valued client while they were running Elite and that the pandemic downturn left the company without a recovery path. They name Alan Jermagian, who ran that company, as one of the best managers on their team and one of the few people they would give a good reference for, list the operations and manufacturing roles Jermagian is seeking, and link to his profile.

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Kay describes a timer circuit built for a munitions system: a two-inch board with a custom integrated circuit designed for a ten-year shelf life and a precise detonation window, alongside an earlier discrete version where fifteen resistors stand on end to fit the space.

• They spent nine months designing and testing it, ran it in an environmental chamber at -40 to +125C with worst-case component tolerances, and report that both manufacturing runs matched the analysis to better than 0.1%.
• The point they draw is that engineering means predicting performance in advance and then verifying the prediction.
• They then apply that standard to AI, saying no published engineering homework backs the AI tools now in use, and ask who holds liability when an off-the-shelf AI component fails in a safety-critical product.

For brands planning a campaign

This post supplies the collaboration's central argument: verification and accountability, not model capability. A QA technology vendor could build thought leadership around the liability question Kay raises — who is responsible when an AI-assisted inspection or testing tool fails — without having to supply the technical analysis they say is missing.

Kay tells the story of Stephen Hauser, who built an industrial design firm over more than 40 years of medical device work and sold it on retirement.

• They describe the acquirer damaging long-standing client relationships and dismantling the environment that had motivated the team, and name two failure patterns: 'Leadership Drift', where operations move to a cheaper site and management separates from the workforce, and 'The Margin Squeeze', where component quality and testing are knowingly deprioritized to hit quarterly targets.
• They call the first incompetence and the second a betrayal, point to Boeing as an example, and argue that a quality management system can look flawless — balanced spreadsheets, framed ISO certificates, passed audits — while having nothing to do with actual quality.

For brands planning a campaign

The post gives a vendor a ready-made framing for content on quality culture: the difference between audit compliance and design integrity.

• A collaboration could take up Kay's closing test — 'Did we compromise the design?' rather than 'Did we pass the audit?' — and apply it to a specific product category.
• Kay has already staked out the position, so the vendor's role is to supply evidence rather than argument.

Kay argues Apollo 13's crew survived because engineers acted on ethics rather than financial incentive, then contrasts that with what they call 'Space as a Service', where commercial space efforts are handed to executives raised on maximizing shareholder value.

• They list recalls and lawsuits — the J&J heart pump failures, the Purdue Pharma crisis, the Philips CPAP recalls — as cases where corporate culture favored the stock ticker over product quality, and return to Boeing's move of the C-suite away from the factory floor.
• Kay closes on a question rather than a claim: whether a culture of rigor can survive the boardroom as space is commercialized.

For brands planning a campaign

Kay will name large companies in public and frame quality failures as leadership choices, so a vendor should expect a sharper register than the usual quality-assurance content and should not expect them to soften claims for brand comfort. The post also shows Kay's habit of ending on an open question, which suits a co-authored piece that invites reader responses.

Kay opens with a line from WWII Medal of Honor recipient Charles E.

• Kelly — that many men deserved medals but no one saw what they did, or did not survive to report it — and reports that the earlier post sharing it drew a strong response.
• They extend the idea to quality assurance in food and beverage: a clean-label brand promising transparent ingredients depends on a QA team that catches counterfeit raw materials and prevents batch-to-batch formulation drift before anything ships, and gets no recognition when a batch doesn't fail.
• Kay then says they are an engineer building rapid spectrometry technology for exactly those teams, and closes by asking readers to name an area of unseen discipline in their own industry.

For brands planning a campaign

The clearest bridge between Kay's editorial themes and a QA technology vendor's interests: unrecognized quality work is the premise, and rapid spectrometry is the tool Kay is already building. A thought-leadership collaboration could co-author the follow-up to the closing question — collecting reader examples of unseen discipline and analyzing them — putting the vendor's audience in the content rather than beside it.

Kay announces a first official seed fundraising pitch for Prismal at an innovation presentation.

• They argue that trust in the food supply cannot rest on centralized corporate promises and must instead be built on replicable, verifiable data generated locally across every node of a supply chain.
• They describe Prismal as a lever giving people tools to independently move and rebuild eroded trust.

For brands planning a campaign

This identifies Prismal as an early-stage venture seeking funding, setting expectations for the scale and timing of any collaboration.

• Kay is likely to value partners who can support credibility with quality and supply-chain buyers.
• It also pins the positioning a vendor would be aligning with: verifiable data generated at the point of need, not assurances from a central authority.

Kay writes that 360 Systems was a valued client while they were running Elite, and that the pandemic downturn left the company without a recovery path.

• They name Alan Jermagian as one of the best managers of their team, citing consistent work, quality, and bringing out the best in the team while driving product design, schedules, and manufacturing.
• They describe Jermagian as someone who keeps their principles under pressure and leads from the front.
• Kay lists the roles Jermagian is seeking — operations manager, manufacturing manager, director of manufacturing, director of operations — and says they are one of the few people Kay would give a good reference for.

For brands planning a campaign

Kay's benchmark for leadership is results under pressure, quality, and leading from the front. A vendor proposing a collaboration should expect them to apply the same test to the partnership itself; content that profiles a person or team in that register is closer to Kay's idiom than an abstract discussion of quality systems.

Kay says their workplace has decided to sell a Haas VF-1 CNC mill, describing it as a well-cared-for workhorse: a three-axis machine with the wiring option for a fourth axis, capable of milling stainless, brass, plastics, and aluminium. Kurt vises, collets, tools, and bits are also available.

For brands planning a campaign

It supports a vendor's case that Kay speaks from hands-on manufacturing experience. Use it as background credibility rather than as a collaboration topic.

Kay's central engineering argument is that a design must be predicted to work in analysis before manufacture, then verified against that prediction by testing — proof by testing alone is not enough.

• They illustrate it with a 2-inch-diameter munitions timer board: nine months of design and test work, a discrete first version built for early delivery and a later custom integrated circuit, and environmental chamber runs at -40°C to +125°C against worst-case component tolerances.
• Both manufacturing runs matched their paper analysis with yields better than 0.1%.
• Kay states the rule plainly: predict in advance that your design will work, then verify the prediction.

Personal design story first, general rule second: Kay walks through a specific board, then turns it into a standard they expect of any engineer today.

For brands planning a campaign

A thought-leadership collaboration on engineering rigor can use this as its anchor narrative: Kay supplies the specifics — chamber conditions, tolerance assumptions, yield figures — that make a quality claim inspectable rather than promotional.

Examples: Source 1

Their operational concern is narrower than a general unease about the technology: they do not find published, valid engineering homework behind AI tools and devices, so they cannot see why those tools should be trusted. The question they raise as a business owner is who is held responsible when an off-the-shelf AI component fails in a way that raises liability or safety concerns.

For brands planning a campaign

For vendors selling verification, compliance, or test tooling, Kay is a credible voice on accountability for third-party components. They are likely to press on who owns a failure and what supports a trust claim, so a campaign should come with technical documentation rather than assurances.

Examples: Source 1

Drawing on their friend Stephen Hauser's industrial design firm, sold after more than 40 years, they describe an acquirer that did not understand the business, damaged decades-old client relationships, and treated the culture as something to strip-mine for short-term yield.

• Kay names two mechanisms: "Leadership Drift," where operations move to a cheaper facility and management separates from the workforce, and "The Margin Squeeze," where component quality and rigorous testing are knowingly deprioritized to hit quarterly targets.
• Their sharpest point is that a quality management system can look flawless on paper — spreadsheets balanced, ISO certificates framed, auditors checking boxes — while functioning only as a shield against liability.

A named friend's business sale supplies the human story; the two named mechanisms give the argument a reusable structure.

Kay treats the two mechanisms as different in kind: Leadership Drift as incompetence, the Margin Squeeze as a betrayal.

For brands planning a campaign

This is directly usable material for a quality-assurance vendor: Kay frames the gap between documented compliance and actual quality, and argues that quality is a behavior rather than a dashboard metric. They distinguish passing an audit from compromising a design.

Examples: Source 1

They point to Boeing moving its leadership away from the factory floor, and to the J&J heart pump failures, the Purdue Pharma crisis, and the Philips CPAP recalls as cases where rigor was pushed aside. Their test is not whether a company is profitable but whether a culture of quality can survive the boardroom.

Kay does not argue that profit is wrong in itself. Their stated line is that putting profits over ethics is acceptable for something like hairpins and a catastrophe for products lives depend on.

For brands planning a campaign

This is Kay's most quotable framing for a campaign about quality culture, but also their most confrontational. A brand should be prepared for them to apply the same test to its own product claims and should not recruit them for messaging that reads as a lobby-wall mission statement.

Examples: Source 1

Their position is that this trust cannot rest on centralized corporate promises; it must be built on replicable, verifiable data generated locally at every node of a supply chain. In a separate post, they describe building rapid spectrometry technology for the quality teams that safeguard food and beverage products.

For brands planning a campaign

This is the clearest statement of what Kay is building and whom they serve: food and beverage quality, operations, and supply-chain leaders. A collaboration on verification technology has a natural home here, and its framing should follow their emphasis on local, independently checkable data rather than brand assurance.

Examples: Source 1 · Source 2

Using a clean-label food brand as the example, they describe a QA team holding the line on standards, catching counterfeit raw materials, and preventing batch-to-batch formulation shifts before a product ships — work that goes unnoticed precisely when it succeeds. They close by asking readers to name an area of unseen discipline in their own industry.

Historical or human framing up front, a concrete industry example in the middle, and a direct question to the reader at the end.

For brands planning a campaign

This is the most natural hook for a thought-leadership collaboration aimed at quality professionals: Kay is already arguing for the audience's value to their employers. A sponsor can support that argument rather than interrupt it, and the closing question offers a format for comment threads or a co-hosted discussion.

Examples: Source 1

They describe Alan Jermagian, who ran 360 Systems while Kay was running Elite, as one of the best managers of Jermagian's team and one of the few people they know who kept their composure and principles under pressure.

• Kay explicitly recommends Jermagian for operations and manufacturing roles.
• The same post shows Kay evaluating managers on how they drive product design, schedules, and manufacturing, and whether they lead from the front.

For brands planning a campaign

Kay's posts draw credibility partly from personal and client relationships, so a collaboration built on a previous working relationship or shared technical problem will read more naturally than a cold promotional placement.

Examples: Source 1

They move between firsthand technical detail, historical analogy (Apollo 13, a WWII Medal of Honor recipient), and named examples of companies that failed on quality.

• They write in extended paragraphs rather than bullet lists, use hashtags such as TechnicalDiligence, EngineeringEthics, and QualityManagement, and often end with a question that invites readers to respond.
• Occasional posts step out of analysis entirely, such as one offering a three-axis Haas VF-1 CNC mill and tooling from their shop.

For brands planning a campaign

The workable collaboration format is a concrete technical or quality problem that Kay can walk through in their own words, with room for a long narrative and a closing question. Slogans, short-form copy, and scripts they did not write will fit poorly.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5 · Source 6

The post does not state their formal relationship with Prismal; ask whether they are a founder, owner, or employee.

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The post describes a prior leadership role but does not say whether they owned the company.

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The company was led by Alan Jermagian, whom Kay recommends for operations and manufacturing roles.

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The post does not describe a commercial relationship with Haas.

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Nath follows complaints through service systems; Kay examines how quality practices and management choices affect products.

Nath focuses on service failures, customer complaints, and how organizational processes shape what reaches leadership. Kay focuses on engineering, product quality, and the consequences of technical and management decisions in higher-consequence settings.

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Morales is a good next read for clear explanations of operations fundamentals; Kay is a closer fit for engineering verification and quality decisions.

Morales explains operations concepts such as capacity and facility layout through direct, classroom-style examples. Kay takes a more personal and argumentative approach, connecting engineering practice to ethics, accountability, and product safety.

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Weeks offers a shop-floor and industrial-supply perspective; Kay brings an engineering and product-safety perspective.

Weeks writes about metalworking shops, tooling costs, and relationships among shops, distributors, and sales representatives. Kay’s examples center on design verification, product quality, and the responsibilities of engineering and company leadership.

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Nath offers vivid, concrete examples of what happens after a failure: a bartender threatening a customer instead of fixing the coffee, three different people handling one complaint without reading the previous notes, blame quietly shifting onto the customer, and the gap between acknowledgement time and resolution time. Nath is a better fit if the vendor wants to discuss how truth degrades on the way up the chain, why feedback becomes dangerous to record, or how customers end up doing a company's follow-up work.

Nath's posts do not cover manufacturing, engineering verification, regulated-product quality, or food-and-beverage QA.

• A campaign with Nath would lose Robert L.
• Kay's predict-then-verify engineering examples and the technical-diligence argument tied to inspection and verification technology.
• Nath's audience is service- and leadership-focused, so a QA technology vendor targeting quality, operations, and supply-chain leaders in regulated manufacturing would need to supply the technical substance.

Examples: Source 1 · Source 2

Morales explains design capacity against effective capacity with a glass-of-water analogy and contrasts product layout with process layout. Choose Morales over Kay for an accessible operations-fundamentals hook or a discussion of how physical space and capacity limits shape output, rather than an argument about audit failures and design compromises.

Morales's posts focus on general operations concepts rather than firsthand engineering design, testing, regulated-product liability, quality management systems, or food-and-beverage QA.

• A collaboration would be less specific and less connected to the vendor's verification technology than one with Kay.
• The vendor would need to bring the technical evidence and quality-culture argument, so the result would likely read as operations education rather than specialist quality-culture thought leadership.

Examples: Source 1 · Source 2

Drawing on 35 years in metalworking, Weeks writes about relationships between distributors, reps, and shops, tooling costs, insert selection, auto-reorders, and trust. Weeks is a better fit if the vendor wants a manufacturing-supply-chain partnership angle or to discuss how tooling and distribution incentives affect shop performance.

Weeks's posts offer industry commentary and practical advice, not engineering verification or quality-culture analysis.

• They do not cover predict-then-verify discipline, regulated manufacturing, food and beverage, or the QA leadership audience for the campaign.
• The campaign would trade Kay's engineering-ethics argument for a narrower metalworking and distribution discussion.
• Ask Weeks for examples connecting that discussion to inspection or verification.

Examples: Source 1 · Source 2