Esra Kucukciftci

Buyers and users are being trained to absorb the work of verification and correction, and that training is now reaching healthcare. My most recent shipment of medication from @wearehers seemed less effective than earlier ones, so I reported and asked them what to do. Their AI assistant told me to contact the compounding pharmacy directly, and it gave me the pharmacy’s name, address, and phone number, in full detail. Three things that are alarming about this exchange: 1. hims & hers moved its responsibility outside the commercial relationship. My contract, my payments, and my expectations are with hims & hers. They are accountable for making sure what they send me meets their quality standards, and for investigating when it does not. 2.      The AI assistant acted beyond what I expect hims & hers intends. I doubt a person on their team would hand a customer the contact details of a supply chain partner and ask her to resolve a quality question, directly with a third party supplier. 3. hims & hers asked the customer – me – to verify the quality of their own product on their behalf. This is a pattern across AI-based offerings: users are left to check whether what they paid for is right and spend money, time, and resources to correct them too – instead of only paying when performance and quality standards are met lime other commercial relationships. Buyers carrying the work and costs of verification and correction is starting to look like what #AIModelProviders are training us to do. In the absence of #AIPerformanceStandards, we pay for AI slop just the same unit economics as the good outputs, and end up carrying the costs of verifying and correcting the work that is below quality expectations. For most AI offerings, those expectations are informal and unwritten. AI offerings are running without a true, commercially contractable performance standard. A commercial contract is what makes a seller accountable for the quality of what it sells, and verifying that quality is the seller’s work - for @wearehers and for #AIVendors. This is the time to correct it, @wearehers , please escalate this and investigate the batch. #quantwarebook #AIEconomics #AIPricing #AIBuyersDemandBetter

When work is delegated, the existing instruments stop working. youtu.be/Pl9PcEX1sA0?si=noLR… via @YouTube

Download the AI Economic Stack Worksheet and understand your real AI costs beyond the invoice #QuantwareBook #QuantwareEra #AIOutcomeEconomics #AIIsNotSoftware #AI #AIEconomics #economics

Jensen Huang says "AI is a five-layer cake." That's an infrastructure builder's view. Buyers ask a different question: What does each unit of intelligence buy me and what is the true cost of each unit of intelligence? Buyer's view of the AI stack has eight layers. Download the AI Economic Stack worksheet and compare your own AI bill to the AI Economic Stack at quantwarebook.com/aistack #Quantware #AIEconomicStack #AI #AIEconomics #MeasuringAI

Five signs that your company is treating AI like software. #AIIsNotSoftware #QuantwareBook #AIEconomicStack #AI #AIEconomics #MeasuringAI

When I say "AI is not software" I mean it's a different thing - technologically, economically, and behaviorally. Technologically, AI stack has eight layers where software had four. Economically, AI’s value is measured per outcome, where software’s was fixed at purchase. Behaviorally, AI is a dynamic value system, whereas software is static. If you found this useful, please follow, repost, and join the conversation. #QuantwareBook, #AIEconomics, #AIOutcomeEconomics, #AIIsNotSoftware

The quantware era has already begun! Companies are buying, selling, and managing AI as if it were software. But AI is not software but an entirely different value system I call quantware. My book Quantware - Post-Software & Post-Product Era in Business & Technology is coming out in March 2027! Pre-order wherever you buy your books! #AIEconomics #QuantwareBook #QuantwareEra #AIOutcomeEconomics #AIPricing youtu.be/8xkmASM6UJ4?si=rjzJ… via @YouTube

Model drift is the silent operational cost of #AIDeployment. Someone pays the bill to keep the model current. #AIContracts that do not specify who pays are waiting for a dispute. I wrote an article on the pricing levers needed on the Model Ops layer of the #AIEconomicStack #AIPricing #AIStrategy #MachineLearning #AIGovernance #PricingStrategy #AIOutcomePricing linkedin.com/pulse/ai-econom…

Model operations is the work required to keep the AI model performing against its contracted standard. It includes initial training (often a one-time cost), fine-tuning for the buyer’s deployment context (usually a recurring cost), inference at runtime (a per-event cost), drift detection (a continuous cost), and retraining when drift exceeds a threshold (a periodic cost). linkedin.com/pulse/ai-econom… #AIPricing #AIStrategy #MachineLearning #AIGovernance #PricingStrategy #AIOutcomePricing #AIEconomicStack

Token counts in production run 30–60% higher than in evaluation. One inference can trigger 10–30 hidden model calls in agentic systems. This is one reason why so many POCs look great — and then explode in cost at deployment. Here are a few of the contractual swords and shields for writing #AIContracts from both vendors' and buyers' points of view for #AIPricing: #AIBuyersPlaybook and #AIVendorsPlaybook Follow me to learn more about #AIEconomics #AIEconomicStack #AIOutcomeEconomics and #AIEconomicStack

#AIInfrastructurePricing is complex. Bundled vs. pass‑through. Tiering. Commitments. Burst pricing. Model‑tier linkage. These five levers determine who holds the option and who absorbs the variable. Get the infrastructure layer wrong, and the entire AI deal becomes a structural mispricing. Follow me to learn more about #AIEconomics #AIBuyersPlaybook #AIVendorsPlaybook #AIEconomicStack #AIOutcomeEconomics

Hello X Community - after a 6-year hiatus! I write about #AIOutcomeEconomics and #AIEconomicStack. Follow me for insights about #AIPricing and commercial strategy -@EsraKc

I just got an email about changes to my Google AI Plus plan. It is the clearest expression I have seen of what is wrong with consumption-based pricing today. open.substack.com/pub/quantw…

Real‑time vs. batch. Dedicated vs. shared. Latency tier. Three decisions most contracts never specify — and they can swing AI costs 5–15x. If you don’t define the architecture at signing, you’re not negotiating price… you’re negotiating blind. In the second article of my series on the 8‑Layer #AIEconomicStack we dive deeper into the infrastructure layer. Three key decisions determine your cost structure: • Real‑time vs. batch • Dedicated vs. shared • Latency tier Most contracts never specify these which results in buyers paying 5–15x more than the use case requires. If you want to understand how #AIEconomics actually work - for both #AIBuyersPlaybook and #AIVendorsPlaybook, the #AIEconomicStack buff.ly/pfqmgWh article series is for you

Jensen Huang’s five-layer cake is a technologist's view of the AI stack. It maps where NVIDIA participates in the value chain. It does not map what AI buyers pay for or what AI vendors price. The #AIeconomics is different. It has eight layers, not five. Three of Jensen's layers (energy, chips, cloud data center infrastructure) collapse into one in commercial reality, because the buyer signs one infrastructure contract, and the vendor most commonly passes through one infrastructure cost. The two top layers (models, applications) explode into seven distinct layers, because the operational, data, ecosystem, governance, and human-services work that turns a model into an application is where most AI cost, economic performance, and commercial value actually live. The eight-layer #AIeconomicstack is the buyer-and-vendor view of the same system Jensen described. Each of its eight layers has its own commercial terms, its own performance standard, its own scale metric, its own cost structure, its own contribution to #AIoutcomeeconomics, and its own #AIpricing levers. Each layer can be a commercial sword or a shield, depending on who absorbs the variable and who holds the option. AI deployments span all eight at once, which is why the contracts that govern them are unlike anything the existing technology contracts can govern.

Jensen Huang Called AI a Five-Layer Cake. The #AIEconomicStack Has Eight Layers. Jensen’s five-layer cake is a technologist's view of the AI stack. It maps where NVIDIA participates in the value chain. It does not map what AI buyers pay for or what AI vendors price. #AIPricing #AIOutcomeEconomics #OutcomePricing #AIEconomics

Hello X Community - after a 6-year hiatus! I write about #AIOutcomeEconomics and #AIEconomicStack. Follow me for insights about #AIPricing and commercial strategy