# Economics Published articles for Economics. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Five monetization trends from global pricing leaders DevFeed: [Five monetization trends from global pricing leaders]() Original publisher: [Read original article]() Author: Scott Woody Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Stripe Blog]() Topics: [Artificial Intelligence](), [Software](), [stripe]() Tags: [agent](), [ai](), [economics](), [monetization](), [pricing](), [revenue](), [stripe]() ### AI overview Stripe presents five monetization shifts identified by global pricing leaders as AI changes software economics. The article highlights faster, more decentralized pricing iteration, the emergence of nonhuman or agent buyers, and the need for more flexible monetization beyond seat-based subscriptions and fixed annual recurring revenue. ### Source excerpt As AI transforms software economics, the standard revenue playbook is breaking down. Learn how leaders around the world are preparing for agent buyers, updating processes for faster pricing iteration, and building more flexible infrastructure. ## Q&A: Rethinking how innovation happens DevFeed: [Q&A: Rethinking how innovation happens]() Original publisher: [Read original article]() Author: Jason Sparapani | Department of Materials Science and Engineering Published: 2026-08-17T19:50:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Materials science and engineering](), [implementation]() Tags: [altruistic-science](), [artificial-intelligence](), [book](), [books-and-authors](), [business-and-management](), [dmse](), [economics](), [engineering](), [eugene-fitzgerald](), [faculty](), [fundamental-innovation](), [future](), [innovation](), [interview](), [invention](), [invisible-engine](), [misconceptions](), [mit-and-masdar-institute-cooperative-program](), [mit-books-and-authors](), [mit-dmse](), [mit-faculty-books](), [mit-faculty-interview](), [moore-s-law](), [q-a](), [research](), [research-commercialization](), [school-of-engineering](), [semiconductor-technologies](), [semiconductors](), [singapore-mit-alliance-for-research-and-technology-smart](), [strained-silicon](), [strategic-research](), [value-creation]() ### AI overview MIT professor Eugene Fitzgerald discusses his book The Invisible Engine: Why Innovation Evades Control, drawing on research programs and his experience co-inventing strained silicon. He describes innovation as an intersection of science, economics, market applications, technology, implementation, and society. ### Source excerpt In his latest book, Professor Eugene Fitzgerald examines the forces that turn breakthroughs into value -- and why innovation resists simple formulas. ## Best CircleCI alternatives in 2026 DevFeed: [Best CircleCI alternatives in 2026]() Original publisher: [Read original article]() Author: Pete Miloravac Published: 2026-07-31T12:11:33Z Content type: comparison Language: en Sources: [Semaphore Engineering]() Topics: [CI/CD](), [GitHub Actions](), [GitLab](), [Development]() Tags: [alternatives](), [ci-cd](), [compare](), [comparison](), [economics](), [platform](), [pricing](), [support]() ### AI overview This guide compares Semaphore, GitHub Actions, GitLab CI/CD, and Buildkite as alternatives to CircleCI in 2026. It evaluates build speed, cost predictability, support, deployment control, source-control integration, and hosting options. ### Source excerpt Teams looking for CircleCI alternatives are rarely replacing a single feature. They are reassessing build speed, unit economics, support coverage, deployment control, and how easily their CI/CD system fits the source-control platform they already use. CircleCI remains a capable hosted CI/CD product, but its credit-based model meters active users, compute by resource class and minute, [...] The post Best CircleCI alternatives in 2026 appeared first on Semaphore. ## Variance Reduction Below the Randomization Grain DevFeed: [Variance Reduction Below the Randomization Grain]() Original publisher: [Read original article]() Author: Tilman Drerup Published: 2026-07-01T16:28:36Z Content type: article Language: en Sources: [Instacart]() Topics: [experiments]() Tags: [capacity](), [causal-inference](), [economics](), [engineering](), [experiment](), [experimentation](), [experiments](), [machine-learning](), [marketplaces](), [metrics](), [reduce](), [science](), [techniques](), [variance]() ### AI overview This article explains how marketplace experiments can reduce metric variance below the level at which treatment is randomized. It describes cluster-level randomization for containing interference and shows how fine-grained outcome predictability can improve statistical power and reduce experimentation time. ### Source excerpt Sergio Camelo, Caitlin Kearns, Matias Cersosimo, and Tilman Drerup As artificial intelligence increases the velocity of engineering and science teams, experimental throughput is set to become a bottleneck for many product decisions. Many companies can now build faster than they can experiment, with queues of good ideas running the risk of not being tested because of lack of experimental capacity. This problem is particularly severe in marketplaces, where the presence of spillover and cannibalization effects between experimental units requires cluster-level randomization techniques. That randomization, in turn, has the unfortunate tendency to substantially reduce statistical power and slow down experimentation. In this post, we show that the predictability of outcomes at fine grains can be exploited to reduce the variance of aggregate metrics, even when experiments themselves are run at a coarse level. Since statistical power depends on metric variability, this yields considerable reductions in experimentation time. The Interference Problem In marketplace settings, behavior and outcomes for individual participants are inherently intertwined. In a delivery marketplace like Instacart, for example, the dispatch system solves a bipartite matching problem between shoppers and customer orders. Since assignments are global and interdependent, matching an order to one shopper means that the same order cannot be matched to another shopper. As a result, changing the handling for a single order creates ripples that affect the orders around it. If an experimenter were to assign a treatment intervention to one of these orders while leaving neighboring orders as controls, the latter would evidently be contaminated. A common response to this problem is to randomize treatments at the level of a cluster, chosen so that interference can stay within it. In food and grocery delivery, that cluster is typically a geographical region. Since every order within a region sees the same treatme ## Introducing the OpenAI Economic Research Exchange DevFeed: [Introducing the OpenAI Economic Research Exchange]() Original publisher: [Read original article]() Published: 2026-06-08T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [data-governance](), [jobs](), [data]() Tags: [ai](), [company](), [data](), [data-governance](), [economics](), [external](), [jobs](), [openai](), [research]() ### AI overview OpenAI is launching the Economic Research Exchange, a platform for structured collaborations with external researchers studying AI's economic effects on workers, firms, institutions, and the broader economy. Selected projects may use privacy-protected OpenAI tools and datasets under defined milestones, data governance, and review processes. ### Source excerpt OpenAI launches the Economic Research Exchange to study AI's impact on jobs, productivity, and the economy. Applications are now open for selected research projects. ## Marginal Revenue in SaaS: Formula, Marginal Costs, and Pricing Implications DevFeed: [Marginal Revenue in SaaS: Formula, Marginal Costs, and Pricing Implications]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-06-07T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Software]() Tags: [cost](), [economics](), [payment-processing](), [pricing](), [pricing-strategy](), [revenue](), [saas](), [saas-finance](), [subscription]() ### AI overview This guide explains marginal revenue in SaaS, including its formula, how subscription pricing affects it, and why SaaS marginal costs can remain relatively low as customer volume grows. It also discusses discounting, tier design, and the difference between marginal revenue and marginal contribution. ### Source excerpt Marginal revenue explained for SaaS founders. Formula, why SaaS marginal cost is near zero, and pricing implications when one more customer costs you almost nothing. ## Working Capital Formula for SaaS: Why Negative is Actually Good DevFeed: [Working Capital Formula for SaaS: Why Negative is Actually Good]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-06-04T00:00:00Z Content type: article Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [1Password in the browser]() Tags: [accounting](), [deferred](), [economics](), [guide](), [saas](), [saas-finance](), [subscription]() ### AI overview This guide explains the working capital formula for SaaS businesses and why negative working capital can support growth. It describes how upfront subscription payments create deferred revenue and may fund operations without debt. ### Source excerpt Working capital formula explained for SaaS. Why negative working capital is a feature not a bug for subscription businesses, with examples and benchmarks. ## AI Wrapper Business Model: How to Price and Monetize AI Apps in 2026 DevFeed: [AI Wrapper Business Model: How to Price and Monetize AI Apps in 2026]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-06-03T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Artificial Intelligence](), [Software as a service](), [Large Language Model](), [API]() Tags: [2026](), [ai](), [api](), [cost](), [economics](), [margin](), [monetization](), [pricing](), [saas](), [usage-based-billing]() ### AI overview This guide explains how AI wrapper businesses can price and monetize products built on external LLM APIs. It focuses on usage-based pricing, token-related costs, gross-margin protection, usage tracking, billing, and payment infrastructure for sustainable AI SaaS economics. ### Source excerpt How AI wrapper businesses make money in 2026 - pricing models, margin protection, usage-based billing for token costs, and the playbook for sustainable AI SaaS economics. ## How AI Changes the Role of Applied Scientists DevFeed: [How AI Changes the Role of Applied Scientists]() Original publisher: [Read original article]() Author: Tilman Drerup Published: 2026-05-22T17:40:32Z Content type: article Language: en Sources: [Instacart]() Topics: [Artificial Intelligence](), [Data Science](), [AI, ML & Data Engineering]() Tags: [ai](), [analysis](), [applied-science](), [artificial-intelligence](), [changes](), [coding](), [economics](), [machine-learning](), [math](), [statistics]() ### AI overview Instacart's Economics Team examines how artificial intelligence is changing the work of applied scientists. The article frames the role as a bundle of tasks and proposes analyzing changes in the team's project portfolio from 2023 onward, with potentially larger effects on coding than on causal inference. ### Source excerpt Levi Boxell, Tilman Drerup, Alexandr Lenk The Economics Team at Instacart is an applied science team that operates at the intersection of machine learning engineering and economics. Similar to other applied science teams, our work involves a good chunk of engineering, steeped in statistics, math, theory, and strategy. And while that is still at the heart of what we do today, the surprisingly rapid emergence of artificial intelligence has also fundamentally altered our work in ways that we did not see coming. With this post, we want to provide a brief check-in and share an analysis of the patterns we are seeing from a distinctly economic perspective. To do so, we analyze the empirical dynamics of our project portfolio between 2023 and today, looking at the evolution of both the nature and quantity of our work over time. To start, let's have a quick refresher of what economists at Instacart do and provide a theoretical framework to think about the impact of technological change through AI. Background & Theoretical Framework At Instacart, economists spend their day-to-day on a diverse portfolio of tasks and activities. Similar to other applied science teams within the company, our work relies on a blend of skills, including economics, statistics, math, machine learning, data manipulation, coding, and AI. Due to this versatility in tasks, the team's work provides a particularly rich testing ground for predictions derived from economic theories concerning the impact of technological change. But what does economic theory actually tell us? A useful theoretical abstraction for an applied scientist's role is to frame it as a bundle of tasks (Autor, Levy, and Murnane, 2003), with each task characterized by its own production function (Acemoglu and Autor, 2011). Slightly simplified, a production function tells us how much output we can produce for a given level of input in a specific task. Comparisons of production functions across tasks in turn determine how we allocate our t ## Claude Code and Margin Pass Through: Pricing Lessons for AI Products DevFeed: [Claude Code and Margin Pass Through: Pricing Lessons for AI Products]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-19T00:00:00Z Content type: opinion Language: en Sources: [Dodo Payments Blog]() Topics: [Claude Code](), [Artificial Intelligence](), [Frontier Model](), [Software as a service](), [developer tooling]() Tags: [agent](), [ai](), [business](), [claude](), [claude-code](), [cost](), [developer-tooling](), [economics](), [margin](), [pricing](), [saas]() ### AI overview The article examines pass-through pricing for AI products, using Claude Code as an example. It explains how charging customers at or near underlying model costs differs from applying a larger markup, and discusses when each approach may fit products with different levels of added value. ### Source excerpt How Claude Code and similar AI products handle pass through pricing of model costs. When pass through works, when it doesn't, and what to charge instead. ## AI IDE Billing: Lessons from Cursor and the New Wave DevFeed: [AI IDE Billing: Lessons from Cursor and the New Wave]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-19T00:00:00Z Content type: article Language: en Sources: [Dodo Payments Blog]() Topics: [Artificial Intelligence](), [cursor](), [Large Language Model](), [long-context](), [Software as a service]() Tags: [ai](), [article](), [cursor](), [developer-tools](), [economics](), [llm](), [long-context](), [pricing](), [saas]() ### AI overview This article examines billing strategies used by AI integrated development environments such as Cursor. It discusses subscription pricing, usage-based overage, pricing transparency, and the challenge of managing high and variable LLM costs. ### Source excerpt What AI IDEs like Cursor reveal about billing AI products. Subscription anchors, usage-based overage, transparency, and avoiding surprise bills. ## SaaS Gross Margin: How to Calculate and What Good Looks Like DevFeed: [SaaS Gross Margin: How to Calculate and What Good Looks Like]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-12T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Finance](), [Software]() Tags: [churn](), [cost](), [economics](), [finance](), [forecasting](), [growth](), [how-to](), [pricing](), [revenue](), [saas](), [saas-finance](), [subscriptions]() ### AI overview A practical guide to SaaS gross margin explains how to calculate the metric, which costs belong in cost of revenue, why margin affects growth and business quality, and how teams can improve it without damaging growth. ### Source excerpt Learn how to calculate SaaS gross margin, which costs belong in cost of revenue, and what margin ranges are healthy for different stages of a software business. ## AI Pricing Models: How to Price AI Products and APIs in 2026 DevFeed: [AI Pricing Models: How to Price AI Products and APIs in 2026]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-03-27T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Artificial Intelligence](), [AI Strategy](), [Software as a service](), [API]() Tags: [ai](), [apis](), [economics](), [implementation](), [pricing](), [pricing-strategy](), [saas](), [unit-economics]() ### AI overview This guide explains five pricing models for AI products and APIs: per-token, per-query, credit-based, seat-plus-usage hybrid, and outcome-based pricing. It connects model choice to inference-cost variability and customer type, and discusses unit economics, credit systems, and usage-based billing implementation. ### Source excerpt Guide to pricing AI products. Covers per-token, credit-based, and hybrid pricing models with unit economics calculations and implementation examples. ## Surprising Scalability of Multitenancy DevFeed: [Surprising Scalability of Multitenancy]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2023-03-23T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Multitenancy](), [Scalability](), [Amazon S3](), [Cloud]() Tags: [architecture](), [cloud](), [cost](), [economics](), [multitenancy](), [performance](), [s3](), [scalability](), [storage](), [systems]() ### AI overview The article explains how multi-tenancy improves the economics and scalability of cloud systems by reducing the overall peak-to-average traffic ratio. Using Amazon S3 as an example, it describes how distributing customer workloads across many storage devices can support high individual workload peaks without making the overall system disproportionately expensive. ### Source excerpt Surprising Scalability of Multitenancy When most folks talk about the economics of cloud systems, their focus is on automatically scaling for long-term seasonality: changes on the order of days (fewer people buy things at night), weeks (fewer people visit the resort on weekdays), seasons, and holidays. Scaling for this kind of seasonality is useful and important, but there's another factor that can be even more important and is often overlooked: short-term peak-to-average. Roughly speaking, the cost of a system scales with its (short-term1) peak traffic, but for most applications the value the system generates scales with the (long-term) average traffic. The gap between "paying for peak" and "earning on average" is critical to understand how the economics of large-scale cloud systems differ from traditional single-tenant systems. Why is it important? It's important because multi-tenancy (i.e. running a lot of different workloads on the same system) very effectively reduces the peak-to-average ratio that the overall system sees. This is highly beneficial for two reasons. The first-order reason is that it improves the economics of the underlying system, by bringing costs (proportional to peak) closer to value (proportional to average). The second-order benefit, and the one that is most directly beneficial to cloud customers, is that it allows individual workloads to have higher peaks without breaking the economics of the system. Most people would call that scalability. Example 1: S3 Earlier this month, Andy Warfield from the S3 team did a really fun talk at OSDI'23 about his experiences working on S3. There's a lot of gold in his talk, but there's one point he made that I think is super important, and worth diving deeper into: heat management and multi-tenancy. Here's the start of the relevant bit on heat2 management: Andy makes a lot of interesting point here, but the key one has got to do with the difference between the per object heat distribution, the per aggregat ## Surprising Economics of Load-Balanced Systems DevFeed: [Surprising Economics of Load-Balanced Systems]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2020-08-06T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Latency](), [systems](), [Server](), [Back end](), [client]() Tags: [backend](), [economics](), [latency](), [load-balancer](), [queuing](), [server](), [servers](), [systems]() ### AI overview The article examines an M/M/c queuing system in which single-request servers sit behind a load balancer with an infinite queue. Using Erlang's C formula, it explains that increasing the number of servers while proportionally increasing load reduces queuing and causes mean client-observed latency to asymptotically approach the one-second service time. ### Source excerpt Surprising Economics of Load-Balanced Systems The M/M/c model may not behave like you expect. I have a system with c servers, each of which can only handle a single concurrent request, and has no internal queuing. The servers sit behind a load balancer, which contains an infinite queue. An unlimited number of clients offer c * 0.8 requests per second to the load balancer on average. In other words, we increase the offered load linearly with c to keep the per-server load constant. Once a request arrives at a server, it takes one second to process, on average. How does the client-observed mean request time vary with c? Option A is that the mean latency decreases quickly, asymptotically approaching one second as c increases (in other words, the time spent in queue approaches zero). Option B is constant. Option C is a linear improvement, and D is a linear degradation in latency. Which curve do you, intuitively, think that the latency will follow? I asked my Twitter followers the same question, and got an interestingly mixed result: Breaking down the problem a bit will help figure out which is the right answer. First, names. In the terminology of queue theory, this is an M/M/c queuing system: Poisson arrival process, exponentially distributed client service time, and c backend servers. In teletraffic engineering, it's Erlang's delay system (or, because terminology is fun, M/M/n). We can use a classic result of queuing theory to analyze this system: Erlang's C formula E2,n(A), which calculates the probability that an incoming customer request is enqueued (rather than handled immediately), based on the number of servers (n aka c), and the offered traffic A. For the details, see page 194 of the Teletraffic Engineering Handbook. Here's the basic shape of the curve (using our same parameters): Follow the blue line up to half the saturation point, at 2.5 rps offered load, and see how the probability is around 13%. Now look at the purple line at half its saturation point, at 5 ## Valuing CyberSecurity Research Datasets DevFeed: [Valuing CyberSecurity Research Datasets]() Original publisher: [Read original article]() Author: Adam Published: 2019-07-26T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [datasets](), [data](), [Cybersecurity]() Tags: [data](), [datasets](), [dhs](), [economics](), [research](), [workshop]() ### AI overview The article discusses a paper evaluating the value of the IMPACT cybersecurity data-sharing platform at DHS and examining how data availability shapes research. It highlights research-data benefits, including scientific progress and broader access to ground truth, alongside barriers such as legal and ethical risks, costs, uncertain value, and weak incentives. ### Source excerpt A paper at the Workshop on the Economics of Information Security titled "Valuing CyberSecurity Research Datasets" focuses on the value of the IMPACT data sharing platform at DHS, and how the availability of data shapes research. ## Serial Dictatorships and House Allocation DevFeed: [Serial Dictatorships and House Allocation]() Original publisher: [Read original article]() Published: 2015-10-26T11:08:00Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Algorithms](), [Computer science](), [Mathematics]() Tags: [algorithms](), [code](), [computer-science](), [economics](), [github](), [mathematics](), [mechanism-design](), [pareto-optimality](), [programming](), [python](), [stable-marriages]() ### AI overview This tutorial connects stable marriages, kidney exchanges, and school allocation through mechanism design. It introduces allocation problems and begins formalizing house allocation using agents, houses, preferences, and one-to-one matchings. ### Source excerpt I was recently an invited speaker in a series of STEM talks at Moraine Valley Community College. My talk was called "What can algorithms tell us about life, love, and happiness?" and it's on Youtube now so you can go watch it. The central theme of the talk was the lens of computation, that algorithms and theoretical computer science can provide new and novel explanations for the natural phenomena we observe in the world. ## Stable Marriages and Designing Markets DevFeed: [Stable Marriages and Designing Markets]() Original publisher: [Read original article]() Published: 2014-04-02T18:21:55Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Algorithm](), [Computing](), [GitHub]() Tags: [algorithm](), [bipartite-graphs](), [code](), [computing](), [course](), [download](), [economics](), [github](), [internships](), [matchings](), [mathematics](), [network-science](), [programming](), [python](), [stable-marriage]() ### AI overview This tutorial explains the stable marriage problem, presents the classical algorithm for constructing stable marriages, and proves its correctness. It also discusses a polygamous generalization and applies it to assigning students to internships. ### Source excerpt Here is a fun puzzle. Suppose we have a group of 10 men and 10 women, and each of the men has sorted the women in order of their preference for marriage (that is, a man prefers to marry a woman earlier in his list over a woman later in the list). Likewise, each of the women has sorted the men in order of marriageability. We might ask if there is any way that we, the omniscient cupids of love, can decide who should marry to make everyone happy.