The Deflationary Impact of AI on the Software Industry

Oct 1, 2025

Introduction

Software has long been viewed as a deflationary force in the economy – enabling businesses to do more with less, driving down the cost of operations, and often delivering greater value at lower prices as it scales. As ServiceNow CEO Bill McDermott declared during the recent inflationary spike, “Software is the most deflationary force in the world,” a sentiment echoed by Microsoft CEO Satya Nadella who noted that “software is ultimately the biggest deflationary force businesses can use”[1]. These industry leaders underscore a key economic reality: technology and software tend to exert downward pressure on costs and prices through efficiency and scalability. Indeed, the price of computing and software has plummeted over time – U.S. data show that computer and software products are roughly 74% cheaper today than 25 years ago[2]. This historical trend of “software deflation” reflects how digital products, with near-zero marginal cost and continual improvements, have delivered exponentially more capability for each dollar spent.

Today, the emergence of artificial intelligence (AI) is massively accelerating this deflationary effect. Generative AI and machine learning are supercharging software productivity and automating tasks that once required extensive human labor. The result is not only lower development costs for software creators, but also compressed pricing across software categories, as AI-fueled alternatives undercut traditional products. Entire software business models – particularly the ubiquitous subscription-based SaaS (Software-as-a-Service) model – are being disrupted by AI, which can often replicate or integrate multiple services at a fraction of the cost. Meanwhile, AI-driven productivity gains, while a boon to output, are shrinking demand for certain labor-intensive services (like outsourcing and routine IT support) as algorithms take over repetitive knowledge work.

This paper examines how AI acts as a deflationary accelerant in the software industry. We begin with a historical perspective on why software has inherently been deflationary in nature. We then delve into the specific ways AI is turbocharging cost reductions and price pressures: by reducing software development costs, compressing software pricing, disrupting the traditional SaaS model, and boosting productivity in a way that reduces the need for various services. Throughout, we draw on expert opinions, case studies, market data, and macroeconomic analysis to illustrate these trends. In closing, we consider the implications for software company executives and investors – from the challenge of maintaining pricing power to the opportunities of embracing AI-driven efficiency. The goal is to provide a comprehensive, analytical view of why AI is accelerating deflation in software, and what that means for the industry’s future.

Software as a Deflationary Force: A Historical Perspective

Software has historically exerted deflationary pressure on costs and prices across many industries. Unlike physical goods, software can be replicated and distributed at near-zero marginal cost once developed, which inherently drives down the unit cost of software functionality over time. As usage scales, providers often charge less per user or per transaction, and open-source or free alternatives proliferate, pushing prices lower. The result is that technological advancement has consistently delivered more value for less expense to end-users. For example, consumer electronics and IT services have seen dramatic price declines; the U.S. Bureau of Labor Statistics finds that while overall consumer prices rose in recent decades, the price index for computer and software products fell by roughly 74% from 1997 to 2022[2]. In plain terms, capabilities that might have cost \$100 in the late 1990s now cost only \$26 or so – a testament to how innovation makes technology cheaper and more powerful.

Several factors explain why software tends to be deflationary by nature:

Scalability and Low Marginal Costs: Once software is written, deploying it to additional users or customers is extremely low-cost. Cloud computing and app stores enable global distribution with minimal friction. This scalability means prices can fall rapidly as user bases grow, or as competitors offer cheaper mass-market solutions. Indeed, cloud software providers emphasize lower total cost of ownership as a benefit; as Satya Nadella observed, cloud technology helps companies “do more with fewer resources”, highlighting efficiency as a core value proposition of modern software services[3].
Productivity and Automation: Software automates manual processes, which reduces the labor and time required for various tasks. When businesses adopt software – from basic spreadsheet programs to advanced enterprise resource planning – they often cut costs in other areas (labor, errors, delays), effectively reducing the price of achieving a given outcome. Microsoft’s CEO has called software a deflationary tool for businesses because it “helps companies do more with fewer resources”[3]. Over decades, software-driven automation has eliminated or streamlined clerical work, supply chain management, customer service, and more, saving money and lowering the cost of services for end consumers.
Competitive Innovation and Open Source: The software industry is highly dynamic – new entrants and open-source projects continually emerge with innovative solutions that undercut incumbents. A successful software product often inspires lower-cost competitors or free alternatives. For instance, what begins as a premium proprietary software can face competition from an open-source equivalent, forcing price reductions or even shifting the product to a free or freemium model. The widespread availability of high-quality open-source software (databases, operating systems, etc.) has eroded pricing power for many traditional vendors over time, contributing to deflationary trends in software pricing.
Economic Deflation vs. Value Increase: It’s important to note that the deflation caused by software is generally a “good” deflation, driven by improved productivity and efficiency (as opposed to a collapse in demand). Customers get more for their money each year. As one BlackRock analysis put it, technology like AI enables “doing more with far less,” pushing unit costs down while output rises[4][5]. In economic terms, this is disinflationary growth – a scenario where innovation boosts GDP and productivity while reducing cost pressures[6]. Indeed, analysts highlight that we may be entering a rare period where new tech lifts productivity and profitability while actually lowering inflation[7].

In short, software’s core economics – high initial development cost but nearly zero cost per additional copy – mean that as software spreads, the effective price per user or per transaction tends to drop. History is replete with examples: the cost of data storage and processing plummeted with each generation of software and hardware; communication costs dropped with internet software; even sectors like entertainment saw distribution costs approach zero with streaming software platforms. It’s no wonder that during recent inflationary concerns, tech leaders argued that investing in software is a way to “deflate” rising costs[1]. As the next sections will explore, the advent of advanced AI is amplifying all of these deflationary mechanisms within the software industry itself.

AI as a Massive Deflationary Accelerant

If software in general has been deflationary, artificial intelligence is like throwing fuel on that fire. Generative AI and related technologies are accelerating cost reductions and productivity gains at an unprecedented pace, fundamentally reshaping the software industry’s economics. In this section, we analyze four major dimensions of AI’s deflationary impact on software:

Reduced Software Development Costs: AI tools are automating and speeding up programming and software creation, dramatically lowering the cost and time needed to develop software.
Pricing Compression Across Software Categories: As AI makes it easier to build alternatives (or bundles functionality), competition increases and pricing power erodes, compressing prices in many software markets.
Disruption of Traditional SaaS Business Models: AI is changing how software is delivered and monetized, challenging the classic SaaS subscription model and enabling new AI-centric models that often charge less or bundle more.
Productivity Boosts & Shrinking Service Demand: AI boosts worker productivity (a positive), but in doing so it reduces the need for certain jobs and services (from coding roles to IT outsourcing and support), effectively deflating demand in those areas.

Let’s examine each of these in detail with examples and data.

AI-Driven Efficiency in Development Lowers Costs

One of the most immediate impacts of AI in software is the dramatic reduction in development effort and cost. Traditional software development is labor-intensive and time-consuming, requiring skilled engineers to write and test code line by line. Now, generative AI models (like OpenAI’s Codex powering GitHub Copilot, and similar tools from tech firms and startups) can produce code, suggest functions, and even detect bugs automatically. This is unlocking new levels of efficiency and cost-effectiveness in software development[8]. Tasks that once took days of human effort might be completed in hours or minutes by an AI assistant, meaning a smaller team of developers can accomplish what a much larger team might have done in the past.

Several data points and studies illustrate just how much AI is cutting development costs and time:

AI Writing a Large Share of Code: Microsoft’s GitHub, which offers the Copilot AI pair programmer, claims that 46% of new code on its platform is now being authored by AI[9]. In other words, nearly half of the code being written in projects using Copilot is machine-generated. This staggering figure suggests that a significant portion of a developer’s work can be offloaded to AI, allowing humans to focus on higher-level design and logic. When AI writes almost half the code, companies potentially need far fewer developer-hours (or fewer full-time engineers) to complete a project – a clear development cost reduction.
Faster Completion Times and Productivity Gains: Experimental studies confirm major productivity boosts from AI assistance. In a controlled setting, developers using AI tools have completed coding tasks over 50% faster than those without AI[10][11]. Surveys of software engineers back this up: for example, GitHub’s own research with thousands of developers found that 88% reported being more productive using AI, with some tasks completed in nearly half the time[11]. And industry surveys indicate that a large majority of developers are adopting these tools – over 85% of developers now use AI coding assistants, with organizations reporting productivity improvements on the order of 40% to 60% on average[12]. Such gains in efficiency mean that projects can be delivered faster and with less labor, saving companies significant money (and allowing quicker revenue generation from new features).
Cost Savings Estimates: Analysts have begun to quantify the direct cost impact. A 2024 study by Forrester Research suggests that by optimizing human-AI collaboration in development teams, companies can reduce software development costs by 15–20% while actually improving output quality[13]. These savings come from automation of routine coding, reduction in errors (and therefore less rework), and faster completion of projects (which cuts overhead). Some anecdotal reports are even more aggressive – for instance, startups have claimed that using end-to-end AI-driven development platforms they can build applications 3x faster and at half the cost, compared to traditional methods[14]. While individual experiences vary, the direction is clear: AI is making development cheaper and faster.

The implications of cheaper development are profound. Barriers to entry for software creation are lowering – a small startup or even a single engineer with AI assistance can build a prototype or product that might have required a whole team in the past. This means more competition in the software market (as we discuss below) and puts pressure on existing firms that have higher cost bases. For software vendors, AI offers an opportunity to improve profit margins by cutting R&D expense, but it also means that pricing models may need to change if development becomes commoditized. As Goldman Sachs noted, as generative AI makes building software easier and more pervasive, enterprises may decide they “might not have to buy so much software” from third-party vendors[9], choosing instead to generate solutions in-house. In effect, the cost savings AI provides to producers can get passed through to consumers or internal users, contributing to deflationary pressure on software prices.

In summary, AI is revolutionizing the software development workflow. Automation of coding, testing, and maintenance tasks by AI leads to significant productivity gains – doing in minutes what might have taken hours – thereby reducing the labor and time costs embedded in software products. With development costs shrinking, the economics of software will increasingly favor those who leverage AI to produce higher-quality code with smaller teams. The next consequence of this efficiency, which we turn to now, is how it compresses pricing and intensifies competition in software markets.

Pricing Compression Across Software Categories

As AI lowers the cost of creating software and even enables organizations to create their own tools, it is leading to pricing compression across many software categories. In plain terms, many types of software are likely to get cheaper for end customers, and vendors will find it harder to justify high price tags or sustain large margins. Historically, software companies have enjoyed strong pricing power in certain niches – especially in enterprise software, where switching costs are high and few alternatives exist. AI is eroding those advantages by democratizing the ability to build and by commoditizing functionality.

There are a few mechanisms by which AI is compressing software prices:

Increased Competition and Easier Substitution: When it becomes easier and cheaper to build software, more competitors can enter a market (including non-traditional ones). Additionally, customers themselves can potentially use AI and low-code tools to develop simple internal solutions, bypassing the need to buy expensive licenses. Venture capitalist David Friedberg pointed out that AI and no-code tools let companies “build their own internal software solutions at a much lower cost than purchasing comparable software from a third party.” The result, he argues, is “increased churn and pricing compression, resulting in deflationary SaaS pricing power.”[15] In other words, if your software vendor is charging too much, an AI-assisted engineer might replicate the core functionality in-house or another startup might offer a clone for less – forcing the original vendor to cut prices or lose the customer. Friedberg even gave a concrete anecdote: an engineer at one of his companies re-created a supply-chain data management tool (for which they had been paying over \$50,000 annually) using AI-assisted development, enabling them to cancel that pricey SaaS subscription[16]. They could even open-source their version or sell it cheaply, undercutting the original vendor’s market.
Erosion of Pricing Power for Packaged Software: With generative AI making custom solution development easier, the “pricing power of packaged software providers decreases,” as Goldman Sachs analysts observed[9]. Many software products – especially simpler or narrowly focused applications – risk becoming commodities. For example, if five different project management SaaS tools all provide similar AI-driven features, they may be forced to compete heavily on price, since the AI capabilities are not exclusive to any one vendor. We are already seeing signs of this: IT buyers in 2023–2024 have been tightening budgets and seeking to consolidate vendors[17], often pressuring software suppliers for discounts. If one vendor doesn’t cooperate on pricing, an alternative (perhaps a newer AI-augmented competitor) might offer a better deal. The overall effect is a secular shift toward lower software prices or more value for the same price, as customers become more price-sensitive and capable of switching.
Commoditization of Features via AI: AI is enabling one application to do the work of several. For instance, a sufficiently advanced AI system might handle customer support, basic analytics, and content generation all within one interface – tasks that previously might require separate software products. As AI platforms bundle functionalities, niche software tools face a “feature squeeze” and must compete on cost or specialism. Categories like translation software, transcription services, simple graphic design tools, etc., have already seen price drops due to AI alternatives (often free or very low-cost). When OpenAI’s ChatGPT can draft marketing copy or write Python scripts as part of a \$20/month subscription[18], it challenges the pricing of specialized copywriting software or coding IDEs. The value is shifting away from individual point solutions to broader AI platforms, which often monetize differently (e.g. usage-based pricing that tends to be economical at scale).
Open-Source and Free AI Models: Another facet of AI-driven price compression is the rise of open-source AI models. While early on, access to powerful AI (like large language models) was limited to a few companies charging premium fees, the open-source community is rapidly catching up. There are now free or low-cost models for tasks like image generation, language translation, and coding assistance. If a company must decide between paying a high fee for a proprietary AI-infused software or using an open model to power an in-house solution, the latter option is increasingly viable. This phenomenon mirrors the open-source software movement of earlier decades, but with AI it may happen even faster due to wide dissemination of research. Commoditized AI capabilities mean commoditized software pricing: for example, if dozens of vendors can all incorporate a given open-source AI model into their product, none can charge an extreme premium purely for that AI capability.

The net outcome of these trends is that software pricing is under downward pressure in many segments. We can already observe this in enterprise software dealmaking: buyers negotiate harder on renewals, sometimes citing cheaper AI-enabled competitors. In the private equity and venture space, investors are looking more skeptically at software companies that don’t have “durable, workflow-embedded” advantages – commodity apps lacking unique data or defensibility are “facing pricing compression” according to market analysts[19]. In 2025, for instance, tech market observers noted that buyers differentiate between truly mission-critical SaaS (which might maintain pricing) and those applications now seen as easily replicable with AI, which must cut prices or risk losing customers[20].

It’s important to note that pricing compression doesn’t necessarily mean all software revenue declines immediately; often the market expands in volume as prices drop (more companies can afford solutions, etc.). But it does mean thinner margins and a shift in where value accrues. Some of the value is likely shifting to the underlying AI infrastructure providers (e.g., cloud platforms and chipmakers) and to consumers who reap the benefits of cheaper software. Indeed, Goldman Sachs points out that a boom in generative AI spend might “cannibalize” budgets from traditional software toward AI infrastructure (hyperscale cloud, GPUs, etc.), squeezing the growth of the software sector itself[17]. Already, we saw in 2023–2024 a telling market comparison: Nvidia, the leading AI chip provider, briefly became the world’s most valuable company on surging AI demand, while major SaaS firms like Salesforce saw their stock prices and valuations lag, reflecting investor expectations of slower growth and lower pricing power in traditional software[21].

In summary, AI is accelerating a commoditization cycle in software. Easier creation of alternatives, internal tool development, feature-bundling by AI platforms, and open-source AI all contribute to deflationary price pressure. For software companies, this means a need to rethink value propositions and possibly pricing models. For customers and the economy, it signals more affordable software and lower costs to achieve business outcomes – a classic deflationary boon to productivity, albeit one that disrupts incumbents.

Disruption of Traditional SaaS Business Models

Perhaps the most strategic impact of AI’s rise is the disruption of the traditional SaaS model that has dominated software over the past two decades. In the conventional Software-as-a-Service paradigm, companies license cloud-based applications on a subscription (often per-user, per-month) basis. These applications usually focus on specific domains or workflows – think of a CRM for sales, an HR management tool, a project tracking app, etc. The value proposition of SaaS has been convenience, continuous updates, and specialized functionality, all accessible via a web browser.

Generative AI is upending some of the fundamental assumptions of this model. There are a few ways this disruption is playing out:

AI as an Integrated Layer Across Applications: Users increasingly expect AI capabilities everywhere – from autocompleting emails to generating reports. This is forcing SaaS vendors to incorporate AI into their offerings, often without a proportional increase in price. In effect, AI features are becoming a standard inclusion rather than a premium upsell, which changes the economics. If every CRM now includes an AI assistant to draft follow-up emails or analyze pipeline, that feature alone can’t command a higher subscription price – it’s necessary just to stay competitive. The organizing principle of software is shifting from standalone apps to AI-driven workflows that cut across applications. For example, an AI “agent” might pull data from the CRM, the project management tool, and the finance system to accomplish a task (“prepare a client update briefing”) without the user manually operating each software. This undermines the siloed nature of traditional SaaS and could reduce the time end-users spend in any single application (potentially affecting how vendors price – e.g., seat licenses may become less relevant if an AI is the primary user interfacing with the system).
Replacement of Simple SaaS by AI Solutions: Many SaaS products essentially automate or streamline relatively straightforward processes (filling forms, retrieving information, routing workflows). Generative AI threatens to replicate a lot of that functionality in a more flexible, human-like way, potentially replacing the need for certain applications altogether. A Goldman Sachs analysis observes that “relatively simple software applications could be obviated by generative-AI-powered solutions”[22]. For instance, consider an AI-driven customer service bot that can handle a wide range of inquiries and processes. It might negate the need for a company to have separate software for ticketing, knowledge base, and FAQ management – the AI taps into a database and does it all via natural conversation. Similarly, an AI content creation tool might replace multiple design and publishing apps for a small business doing basic marketing. If a single AI interface can perform tasks that previously required five different SaaS subscriptions, those subscriptions become hard to justify. This is deflationary not only in price but in the number of vendors a customer needs (vendor consolidation).
Internal Tool Development vs. SaaS Purchase: We touched on this under pricing, but it’s also a business model disruption. Traditional SaaS assumes that it’s cheaper and easier to rent software than to build it yourself. AI is changing that calculus for certain cases. If a company’s engineering team can quickly spin up an internal tool using AI (perhaps customizing open-source components) that exactly fits their need, they might prefer that over paying an ongoing subscription to a SaaS vendor – especially if the SaaS is expensive. The Friedberg anecdote about replicating a \$50k/year tool internally is a case in point[16]. This trend, if it scales, turns the clock back somewhat to custom software, but with AI making custom development far more accessible. It particularly threatens smaller SaaS companies that don’t have deep moats in terms of unique data or complexity.
Changing Value of Data and Integrations: In the AI era, a software company’s competitive advantage may shift from having the best user interface or feature set to having the best data and integration ecosystem. Incumbent SaaS firms still hold valuable proprietary data and customer relationships in their platforms – and those are not trivial to replicate. Some analysts argue that SaaS incumbents might endure if their “data gravity” remains strong (i.e. the customer’s data is locked in their system and not easily ported)[23][24]. However, even here AI is a double-edged sword: if generative AI can interface with legacy systems through APIs, it might extract or bypass data silos. Many SaaS companies are exposing APIs to let AI models interact with their software (for example, allowing a GPT-based system to query your CRM or ERP). This is great for customers, but it also means the SaaS product itself might become a background utility – essentially a database with a standardized interface – while the primary user interaction happens through a third-party AI assistant. Over time, the risk is that the SaaS becomes commoditized (just one of many back-end data sources) and the AI orchestrator captures more of the user’s attention and value.
New Pricing Models – Usage and Outcome-Based: AI services often run on usage-based pricing (e.g., paying per API call or per number of queries, rather than per seat per month). This is introducing more variable pricing schemes in software. SaaS companies accustomed to predictable subscriptions may have to adopt usage-based pricing for their AI features, which can lead to overall lower costs for efficient users (deflation for the customer who uses less). Furthermore, AI enables more outcome-based models – for instance, an AI-driven optimization service might charge a percentage of savings achieved rather than a flat fee. These models usually transfer some efficiency gains to the customer by design. In competitive markets, pricing is likely to trend downward to whatever model offers the most customer-aligned value.

Concrete examples of this disruption abound. One stark example came in the education tech sector: Chegg, a popular education SaaS (offering homework help and Q&A services), saw its stock plummet by 50% in 2023 when it revealed that ChatGPT was reducing customer growth and would likely impact its subscriber base[25]. Students found they could get answers and tutoring from a general AI (at low or no cost) rather than paying Chegg’s monthly fee, putting Chegg’s entire model in jeopardy. This kind of abrupt disruption illustrates how an AI not specifically designed to compete with a given software product can nonetheless cannibalize its value proposition through a more generalized, affordable service.

Another area is customer support software and call center solutions. AI chatbots and voice assistants are rapidly becoming capable of handling many support tasks. A senior executive at Tata Consultancy Services (a major IT firm) predicted that within a year, there would only be a “minimal” need for call centers as AI takes over basic customer service interactions[26]. If fewer human agents are needed, companies might reduce seats for support software or consolidate systems, impacting SaaS providers in that space. Similarly, AI-driven automation in IT management (like AI ops tools that auto-resolve incidents) could reduce the need for certain IT service management software licenses or modules.

In summary, the SaaS model is not dead, but it is being forced to evolve. We will likely see a split between: - “Intelligent SaaS” – traditional software enhanced by AI, offering far more value (those who succeed here will justify their fees through AI-driven capabilities, effectively giving deflationary value within their product – more output for same cost). - Commoditized services – simpler SaaS offerings that can’t differentiate on AI or data will either cut prices, merge, or fade away as open models and DIY solutions replace them.

For software company executives, this means rethinking product strategy. The moat can no longer be just a nice UI or a specialized feature; it has to be about network effects, unique data, integration depth, or AI models trained on proprietary data. Many SaaS firms are racing to launch their own AI copilots (e.g., Salesforce with its Einstein GPT, Microsoft infusing AI across Office and Dynamics) to stay relevant. The business model may shift more toward platform and usage-based revenue instead of purely per-seat subscriptions. Those who fail to adapt could see their revenue base erode in an AI-driven deflationary squeeze.

Productivity Boosts with Reduced Demand for Services

AI’s deflationary impact isn’t limited to software products – it also extends to the services and human labor associated with software and IT. In economic terms, AI is significantly boosting productivity of knowledge workers (developers, analysts, customer support reps, etc.), which is a positive for output, but the flip side is shrinking demand for certain jobs and services that were previously needed. This is a classic deflationary pattern: efficiency gains reduce the quantity (or price) of labor or service inputs required for the same result.

Several facets of this trend are worth exploring:

Lean Engineering Teams and Less Outsourcing: As discussed, AI allows smaller teams of engineers to build and maintain software. This may reduce reliance on large outsourced development projects or big in-house departments. For example, if one AI-assisted developer can do the work of, say, three traditional developers, a company may choose to hire fewer programmers or cut back on contracting. Countries that have large outsourcing industries are already sensing this shift. India’s \$280 billion IT outsourcing sector is facing a potential crisis as foreign clients adopt AI. A co-founder of a major Indian IT firm (HCL) bluntly stated that “areas like BPO and coding are in trouble and will get replaced by generative AI”, urging the industry to move up the value chain[27]. Indeed, reports indicate some global clients are billing fewer hours from Indian IT outsourcers because AI automation is handling tasks those engineers used to do[28]. The Goldman Sachs “Deflation of Software” analysis similarly predicts that demand for offshore development services may shrink, redistributing profits and jobs away from countries that specialize in providing those services[29]. In plain terms, if a U.S. company can use an AI system to handle maintenance programming or testing, it might reduce the contracts it sends to an overseas development team, leading to deflation in the outsourcing sector.
Automation of Routine IT and Support Roles: Beyond development, AI is automating many support functions – from helpdesk support (chatbots handling tier-1 queries) to infrastructure management (AI systems predicting and fixing IT incidents). This reduces the need for as many support agents or system administrators. For instance, call center operations are rapidly changing. AI voice agents and chatbots can handle large volumes of customer inquiries without human intervention. The Brookings Institution found that 86% of customer service tasks have “high automation potential”, and major BPO companies acknowledge that simpler customer inquiries are moving to self-service AI[30]. Some experts predict a “minimal” need for call center workers in the near future for basic tasks[26]. As companies implement these solutions, the cost of customer service per contact drops (deflation in service cost), but it also means potentially fewer outsourced call center contracts or smaller internal support teams. One Indian AI firm co-founder noted that all their corporate clients are asking AI solutions to “help reduce headcount”, clearly seeing labor reduction as a benefit of AI adoption[31].
Wage and Salary Pressures: With AI taking over tasks, there may be downward pressure on wages for certain roles. If, for example, one highly skilled developer with AI can replace three average developers, companies might concentrate hiring on a few top people and not need the others, or pay remaining staff relatively less because the supply of effective labor (human+AI) is higher. The Reuters analysis pointed out that “AI still poses a looming threat to workers and could drive down salaries”, especially for roles like junior lawyers, call center operators, and copywriters that AI can increasingly handle[32]. In economic terms, this expands the effective labor supply and productivity, which tends to be deflationary for labor costs (good for reducing inflation, though obviously challenging for workers in affected fields).
Macroeconomic Productivity and “Good Deflation”: On the positive side, these productivity gains mean the economy can produce more output with the same or fewer inputs. McKinsey estimates that generative AI could add about \$7.9 trillion to global GDP annually by 2030 (roughly boosting annual GDP by 7-8%)[33], thanks to lower costs and higher volumes from AI-driven productivity. This is growth with deflationary characteristics – higher output, lower price per unit. For businesses, higher productivity can mean expanded margins if they can maintain prices, or competitive advantage if they cut prices. From a macro view, if sectors like healthcare or education (historically inflationary) start to see AI-driven efficiency, it could relieve some of the cost pressure there. For instance, healthcare services are a big part of consumer expenses; one scenario suggests if AI could reduce healthcare costs by 20%, it would shave about 1.2 percentage points off U.S. inflation (CPI)[34]. This illustrates how powerful AI’s deflationary force could be in the broader economy – it’s not just tech gadgets getting cheaper, but potentially major services as well.
Corporate Restructuring and Workforce Reduction: Many companies are already planning for leaner operations due to AI. Amazon’s CEO Andy Jassy stated in mid-2023 that he expects AI will allow the company to reduce its total corporate workforce over time as efficiency gains are realized[35]. Similarly, IBM’s CEO announced a pause in hiring for certain back-office roles, estimating that roughly 7,800 jobs (about 30% of such roles) at IBM could be replaced by AI in the next 5 years[36]. These announcements are early indicators of a broader restructuring trend: repetitive, support, or lower-level analytical jobs are prime targets for AI automation. For the software industry specifically, roles like quality assurance testing, basic front-end development, or L1 support might see decreased demand. This can improve profitability or allow reallocation of talent to more creative tasks, but it also means services that were billed by the hour or by headcount (like consulting, implementation, or support contracts) may generate less revenue. A software integrator that once charged a client for 100 hours of manual data migration might now charge for 10 hours with an AI doing the heavy lifting – a win for the client’s costs (deflation), and a need for the integrator to adapt their business model.

It’s worth noting that not all effects are negative for service providers – AI is also creating new service opportunities (for example, AI integration consulting, custom AI model development, data labeling services, etc.). However, these typically require higher skill and are smaller in scale compared to the large swath of automatable work. The net near-term effect is likely a reduction in total service spend for certain routine work. Private equity investors, who often arbitrage labor costs by offshoring or streamlining operations, are now eyeing AI as another tool to cut costs in their portfolio companies. The software industry might see M&A activity where firms combine to cut redundant roles and apply AI, expecting to maintain output with fewer staff – essentially a deflationary synergy.

From a societal perspective, this productivity-driven deflation raises the classic dilemma: the gains in efficiency and lower prices benefit consumers and the economy overall, but they come with transition costs as workers and service providers adjust. Historically, each wave of automation (from looms in textiles to ATMs in banking) did lead to some job displacement but also eventually to job evolution. The pace of AI’s impact, however, is exceptionally rapid (“happening faster than most people realize,” as one outsourcing expert noted[37]), which can compress the adjustment period.

For software executives and investors, the key takeaway is that business models relying on large human labor forces or manual services are at risk. This includes not just outsourced coding or call centers, but even high-end consulting could face pressure if AI can quickly analyze systems and recommend optimizations. Companies in these spaces will need to pivot to higher-value offerings or integrate AI themselves to stay relevant. On the flip side, companies that use AI to streamline their own operations can gain a cost advantage. For instance, a software firm that uses AI for QA and customer support can operate more cheaply than competitors, potentially allowing it to lower prices or achieve better margins.

In conclusion, AI-driven productivity gains are a double-edged sword – they clearly contribute to the deflationary trend by lowering the need (and spend) on various services, even as they boost output. This is likely to lead to a redistribution of resources: savings from reduced labor costs might flow to consumers (lower prices) or to investments in new innovations. From an economic standpoint, it’s a recipe for higher efficiency and potentially lower inflation, validating those early claims by tech CEOs that technology is the antidote to cost pressures. The challenge for industry and society is managing the transition compassionately, ensuring that the “deflation dividend” from AI is channeled into new opportunities, innovation, and perhaps retraining the workforce for more complex roles that AI cannot fill.

Implications and Conclusion

Software has always been about creating abundance from ideas – building once and benefiting many – which inherently carries deflationary economics. The advent of AI has supercharged this dynamic. We are entering an era in which software can be developed, delivered, and utilized with vastly greater efficiency, at far lower cost, than previously imaginable. For software industry leaders, venture capitalists, and private equity investors, this deflationary acceleration by AI presents both significant opportunities and serious challenges.

Key implications include:

Pressure on Profit Margins and Valuations: Software companies may face diminishing pricing power as AI commoditizes features and competitors proliferate. This could lead to tighter profit margins unless costs are reduced commensurately. Investors have already started differentiating between firms – rewarding those tied to AI infrastructure (e.g., chipmakers, cloud providers) and punishing those in crowded software niches with eroding growth. We’ve seen the world’s largest SaaS firms trade at lower valuations than a year prior, while AI-focused companies skyrocketed in value[21]. Going forward, software firms will be expected to justify their business models in an AI-rich world, focusing on moats like proprietary data, deep integration, and network effects that AI alone cannot instantly replicate.
Need to Embrace AI or Fall Behind: For incumbents, adopting AI is not optional – it’s existential. Companies that leverage AI to improve their products and operations can ride the deflationary wave to outcompete rivals. Those that ignore it risk being left with outdated offerings that can’t match the cost or functionality of AI-enhanced solutions. This may spur consolidation – stronger players acquiring weaker ones to absorb their customer base and inject AI capabilities efficiently. It could also drive a shakeout of the myriad small SaaS tools on the market: as AI-enabled platforms cover more ground, customers may consolidate spend on a few AI-rich ecosystems, dropping niche tools that don’t justify separate contracts. In essence, the “organizing principles” of the software industry could change[38], with AI-centric design and economics at the core.
Customer Benefits and Shifting Value to Users: From the perspective of software consumers (businesses and individuals), the deflationary impact of AI is largely a boon. It means lower costs for software and IT services over time, or much more value at the same cost. Enterprises might spend less on software licenses by using AI to build or by negotiating better deals. Small businesses and startups benefit from cheaper tools and the ability to develop capabilities in-house. In macro terms, widespread AI adoption could help counteract inflation in other areas by lowering the cost of doing business and increasing productivity. This is one reason economists look at AI as a potential offset to the inflationary pressures of the early 2020s – a source of “good deflation” that boosts living standards[2][39]. However, customers will also have to navigate new considerations, such as the reliability and ethics of AI-driven systems, and the trade-off between using vendor-provided AI vs. building their own.
Workforce and Societal Adjustments: The deflationary acceleration comes with workforce disruption. Companies will need to manage re-skilling and transitioning employees whose roles are changed or diminished by AI. From a societal view, the challenge will be ensuring that productivity gains do not simply concentrate as higher corporate profits or lower prices alone, but also translate into new job creation in emerging fields. Historically, technology shifts do create new industries and roles – for instance, demand is rising for AI model trainers, data scientists, and AI ethicists, which were niche or non-existent roles a decade ago. The hope is that as AI handles rote work, human workers can move to more creative, complex, and value-generating tasks, ultimately expanding the economy in new directions. Policymakers and education systems will play a role in smoothing this transition. In the meantime, companies using AI aggressively should also consider the optics and responsibility – sudden large layoffs attributable to “AI efficiency” can invite backlash. A more gradual repurposing of human talent could be a win-win: keep experience in-house, but direct it toward areas where human judgment and innovation are irreplaceable.
Global and Geopolitical Shifts: A deflationary software industry could have global ripple effects. Countries that have been large exporters of software services (like India in IT outsourcing, or Eastern Europe’s developer contracting market) might face economic headwinds if that demand falls[29]. Conversely, countries or companies that invest heavily in AI might leapfrog previous leaders – for example, a nation without a huge developer workforce could nonetheless become a software powerhouse by leveraging AI tools broadly[40]. There’s also a strategic angle: if AI makes it easier for any country to produce its own software solutions, it could erode the dominance of traditional software-exporting countries (the U.S. and Western Europe). We might see more localized software ecosystems, or a push for global tech standards to prevent fragmentation[41]. For investors, this means being open to innovation hubs outside the usual hotspots, as AI “democratizes” software creation worldwide.

In closing, the deflationary impact of AI on the software industry is a story of rapid change with two sides. On one side, unprecedented efficiency, lower costs, and democratization of software capabilities – a clear gain for consumers and those companies that harness these advances. On the other side, disruption of business models, margin compression, and the need to adapt for incumbents and workers. The balance of these forces will shape the next era of software.

What’s certain is that AI’s role as an accelerant is here to stay. Marc Andreessen’s famous 2011 statement that “software is eating the world” has a 2025 addendum: AI is eating software. By massively reducing the cost to develop and deliver software, AI is ensuring that every industry (not just tech) has access to powerful software tools, often at declining prices. For software industry executives, the mandate is clear: innovate with AI or see your product become yesterday’s commodity. For investors, the task is to identify which companies will use AI to create deflationary advantages (and perhaps gain volume or market share as prices fall) versus those whose moats will be washed away in a tide of cheap, AI-generated code.

In an economic climate still recovering from inflationary shocks, the deflationary surge from AI might be the structural change that defines the coming decade. Quality of life could improve as software and services get cheaper and more capable. Companies that leverage “AI deflation” can potentially scale faster and serve global markets with lighter cost structures. In the end, the deflationary impact of AI, while challenging for some, could herald a new wave of innovation and growth – a world where software (and the intelligence built atop it) becomes ever more abundant, affordable, and impactful across the economy. As one analysis succinctly put it, “technology has always aimed to do more with less – AI is a breakthrough in doing more with far less”, yielding a scenario of “growth with deflationary tendencies”[4][6]. Navigating this scenario will be the central strategic task for the software industry in the years ahead, and those who get it right will not only survive the deflationary wave but ride it to new heights of innovation and efficiency.

Sources:

Lucas, M. (2024). The Deflation of Software. Goldman Sachs Global Institute[42][22].
Reuters Breakingviews (2023). AI’s deflationary winds will blow away profits[2][43].
Smart Industry (2023). How manufacturers can deflate inflation with software[1].
BlackRock Investment Institute (2025). AI-Driven Investing & Deflationary Growth[4][6].
Euclid Ventures (2025). Does AI Threaten Vertical SaaS?[15].
Medium – DBServices (2024). Leveraging AI for Software Engineering Productivity[13].
Bitcot (2025). How AI Tools Are Rewriting Development Workflows[12].
Washington Post (2025). AI is transforming Indian call centers[27][31].
Reuters (2023). IBM to pause hiring in plan to replace 7,800 jobs with AI[36].
Reuters (2023). Chegg shares plunge after warning on ChatGPT impact[25]. (Chegg example in Breakingviews piece)

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[36] IBM to pause hiring in plan to replace 7,800 jobs with AI, Bloomberg reports | Reuters

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