SaaS is for Suckers: AI Already Builds Better Custom Software

It used to be that every company faced a definitive choice: buy or build. For most, the answer was to buy. Software-as-a-Service (SaaS) dominated because it was cheaper, faster, and easier than building proprietary software from scratch. But things have changed now that many tasks within SaaS companies and many features within SaaS products can now be completed by your own AI. Today, more companies are realizing that building, once seen as prohibitively expensive, is not just viable—it’s becoming the superior option—offering more customization and significantly lower labor costs. Klarna and IBM: The Corporate Shift From Labor to AI Has Already Begun Look at Klarna. The fintech giant recently replaced significant portions of its workforce with AI-driven automation. IBM did something similar, cutting hiring for roles such as customer support agents, data entry clerks, financial analysts, software testers, and even junior developers, which they knew would soon be automated by AI. But it’s not just these high-profile examples. This shift is happening across industries like finance, healthcare, retail, and manufacturing, driven by the simple fact that AI is reducing the complexity of software development. The key reason SaaS took off was that building software was difficult, requiring large teams of engineers, months (if not years) of development, and significant ongoing maintenance. Now, AI is significantly lowering those barriers, making development more accessible while still requiring ongoing maintenance and updates. Companies no longer need to license software for every function because they can generate their own AI-powered solutions dynamically. For example, AI-driven automation is eating into Salesforce’s subscription market cap as companies build their own custom CRM tools, reducing dependence on third-party SaaS providers. The reliance on third-party software vendors is diminishing, and enterprises that recognize this early are the ones getting ahead. The New Buy vs. Build Debate Buying SaaS used to make sense because it let companies focus on their core business instead of wasting time building and maintaining internal tools. Salesforce, HubSpot, and Workday grew by convincing enterprises that outsourcing software to specialists was the smart move. But AI changes the game. Now, building isn’t just cheaper—it’s better. Here’s why: Customization: AI enables businesses to tailor software precisely to their needs rather than force-fitting workflows into rigid SaaS platforms. For instance, companies like Netflix are leveraging AI to create highly customized content recommendation engines rather than relying on off-the-shelf solutions. Cost Savings: Once AI-powered tools are set up, they can operate with minimal human intervention, reducing ongoing licensing and staffing costs. Tesla, for example, has reduced reliance on third-party software vendors by developing AI-driven automation for manufacturing and quality control. Faster Iteration: Enterprises can update and refine AI-driven tools on demand rather than waiting for vendors to release updates. Amazon continuously refines its AI-driven logistics and warehouse management systems to improve efficiency without waiting for external software updates. Data Ownership: Companies no longer need to send valuable proprietary data to third-party vendors. Apple has emphasized data privacy by developing its own AI models for on-device processing, reducing reliance on external cloud-based AI services. For decades, enterprises made peace with SaaS limitations, but AI reduces the need for those compromises. The Infrastructure Advantage: Who Wins? So if AI is making custom software development easier and more attractive than SaaS, who benefits the most? The answer lies in infrastructure. In the early days of the cloud, companies realized that managing their own data centers was inefficient. AWS, Google Cloud, and Microsoft Azure took over, turning infrastructure into a commodity and becoming the backbone of the Internet. Now, a similar battle is playing out in AI. The companies that win will be those that control AI compute infrastructure. OpenAI, Anthropic, and other model providers are competing for dominance, but their fate ultimately depends on access to the real power brokers: those who control chips, energy, and data centers. Nvidia, Intel, and AMD own the GPU market. AWS, Google, and Microsoft control hyperscale cloud computing. Enterprises that recognize this are increasingly bypassing external AI providers and optimizing their own AI stacks. The closer they are to the infrastructure layer, the more leverage they have. SaaS Vendors Are in a Whole Lotta’ Trouble SaaS companies should be worried. The economic model that made them dominant—recurring revenue through subscription licensing—relies on the assumption that building software is too costly for most businesses. That assumption is breaking down. Take enterprise automation tools like Zapier or UIPath. Five years ago, companies paid them hefty fees for automation. Today, those same companies can use AI to write scripts that handle automation on demand, tailored to their specific workflows. CRM platforms, ERP systems, and customer service software all face the same challenge: AI enables companies to replace generic, expensive SaaS solutions with custom-built, AI-powered tools that are cheaper and more effective. This isn’t just a small shift. It’s an existential crisis for SaaS vendors who have spent years building walled gardens around their platforms. When enterprises no longer need to play inside those walls, the foundations of the SaaS business model begin to erode. The New Enterprise Software Development Playbook Forward-thinking enterprises are already adjusting. They’re reducing reliance on third-party SaaS vendors and investing in AI-powered internal tools. Here’s what the winning strategy looks like: Control Compute: Companies are moving from renting AI services to controlling their own compute resources. For example, OpenAI has begun building its own AI supercomputers to reduce reliance on external cloud providers. AI-Native Development: Instead of licensing SaaS, enterprises are using AI to generate and maintain their own software dynamically. Shopify, for example, has implemented AI-driven code generation to streamline backend development and reduce dependency on third-party software solutions. API-First Thinking: Businesses are integrating AI into their core workflows through APIs rather than relying on bloated SaaS interfaces. Stripe has heavily invested in AI-powered financial APIs to enhance fraud detection and transaction efficiency. Security & Compliance: By keeping AI workloads in-house, enterprises avoid the compliance risks of sending sensitive data to third-party SaaS platforms. JPMorgan Chase has developed proprietary AI-driven financial analytics platforms to maintain data security and regulatory compliance. The smartest companies aren’t just replacing SaaS—they’re designing their entire tech stacks around AI-native architectures. The end goal isn’t just cost-cutting; it’s agility, security, and ownership. AI to Consume SaaS and Beyond SaaS isn’t going to disappear overnight, with the market cap of the global SaaS industry currently exceeding $3 trillion, but its dominance is slipping. AI is changing the economics of software development, making in-house solutions not just viable, but preferable. Companies that recognize this now will gain an edge. But beyond simply taking market share from SaaS, AI is expanding the overall market by creating efficiencies that reduce labor hours and unlock new economic opportunities. Instead of just shifting dollars from software subscriptions to AI-powered internal solutions, businesses are using AI to optimize processes, automate routine tasks, and augment human capabilities. This shift not only lowers operational costs but also increases productivity, allowing companies to scale faster and compete more effectively. Tesla has developed in-house AI-powered automation to streamline manufacturing, JPMorgan Chase has built proprietary AI-driven financial analytics platforms, and Netflix has created its own AI-enhanced content recommendation system—each reducing reliance on third-party SaaS providers. Those who continue relying on SaaS vendors may find themselves constrained by costly and less adaptable solutions. The lesson from past tech revolutions is clear: the real power belongs to those who control the infrastructure. Just as cloud computing reshaped enterprise IT, AI is reshaping enterprise software. The winners won’t be the SaaS providers of yesterday. They’ll be the companies that own their AI stacks, control their compute, and move faster than the competition. If you’re still debating buy vs. build, the answer is already clear: AI makes building the better choice. It used to be that every company faced a definitive choice: buy or build. For most, the answer was to buy. Software-as-a-Service (SaaS) dominated because it was cheaper, faster, and easier than building proprietary software from scratch. But things have changed now that many tasks within SaaS companies and many features within SaaS products can now be completed by your own AI. Today, more companies are realizing that building, once seen as prohibitively expensive, is not just viable—it’s becoming the superior option—offering more customization and significantly lower labor costs. Klarna and IBM: The Corporate Shift From Labor to AI Has Already Begun Look at Klarna. The fintech giant recently replaced significant portions of its workforce with AI-driven automation. IBM did something similar, cutting hiring for roles such as customer support agents, data entry clerks, financial analysts, software testers, and even junior developers, which they knew would soon be automated by AI. But it’s not just these high-profile examples. This shift is happening across industries like finance, healthcare, retail, and manufacturing, driven by the simple fact that AI is reducing the complexity of software development. The key reason SaaS took off was that building software was difficult, requiring large teams of engineers, months (if not years) of development, and significant ongoing maintenance. Now, AI is significantly lowering those barriers, making development more accessible while still requiring ongoing maintenance and updates. Companies no longer need to license software for every function because they can generate their own AI-powered solutions dynamically. For example, AI-driven automation is eating into Salesforce’s subscription market cap as companies build their own custom CRM tools, reducing dependence on third-party SaaS providers. The reliance on third-party software vendors is diminishing, and enterprises that recognize this early are the ones getting ahead. The New Buy vs. Build Debate Buying SaaS used to make sense because it let companies focus on their core business instead of wasting time building and maintaining internal tools. Salesforce, HubSpot, and Workday grew by convincing enterprises that outsourcing software to specialists was the smart move. But AI changes the game. Now, building isn’t just cheaper—it’s better. Here’s why: Customization: AI enables businesses to tailor software precisely to their needs rather than force-fitting workflows into rigid SaaS platforms. For instance, companies like Netflix are leveraging AI to create highly customized content recommendation engines rather than relying on off-the-shelf solutions. Cost Savings: Once AI-powered tools are set up, they can operate with minimal human intervention, reducing ongoing licensing and staffing costs. Tesla, for example, has reduced reliance on third-party software vendors by developing AI-driven automation for manufacturing and quality control. Faster Iteration: Enterprises can update and refine AI-driven tools on demand rather than waiting for vendors to release updates. Amazon continuously refines its AI-driven logistics and warehouse management systems to improve efficiency without waiting for external software updates. Data Ownership: Companies no longer need to send valuable proprietary data to third-party vendors. Apple has emphasized data privacy by developing its own AI models for on-device processing, reducing reliance on external cloud-based AI services. Customization: AI enables businesses to tailor software precisely to their needs rather than force-fitting workflows into rigid SaaS platforms. For instance, companies like Netflix are leveraging AI to create highly customized content recommendation engines rather than relying on off-the-shelf solutions. Customization: AI enables businesses to tailor software precisely to their needs rather than force-fitting workflows into rigid SaaS platforms. For instance, companies like Netflix are leveraging AI to create highly customized content recommendation engines rather than relying on off-the-shelf solutions. Customization: Cost Savings: Once AI-powered tools are set up, they can operate with minimal human intervention, reducing ongoing licensing and staffing costs. Tesla, for example, has reduced reliance on third-party software vendors by developing AI-driven automation for manufacturing and quality control. Cost Savings: Once AI-powered tools are set up, they can operate with minimal human intervention, reducing ongoing licensing and staffing costs. Tesla, for example, has reduced reliance on third-party software vendors by developing AI-driven automation for manufacturing and quality control. Cost Savings: Faster Iteration: Enterprises can update and refine AI-driven tools on demand rather than waiting for vendors to release updates. Amazon continuously refines its AI-driven logistics and warehouse management systems to improve efficiency without waiting for external software updates. Faster Iteration: Enterprises can update and refine AI-driven tools on demand rather than waiting for vendors to release updates. Amazon continuously refines its AI-driven logistics and warehouse management systems to improve efficiency without waiting for external software updates. Faster Iteration: Data Ownership: Companies no longer need to send valuable proprietary data to third-party vendors. Apple has emphasized data privacy by developing its own AI models for on-device processing, reducing reliance on external cloud-based AI services. Data Ownership: Companies no longer need to send valuable proprietary data to third-party vendors. Apple has emphasized data privacy by developing its own AI models for on-device processing, reducing reliance on external cloud-based AI services. Data Ownership: For decades, enterprises made peace with SaaS limitations, but AI reduces the need for those compromises. The Infrastructure Advantage: Who Wins? So if AI is making custom software development easier and more attractive than SaaS, who benefits the most? The answer lies in infrastructure. In the early days of the cloud, companies realized that managing their own data centers was inefficient. AWS, Google Cloud, and Microsoft Azure took over, turning infrastructure into a commodity and becoming the backbone of the Internet. Now, a similar battle is playing out in AI. The companies that win will be those that control AI compute infrastructure. OpenAI, Anthropic, and other model providers are competing for dominance, but their fate ultimately depends on access to the real power brokers: those who control chips, energy, and data centers. Nvidia, Intel, and AMD own the GPU market. AWS, Google, and Microsoft control hyperscale cloud computing. Enterprises that recognize this are increasingly bypassing external AI providers and optimizing their own AI stacks. The closer they are to the infrastructure layer, the more leverage they have. SaaS Vendors Are in a Whole Lotta’ Trouble SaaS companies should be worried. The economic model that made them dominant—recurring revenue through subscription licensing—relies on the assumption that building software is too costly for most businesses. That assumption is breaking down. Take enterprise automation tools like Zapier or UIPath. Five years ago, companies paid them hefty fees for automation. Today, those same companies can use AI to write scripts that handle automation on demand, tailored to their specific workflows. CRM platforms, ERP systems, and customer service software all face the same challenge: AI enables companies to replace generic, expensive SaaS solutions with custom-built, AI-powered tools that are cheaper and more effective. This isn’t just a small shift. It’s an existential crisis for SaaS vendors who have spent years building walled gardens around their platforms. When enterprises no longer need to play inside those walls, the foundations of the SaaS business model begin to erode. The New Enterprise Software Development Playbook Forward-thinking enterprises are already adjusting. They’re reducing reliance on third-party SaaS vendors and investing in AI-powered internal tools. Here’s what the winning strategy looks like: Control Compute: Companies are moving from renting AI services to controlling their own compute resources. For example, OpenAI has begun building its own AI supercomputers to reduce reliance on external cloud providers. AI-Native Development: Instead of licensing SaaS, enterprises are using AI to generate and maintain their own software dynamically. Shopify, for example, has implemented AI-driven code generation to streamline backend development and reduce dependency on third-party software solutions. API-First Thinking: Businesses are integrating AI into their core workflows through APIs rather than relying on bloated SaaS interfaces. Stripe has heavily invested in AI-powered financial APIs to enhance fraud detection and transaction efficiency. Security & Compliance: By keeping AI workloads in-house, enterprises avoid the compliance risks of sending sensitive data to third-party SaaS platforms. JPMorgan Chase has developed proprietary AI-driven financial analytics platforms to maintain data security and regulatory compliance. Control Compute: Companies are moving from renting AI services to controlling their own compute resources. For example, OpenAI has begun building its own AI supercomputers to reduce reliance on external cloud providers. Control Compute: Companies are moving from renting AI services to controlling their own compute resources. For example, OpenAI has begun building its own AI supercomputers to reduce reliance on external cloud providers. Control Compute: AI-Native Development: Instead of licensing SaaS, enterprises are using AI to generate and maintain their own software dynamically. Shopify, for example, has implemented AI-driven code generation to streamline backend development and reduce dependency on third-party software solutions. AI-Native Development: Instead of licensing SaaS, enterprises are using AI to generate and maintain their own software dynamically. Shopify, for example, has implemented AI-driven code generation to streamline backend development and reduce dependency on third-party software solutions. AI-Native Development: API-First Thinking: Businesses are integrating AI into their core workflows through APIs rather than relying on bloated SaaS interfaces. Stripe has heavily invested in AI-powered financial APIs to enhance fraud detection and transaction efficiency. API-First Thinking: Businesses are integrating AI into their core workflows through APIs rather than relying on bloated SaaS interfaces. Stripe has heavily invested in AI-powered financial APIs to enhance fraud detection and transaction efficiency. API-First Thinking: Security & Compliance: By keeping AI workloads in-house, enterprises avoid the compliance risks of sending sensitive data to third-party SaaS platforms. JPMorgan Chase has developed proprietary AI-driven financial analytics platforms to maintain data security and regulatory compliance. Security & Compliance: By keeping AI workloads in-house, enterprises avoid the compliance risks of sending sensitive data to third-party SaaS platforms. JPMorgan Chase has developed proprietary AI-driven financial analytics platforms to maintain data security and regulatory compliance. Security & Compliance: The smartest companies aren’t just replacing SaaS—they’re designing their entire tech stacks around AI-native architectures. The end goal isn’t just cost-cutting; it’s agility, security, and ownership. AI to Consume SaaS and Beyond SaaS isn’t going to disappear overnight, with the market cap of the global SaaS industry currently exceeding $3 trillion, but its dominance is slipping. AI is changing the economics of software development, making in-house solutions not just viable, but preferable. Companies that recognize this now will gain an edge. But beyond simply taking market share from SaaS, AI is expanding the overall market by creating efficiencies that reduce labor hours and unlock new economic opportunities. Instead of just shifting dollars from software subscriptions to AI-powered internal solutions, businesses are using AI to optimize processes, automate routine tasks, and augment human capabilities. This shift not only lowers operational costs but also increases productivity, allowing companies to scale faster and compete more effectively. Tesla has developed in-house AI-powered automation to streamline manufacturing, JPMorgan Chase has built proprietary AI-driven financial analytics platforms, and Netflix has created its own AI-enhanced content recommendation system—each reducing reliance on third-party SaaS providers. Those who continue relying on SaaS vendors may find themselves constrained by costly and less adaptable solutions. The lesson from past tech revolutions is clear: the real power belongs to those who control the infrastructure. Just as cloud computing reshaped enterprise IT, AI is reshaping enterprise software. The winners won’t be the SaaS providers of yesterday. They’ll be the companies that own their AI stacks, control their compute, and move faster than the competition. If you’re still debating buy vs. build, the answer is already clear: AI makes building the better choice.