Software Century

In 1879, Gould & Curry ran Nevada's largest mining operation with 97% efficient Corliss steam engines. Electric motors were crude, unreliable, 60% as powerful. Yet within years, every steam-powered mine was electrified or extinct.

The value was never efficiency. It was architecture.

Steam required centralization. Massive engines, elaborate belt systems, entire operations built around the power source. Electricity enabled distributed power. Machines where needed. Operations designed around work, not infrastructure.

Today's enterprises perfected their steam engines. ERPs, mainframes, custom fortresses of logic achieving local maxima within a dying paradigm. Now they're installing AI chatbots like electric lights in steam factories. They wonder why nothing fundamentally changes.

The architecture is wrong. Not the technology.

The US auto industry's transformation from installation to collapse to deployment mirrors today's software paradigm shift

The Current Crisis

Software ate the world. Now it's eating you alive.

SaaS promised us simplicity, agility, efficiency. Now we house buildings of people where software drowns us with complexity, fragmentation and dependence. We work to service the software that was supposed to service us.

The average enterprise maintains 175 SaaS subscriptions. Employees spend 20-30% of their time as human duct tape. They copy and paste between systems that refuse to communicate. SaaS fragmentation isn't a bug. It's the business model. Every vendor wants their slice of recurring revenue. Wrappers masquerading as features. Pathogens sold as cures.

IT budgets dedicate 60-80% to maintaining existing systems. 57% goes to operations. Only 16% to innovation. 88% of businesses say legacy tech hampers efficiency and growth. Government systems cost $337 million annually just to maintain. The Social Security Administration still runs on 60 million lines of COBOL.

Software as a service doesn't work for organizations. Organizations work for software. Every subscription represents rent on operational inefficiency. SaaS doesn't care if organizations win. Whether productivity increases or decreases, the subscription renews. Adding AI features only amplifies this misalignment. Enterprises pay to beta-test someone else's wrapper.

You don't need to fire your employees. You need to fire your subscriptions.

The Great Bundling

SaaS gave us thousands of tools, each extracting rent for a sliver of functionality. The Great Bundling is happening as software becomes commodity.

What you pay Salesforce, Jira, DocuSign, ServiceNow, and your ERP systems will collapse into primitives generated on demand. Composed as needed. Dissolved into context when done. Each of these services becomes a function call, not a subscription.

The entire SaaS ecosystem collapses into a single runtime where intelligence orchestrates capabilities on demand. Bundle everything because unbundling was always the wrong paradigm.

AI promises to slash software development costs by up to $1.1M per engineer—if enterprises embrace the architectural revolution

The Architectural Revolution

Not agents. Not wrappers. Not features. New technology software.

The generative AI paradigm brought two fundamental shifts to software.

First: Intent replaces control. Traditional software follows predetermined paths. Modern intelligence uses late-bound control flow, powered by function calling. Path selection happens at runtime through probabilistic reasoning, not compile time. We've moved from static programs to dynamic, policy-instantiated intent graphs.

Second: Continuous software. Applications no longer need to exist until the moment of use. Just-in-time interfaces, generated for specific tasks, then dissolved. Built on runtimes with strict schemas and governance, composed to serve enterprise intent.

These aren't features. They're new physics.

AI-native software generation is fundamentally different from adding AI features to legacy systems. Calling modern capabilities an "AI-enabled feature" is like calling a vacuum cleaner an electricity-enabled broom. Technology isn't a feature anymore. It's the foundation.

The world will be AI-native by 2030. Why build like SaaS is permanant?

What Doge Proved

Enterprises burn 70% of IT budgets maintaining systems built for the 2000s. Major banks, similar percentages. The Social Security Administration runs 60 million lines of COBOL. Federal IT systems average 25 years old, costing $337 million annually each.

Meanwhile, 90% of AI pilots fail. Not because the technology fails. Organizations pour intelligence into containers built for humans. o3 achieves 50% success with hours of reasoning, but still can't overcome architectural mismatch that is SaaS made for humans.

DOGE represents what every executive wants: dramatic operational efficiency without destroying capability. But they didn't retrofit legacy codebases older than their staff. They opened Cursor and generated new applications. Weeks to build what took decades.

The DOGE approach proves everyone recognizes the same problem: Organizations drowning in operational inefficiency. The solution isn't cutting people, it's building software that makes operations intelligent by design.

The lesson is clear. By spending 70% of budgets maintaining yesterday's constraints, you let competitors build the advantages of tomorrow. Your company becomes radically more efficient by becoming radically more intelligent.

Most software ships with 90% of its dependencies never used. Why pay for code you don't need?

The Venture Arbitrage

This is the cheapest moment in our lifetimes to build software.

Last month you could spend $15 on Claude 3.5 Sonnet to complete $58,000 in freelance software engineering tasks, benchmarked on OpenAI's SWE-Lancer.

Naturally, the question is why wouldn't AGI labs simply close this arbitrage themselves? Why wouldn't they complete all the Upwork tasks? Why wouldn't they automate everything they've beaten on benchmarks? Because in 2025 you can raise unlimited money with the insight that you can do it rather than actually doing it. Market share over profit maximization.

OpenAI lost $5 billion last year. This is Uber vs Lyft for intelligence. Every token you consume is priced below its economic value. For a brief moment in history, AGI labs are subsidizing your transformation while they fight for market dominance.

Model capabilities double every seven months. The arbitrage window closes when token prices normalize to economic value. Organizations building intelligence infrastructure now gain permanent advantage. Those waiting for "maturity" will find themselves priced out of transformation.

The economic value of AI in software engineering: $15 in compute yields $58,000 in value

The length of tasks AI can complete is doubling every 7 months

Inference costs are falling faster than power and memory costs—the arbitrage window is temporary

Aligned Pricing

Traditional software pricing assumes predictable value. Intelligence doesn't work that way.

Applying subscription or consumption pricing to AI inherently has perverse incentives. Where value comes from test-time compute, multiple attempts, exploring different trajectories. AI wrapper companies stuff deterministic pricing models built for static software. They're incentivized to meter intelligence not unleash it.

They fork RAG github repositories. Add function calling to zero shot output. Generate trivial, wordy paragraphs and call it an agent. They don't care if they delivered on their outcome. They just delivered on the output. They call it intelligence but it's gibberish, risking wrong answers destroying your business. You're just another customer paying them a monthly fee to get them $100m ARR faster than their competitor.

We introduce aligned pricing: compute-based retainer plus success fees based on economic value delivered. We win only when you dominate. Risk-adjusted, value-based, aligned with your success. When vendors share your risk, transformation accelerates.

This isn't about selling you a product. This is about total enterprise dominance.

Historical innovation required aligned incentives—from 18th century brewing to modern enterprise transformation

Software sprawl is drains budget and time. How much is hiding in your stack?

Process Engineering, Not Prompt Engineering

Everyone obsesses over prompts. They miss the point.

Since 2019 our team has been prompting and finetuning GPT-2, GPT-3, GPT-3.5 before ChatGPT existed. The gap between GPT-3.5 and ChatGPT wasn't the model. It was the application layer.

We learned prompting GPT is harder than coding because with coding there's a compiler. With prompting, you need to refine and derive full potential. No compiler exists. No runtime guarantees outcomes.

What we realized is the value isn't in prompting. It's in process engineering the runtime for intelligence to operate through your enterprise, as your enterprise.

Even AGI labs don't have full interoperability into their models. They don't understand why models do what they do. Just because you have an F1 car doesn't mean you know how to drive it. There's billions of dollars stuck inside LLMs. We unearth that value.

The complexity of tasks AI can handle is growing exponentially—what takes humans weeks will soon take AI hours

AI models now outperform 90%+ of human programmers in competitive coding—your engineering team needs augmentation, not replacement

Operations As Intelligence

Enterprises don't want AI. They want dominance.

Speed becomes the moat. Organizations running on intelligence move at token speed, not meeting speed. They iterate in minutes, not months. Their operational intelligence compounds while competitors debate in conference rooms.

Like Rockefeller transforming volatile kerosene into predictable commodity through infrastructure, Prava transforms raw intelligence into reliable operational capability.

Runtime For Intelligence makes this possible. Every action is managed. Every output validated. Every process logged immutably. Compliance isn't an afterthought. It's architecture. When software becomes commodity, governance and reliability become the differentiator.

We're not trying to replace all legacy software. We're animating it onto an intelligence runtime. Your operational expertise, embedded into continuously improving systems.

AGI Is Here

AI is quickly surpassing human performance across every major benchmark

It took over 200 years for people to realize Rome had fallen. AGI is already here—talk to any undergraduate and it becomes clear that artificial intelligence has surpassed general intelligence.

The steam engine was 97% efficient. Electricity still replaced it. Your current software works. Intelligence will still replace it.

The question isn't whether transformation happens. It's whether you lead it or follow it. Whether you build the intelligent enterprise or compete against one.

In the Software Century, every organization becomes a software company. Not by choice. By necessity. We're not retrofitting the past. We're building the infrastructure that makes every other approach obsolete. Organizations that understand this are already moving. They recognize that in five years, there will be two types of enterprises: those running on intelligence, and those that no longer exist.

The arbitrage window is measured in months, not years. Model capabilities double every seven months. Token prices subsidized by a venture capital war that won't last.

Every day you wait, someone else is building what will replace you. Not with better features or lower prices. With fundamentally different physics. With operations that think.

We built the runtime for intelligence. AI-native software that transforms your enterprise operations into intelligent systems. Where every process compounds. Where operational dominance emerges from architectural advantage.

You're not going to lose your business to AI. You're going to lose to somebody who uses AI to apply intelligence to every business operation.

The Software Century runs on intelligence. Prava builds the runtime.