Elon Musk's 5-Step Algorithm: Are You Solving the Wrong Problems?

What Elon Musk's 5-Step Algorithm Teaches Us About Strategy, AI, and the Cost of Unnecessary Complexity

There's something fundamentally backwards about the way many businesses approach improvement.

When a process takes too long, we look for ways to accelerate it. When a department becomes overwhelmed, we introduce new software or additional resources. When a marketing campaign underperforms, we adjust the creative, refine the targeting, or increase the budget. And increasingly, when employees find themselves buried in repetitive tasks, we turn to artificial intelligence to automate the workload.

These are reasonable responses to familiar business challenges. Improving efficiency, reducing costs, and increasing productivity have always been important management objectives.

Yet our instinct to immediately solve the problem in front of us can prevent us from asking a more valuable question:

Does the thing we're trying to improve actually need to exist?

What if the process taking too long is unnecessary? What if the department is overwhelmed because it spends much of its time compensating for problems created elsewhere in the organization? What if the underperforming marketing campaign is built around an offer the market doesn't understand?

In each scenario, the organization might invest considerable resources into improving something that should have been fundamentally reconsidered, redesigned, or eliminated.

I've become increasingly interested in this distinction as businesses accelerate their adoption of AI and automation. Technology has dramatically expanded what organizations can accomplish with limited resources, but it hasn't eliminated the need for sound judgment. If anything, the ability to execute almost anything more quickly makes deciding what deserves to be executed considerably more important.

An interesting framework for navigating this challenge comes from Elon Musk's approach to engineering and manufacturing. Although developed in an environment far removed from most businesses, its underlying principles offer a compelling way to reconsider how organizations approach strategy, operational improvement, and innovation.

The Optimization Trap: When Improving Something Isn't the Answer

At the heart of this problem is a tendency to equate improvement with progress.

When something isn't working, organizations naturally look for ways to make it work better. We identify bottlenecks, allocate resources, implement systems, and measure changes in performance. These activities are familiar, tangible, and relatively easy to justify.

But they often begin with an unspoken assumption that the existing activity deserves to continue.

A company might spend months improving an internal reporting system without asking whether its reports inform meaningful decisions.

A marketing department might invest in increasingly sophisticated campaign management tools while overlooking fundamental weaknesses in its positioning. A customer service team might introduce automation to handle recurring questions without investigating why customers are confused in the first place.

The individual improvements can be technically successful without addressing the underlying business problem.

This is what I think of as the optimization trap: investing in making an activity more efficient before establishing whether that activity contributes enough value to justify its existence.

Escaping the trap requires more than operational expertise. It requires the willingness to challenge assumptions, including those that have become deeply embedded in how an organization functions.

Elon Musk's 5-Step Algorithm: Why the Order Matters

In a 2021 Starbase interview with Tim Dodd of Everyday Astronaut, Musk described a five-step engineering process he wanted rigorously applied across his companies. The framework, which he calls The Algorithm, was subsequently explored in Walter Isaacson's 2023 biography, Elon Musk.

The process follows five steps in a specific sequence:

1. Question every requirement. Challenge the assumptions behind what needs to be built or accomplished, regardless of who established them.

2. Delete any part or process you can. Aggressively eliminate anything that isn't necessary to achieve the intended outcome.

3. Simplify and optimize. Improve the design or process only after removing what doesn't need to exist.

4. Accelerate cycle time. Increase the speed of execution once the remaining process has been simplified.

5. Automate. Introduce automation only after the previous four steps have been addressed.

What makes the algorithm interesting isn't the individual steps. Questioning assumptions, reducing waste, optimizing processes, and improving efficiency are hardly new concepts. Similar principles have informed engineering, lean manufacturing, and management thinking for decades.

What makes this framework particularly useful is its insistence on sequence.

Most organizations are comfortable with the final three steps. Optimization, acceleration, and automation produce tangible activities that can be assigned, budgeted, measured, and reported. They offer the visible satisfaction of making progress.

The first two steps are considerably less comfortable because they require questioning the decisions, assumptions, and organizational structures that made those activities necessary in the first place.

Musk argues that one of the most common engineering mistakes is optimizing something that should never have existed. It's a deceptively simple observation with implications well beyond engineering.

Businesses can become remarkably efficient at executing activities that contribute very little to their success. 

The danger is that improvements in productivity can make those activities appear increasingly justified, even when their underlying purpose remains questionable.

1. Question Every Requirement: The Hidden Cost of Unchallenged Assumptions

Every organization operates on a collection of assumptions. Some are intentionally established through strategic decisions, while others develop gradually as a business grows, hires employees, introduces new systems, and responds to challenges.

Over time, these assumptions become embedded in the way the company operates. A particular approval process becomes mandatory. A reporting structure becomes standard practice. A software platform becomes indispensable. Employees inherit responsibilities without necessarily understanding why those responsibilities were created.

Eventually, the explanation for why something exists becomes little more than, "That's how we've always done it."

One of the more distinctive aspects of Musk's algorithm is his requirement that every engineering constraint be associated with a specific individual who can explain and defend it. A department name isn't sufficient. The requirement needs an accountable person, and even requirements introduced by highly capable engineers are open to scrutiny.

The broader lesson is that authority and familiarity are not substitutes for validity.

Consider a company that requires three levels of approval before publishing marketing material. Perhaps the process originated after a costly mistake several years earlier, when an employee published inaccurate information. Additional oversight was introduced to reduce the risk of repeating that mistake.

At the time, the decision may have been perfectly reasonable. But years later, the company has developed comprehensive brand guidelines, hired more experienced employees, and implemented better quality controls. The original approval structure remains, even though the conditions that justified it have changed.

Management eventually notices that content production takes too long and begins investigating software that could streamline the approval process.

The organization is now preparing to invest in a solution without first determining whether the problem needs to exist.

A more strategic approach would question the requirement itself. Which approvals genuinely protect the business? Which exist primarily because they were introduced years ago? Could clearer ownership, better training, or more defined standards eliminate unnecessary oversight while maintaining appropriate safeguards?

Not every requirement should be removed. Legal obligations, safety standards, and essential controls often exist for very good reasons. The objective is to distinguish legitimate constraints from inherited assumptions, not to treat compliance or risk management as unnecessary bureaucracy.

This distinction matters because the longer a requirement goes unquestioned, the more likely the organization is to build additional processes around it. What begins as a single decision can eventually become an interconnected system of responsibilities, technologies, and costs.

Questioning a requirement before improving it creates an opportunity to address the source of complexity rather than its symptoms.

2. Delete Before You Improve: Why Complexity Is Easier to Create Than Remove

Organizations are exceptionally good at adding things.

When a new challenge emerges, the natural response is often to introduce another process, hire another employee, establish another meeting, purchase another platform, or create another initiative. Each addition can appear justified when viewed independently, particularly if it solves an immediate problem.

The difficulty is that these decisions rarely happen in isolation.

A growing professional services company might adopt a project management platform to improve coordination. As the team expands, it introduces reporting software to monitor performance. New approval workflows follow to maintain quality. Additional meetings become necessary to coordinate responsibilities, and eventually an internal administrator is assigned to manage the systems supporting the work.

Individually, every decision has a reasonable explanation. Collectively, they can create an organization in which employees spend an increasing percentage of their time managing the infrastructure surrounding their work rather than delivering value to customers.

This is how operational complexity accumulates. Not necessarily through poor decisions, but through a series of seemingly sensible additions that are rarely reconsidered as a whole.

Musk's second step challenges this tendency directly. His rule of thumb is that if you're not occasionally forced to restore roughly 10% of what you've removed, you probably haven't deleted aggressively enough. It's an intentionally provocative standard rather than a universal prescription for business management, but the underlying message is valuable: organizations tend to be much more comfortable retaining complexity than eliminating it.

In practice, deletion requires a different form of discipline than improvement. Optimization asks how something can perform better within its current structure. Elimination asks whether the structure deserves to remain.

The distinction is important because unnecessary complexity carries costs that aren't always captured in traditional productivity measures. A recurring meeting consumes time, but it also interrupts concentration and requires preparation. A redundant approval introduces delay, but it can also dilute accountability. An additional software platform comes with a subscription fee, but its greater cost may be the training, integration, data management, and organizational dependency it creates.

These costs compound as the business grows.

The challenge for leadership is recognizing that removing an activity can create just as much value as introducing a new capability. Eliminating a redundant process can improve decision-making speed, clarify ownership, reduce operating expenses, and free employees to focus on work that directly advances the business.

Of course, effective simplification isn't simply a matter of cutting costs or reducing headcount. Removing the wrong process can damage quality, weaken controls, or shift work onto customers and employees. The goal is not minimalism for its own sake; it's to remove what contributes little value while preserving what makes the organization effective.

The most productive activity an organization can eliminate is often the one nobody has thought to question.

3. Simplify and Optimize: Improvement Should Follow Elimination

Only after questioning requirements and removing unnecessary elements does Musk's algorithm turn toward simplification and optimization.

This ordering challenges a common assumption in business improvement: that the existing process is fundamentally correct and simply needs to perform better.

Traditional optimization initiatives often begin with detailed analysis of current workflows. Teams measure performance, map activities, identify bottlenecks, and search for opportunities to reduce friction. Those efforts can produce meaningful results, but they also risk reinforcing the structure they're intended to improve.

Imagine a business with a complicated customer onboarding experience. New customers complete multiple forms, provide information they've already submitted, wait for internal approvals, and communicate with several employees before receiving the service they purchased.

The company could approach this challenge through conventional process optimization. Forms could be redesigned, communications standardized, response times reduced, and workflows integrated through automation.

But what if half the steps were unnecessary?

By first examining what genuinely needs to happen between the initial purchase and successful onboarding, the business might discover that several activities exist primarily to accommodate outdated internal systems. Removing those dependencies could eliminate the need for many of the improvements being considered.

The remaining process would then be easier to simplify, measure, and optimize.

The same principle applies to brand strategy, an area where businesses frequently confuse activity with progress.

A company struggling with inconsistent messaging may respond by redesigning its website, rewriting sales presentations, improving advertising creative, and developing more comprehensive brand guidelines. Each initiative might make an individual touchpoint more polished, but the underlying problem could be that the business has never clearly defined its market position or articulated why customers should choose it.

Without that foundation, the organization risks refining multiple expressions of the same unresolved issue.

As we explore in our guide to brand strategy and positioning, effective brand development begins with the decisions that inform execution: who the organization serves, what differentiates it, what value it promises, and how those decisions translate into a consistent customer experience.

Once those questions are answered, the organization can eliminate conflicting messages, simplify its value proposition, and make more informed decisions about the assets and activities worth improving.

This is the difference between improving individual outputs and strengthening the system that produces them.

Optimization becomes considerably more valuable when it is applied to a structure that has already been critically examined.

4. Accelerate Cycle Time: Speed Is Only Valuable When Direction Is Right

Few business objectives receive as much attention as speed. Organizations want shorter development cycles, faster production, more responsive marketing, quicker decisions, and reduced time between identifying an opportunity and bringing a solution to market.

These ambitions make sense. In competitive markets, the ability to respond quickly can create a meaningful advantage. But the relationship between speed and business performance depends on an often-overlooked variable: direction.

A company can execute a poor strategy with extraordinary efficiency and still produce disappointing results. In certain circumstances, accelerating execution can actually make the consequences of a flawed decision more expensive.

Consider an organization struggling with customer acquisition. Leadership concludes that the marketing team isn't producing enough campaigns, so it invests in tools that dramatically increase creative output and reduce production timelines. Within months, the company is publishing more content, launching more advertising variations, and conducting more experiments than ever before.

Internally, productivity appears to have improved. Campaign volume is higher, turnaround times are lower, and the marketing department can demonstrate a substantial increase in output.

Yet acquisition costs remain elevated and customer conversion rates show little improvement.

The problem may not have been a shortage of marketing activity. Perhaps the company's offer is poorly differentiated, its positioning is unclear, or its targeting doesn't reflect the customers most likely to perceive value.

Accelerating execution hasn't resolved those problems. It has simply increased the volume of activity occurring around them.

This is closely connected to what I've previously described as the strategy-to-execution gap, where organizations struggle to translate strategic intentions into coordinated action that produces meaningful business outcomes.

It's possible to have excellent execution capabilities without a sufficiently clear strategy to direct them. When that happens, teams can become highly productive while the organization makes limited progress toward its most important objectives.

Why More Marketing Isn't Always Better Marketing

This is a challenge we regularly encounter when working with businesses looking to improve their digital marketing performance.

Many organizations approach growth with the belief that they need to be everywhere. They want an active presence across every major social media platform, comprehensive advertising campaigns, consistent content production, search visibility, and an expanding collection of digital touchpoints.

The ambition is understandable. Greater visibility among relevant audiences can create meaningful opportunities, and a diversified marketing strategy can ultimately become an important competitive advantage. The problem isn't the desire to reach more customers. It's attempting to establish and scale too many channels before understanding which ones are capable of generating meaningful results.

Imagine a business with a modest marketing budget dividing its investment across five different advertising platforms. Each channel receives enough funding to generate some activity, but potentially not enough to develop reliable insights, refine its approach, or establish a consistent pattern of performance. The organization is investing in greater exposure without necessarily developing a clear understanding of what produces results.

Our approach at JAXONLABS often involves bringing clients back to a more focused starting point. We work to identify where their audience is most likely to engage, which channels align with their commercial objectives, and where the business has the greatest opportunity to establish traction.

Rather than spreading resources across every available channel, we encourage clients to concentrate their initial investment where they have the strongest rationale for success. This creates an opportunity to monitor performance more closely, test assumptions, refine messaging, improve conversion rates, and establish a foundation supported by actual market feedback.

Once that foundation is producing meaningful results, expanding into additional channels becomes a more informed decision. The organization is no longer simply adding activity in the hope of generating growth. It's using what it has learned to guide where and how it scales.

There are circumstances where a broader multichannel approach makes sense from the beginning, particularly when audience behaviour, budget, and existing performance data justify it. But for many growing businesses, focus creates the conditions for learning that premature diversification can make difficult.

This is also why advertising budgets should be informed by acquisition economics rather than arbitrary spending targets. Our Advertising Budget Planner helps businesses evaluate starting budgets in the context of their offer, margins, customer acquisition targets, and growth objectives.

Ultimately, our role as advisors isn't to convince a business to do less for the sake of doing less. It's to ensure that the activities receiving resources have a clear purpose, a reasonable opportunity to succeed, and a foundation that supports future growth.

You don't scale to find out what works. You find out what works so you can scale it.

That distinction is particularly relevant to Musk's fourth step. Accelerating execution should follow the work of determining what deserves to be accelerated.

Scaling before establishing a sound foundation doesn't resolve weak strategic thinking. It increases the resources committed to it.

And this becomes especially important as technology reduces the time and effort required to produce work. More output is increasingly easy to achieve. Better outcomes still depend on understanding which activities contribute to value creation.

Musk's fourth step is therefore about more than moving quickly. It's about earning the right to accelerate through the discipline of the preceding steps.

Once an organization has challenged its assumptions, eliminated unnecessary activities, and simplified its operating processes, increasing speed becomes a logical extension of a more effective system.

The aim isn't to move as quickly as possible in every direction. It's to build the capacity to move decisively in the directions that matter.

5. Automate Last: The Most Important Lesson for the AI Era

If the first four steps expose weaknesses in how organizations approach improvement, the fifth reveals why this framework feels particularly relevant today.

Artificial intelligence is rapidly changing the economics of execution. Work that previously required specialized expertise, dedicated teams, or substantial financial investment can increasingly be accomplished using accessible AI tools.

Businesses can generate creative assets, develop software, analyze information, automate communications, build operational workflows, and conduct research with a level of speed and accessibility that would have been difficult to imagine only a few years ago.

The opportunity is significant, but it introduces a new problem. As automation becomes easier to implement, organizations may become less disciplined about determining whether the underlying activity should be automated at all.

Interestingly, Musk has acknowledged making this mistake himself.

During the development of Tesla's Model 3 manufacturing systems, the company encountered significant production challenges associated with excessive automation. As described in Isaacson's biography, Musk eventually recognized that some of the processes being automated were themselves unnecessary.

One particularly revealing example involved fiberglass mats used in battery-pack production. According to Musk's account, Tesla spent time improving the robotic installation process, increasing speed and refining the application of adhesive before ultimately determining that the mats could be eliminated.

The company had invested engineering effort into automating, accelerating, and optimizing a manufacturing step that didn't need to exist.

The problem wasn't necessarily that the automation technology had failed. The deeper mistake was assuming that the process being automated was essential.

Now consider how frequently a similar pattern could emerge as businesses adopt AI.

An organization receives hundreds of customer service inquiries each week and decides to implement an AI support agent. The business case is compelling: faster responses, lower service costs, improved availability, and less repetitive work for employees.

But before building the automation, what if the company investigated why customers were contacting support in the first place?

Perhaps a significant percentage of inquiries relate to confusing pricing, incomplete onboarding instructions, or information that should be readily accessible through the website. In that case, the business has two potential paths.

It can automate the process of answering recurring questions, or it can address the underlying experience that causes those questions to arise.

Both approaches may have merit, and the best solution could involve a combination of the two. But they produce fundamentally different outcomes.

One makes customer friction less expensive to manage, while the other attempts to remove that friction at its source.

The same challenge appears in internal operations. Businesses are beginning to use AI to summarize meetings, prepare reports, coordinate projects, and manage complex administrative workflows. These applications can create genuine value, particularly when the underlying work is necessary and well understood.

However, a meeting that shouldn't exist doesn't necessarily become valuable because AI can summarize it. A report that nobody uses doesn't suddenly become strategically important because it can be generated automatically. And a complicated workflow doesn't become well designed simply because employees can navigate it with an AI assistant.

Automation can reduce the cost of complexity without eliminating the complexity itself.

That distinction deserves more attention as businesses consider where to invest in AI.

At JAXONLABS, we've been exploring how organizations can evaluate AI opportunities in the context of broader business objectives rather than treating automation as an end in itself. Our AI Priority Planner is one practical resource for assessing potential initiatives by impact, effort, complexity, and confidence.

Yet even prioritization should be preceded by a more fundamental conversation about the processes and problems an organization is attempting to address.

AI can improve the economics of execution, but those economics only matter when execution supports something worth accomplishing.

The real opportunity isn't simply to automate more work. It's to create organizations that require less unnecessary work in the first place, then use automation to strengthen the activities that remain.

The Broader Lesson: Strategy Is About Deciding What Not to Do

Although Musk's algorithm emerged from engineering and manufacturing, I think its most important business implication concerns the nature of strategy itself.

Organizations often approach strategy as an exercise in identifying opportunities.

Leadership teams explore new markets, products, technologies, partnerships, marketing channels, and growth initiatives. They develop ambitious plans and attempt to align resources around the possibilities they believe offer the greatest potential.

But the quality of a strategy isn't determined by the number of opportunities it identifies.

It's determined by the quality of the choices it makes.

Every business operates with constraints, even when financial resources are readily available. Leadership attention is finite, organizational capacity has limits, and employees can only divide their efforts among so many competing priorities before effectiveness begins to deteriorate.

This makes deciding what not to pursue just as important as deciding where to invest.

The difficulty is that opportunities rarely present themselves as obviously unnecessary. Most initiatives can be supported by a plausible business case, particularly when evaluated independently. A new product could open another revenue stream. An additional service could increase customer retention. A different marketing channel could expand market reach.

Individually, these ideas may be reasonable. Collectively, they can pull an organization in too many directions.

This is one of the reasons I believe strategic clarity becomes more valuable as execution becomes less expensive.

When building a new website, launching a campaign, creating software, or developing a product required substantial resources, the cost of execution created a natural incentive to evaluate decisions carefully. That financial friction didn't guarantee good strategy, but it forced organizations to consider whether an initiative justified the investment.

AI is changing that relationship.

The cost of testing ideas, producing assets, and building solutions is declining, which creates extraordinary opportunities for experimentation. Yet when the barrier to execution falls, so does the barrier to pursuing poorly considered ideas.

Businesses can now afford to do more things, but that doesn't necessarily mean they should.

Without a clear decision framework, lower execution costs can translate into a larger portfolio of disconnected initiatives, more systems to maintain, and greater demands on the very people responsible for driving strategic progress.

This creates a paradox: The easier it becomes to build things, the more important it becomes to know what not to build.

Strategy provides the discipline required to navigate that paradox. It establishes the direction, priorities, and trade-offs that allow an organization to distinguish meaningful opportunities from distractions.

Musk's algorithm offers a practical extension of that thinking. Before investing in improvement, it encourages leaders to question necessity. Before adding capability, it encourages the removal of complexity. And before introducing technology, it asks whether the underlying system is worth preserving.

The framework isn't a replacement for strategy. It's a useful method for challenging the assumptions and operational decisions through which strategy is executed.

How Business Leaders Can Apply the Algorithm

One of the most useful aspects of this framework is that it doesn't require a large transformation initiative or an extensive organizational restructuring to begin applying it.

A leadership team could start with a single process, customer experience, operational challenge, or proposed technology investment. The objective would be to work through the five steps in sequence rather than jumping immediately to the solution that appears most obvious.

Begin by questioning the requirement. What outcome is the organization trying to achieve, and why does the current approach exist? Which constraints are essential, and which are inherited from previous decisions? Importantly, who has the knowledge and authority to explain them?

Then examine what can be removed. Identify activities, approvals, deliverables, or features that contribute limited value to the desired outcome. Consider not only their direct operating costs but also the time, attention, and coordination they consume.

Simplify what remains. Once unnecessary steps have been eliminated, redesign the process around the needs of the people using it. Clarify responsibilities, reduce friction, and establish a more coherent structure for delivering the intended result.

Look for opportunities to accelerate. With a stronger process in place, identify bottlenecks that slow execution and determine where improvements in speed would create meaningful value without compromising quality or increasing risk.

Finally, evaluate automation. Determine which remaining activities are repeatable, sufficiently well understood, and valuable enough to justify investment in technology or AI.

The process should also be iterative. Removing a requirement may expose an issue that needs reconsideration, and an attempt to automate a workflow may reveal complexity that should have been addressed earlier. The sequence provides discipline without requiring organizations to ignore new information.

Leaders who want to apply this thinking more broadly can also use a One-Page Strategic Plan to connect decisions about operational improvement with the organization's wider objectives and priorities.

The purpose of the exercise isn't simply to create a leaner operation. It's to ensure that the resources committed to execution are contributing to outcomes that matter.

The Competitive Advantage of Knowing What Not to Build

We're entering a period in which the ability to produce, develop, and execute is becoming more accessible across almost every industry.

AI is making specialized capabilities available to smaller organizations. Software development is becoming faster, content is easier to generate, and automation is increasingly practical for businesses that previously lacked the resources to implement it.

These developments create tremendous opportunities, particularly for organizations that have historically been constrained by limited technical expertise or capital. But they also challenge traditional assumptions about where competitive advantage comes from.

If more companies can access similar technologies and produce comparable outputs, execution capability alone becomes a less reliable source of differentiation.

The quality of the thinking that directs execution becomes increasingly important.

That means understanding the customer, identifying the right problem, challenging assumptions, making difficult trade-offs, and building systems that reinforce a coherent strategic direction. Technology can support these capabilities, but it cannot remove the responsibility of leadership to exercise judgment.

This is what I find most compelling about Musk's algorithm. Its value isn't that it offers a universal formula for solving every business problem, or that engineering principles should be adopted without considering organizational context. It's that the framework forces a discipline many organizations struggle to maintain: questioning whether the work itself is necessary before investing in making it better.

For business leaders, that may be one of the most consequential habits to develop in the years ahead.

Because as the cost of execution continues to fall, the cost of a poor strategic decision doesn't necessarily disappear. In some cases, it simply becomes easier to replicate, accelerate, and scale.

The companies that benefit most from AI and automation won't necessarily be those that adopt the most tools or generate the greatest volume of output. I believe they'll be the organizations that develop the judgment to distinguish meaningful work from unnecessary complexity, and the discipline to direct their capabilities toward the opportunities that matter most.

At JAXONLABS, that philosophy sits at the centre of how we approach strategy and innovation: establish clarity before committing resources, challenge assumptions before building solutions, and use technology to strengthen a direction worth pursuing.

The objective has never been simply to help businesses do more.

It's to help them make better decisions about what deserves to be done.

And in an era where almost anything can be built faster, that distinction may become one of the most valuable competitive advantages of all.