Can a Person with a Non-Technical Background Learn AI? [10 Key Factors] [2026]
The question of whether individuals without technical backgrounds can learn artificial intelligence has evolved from skeptical inquiry to resounding affirmation. As emphasized by DigitalDefynd, artificial intelligence has democratized significantly, breaking down traditional barriers that once gatekept technological advancement.
The landscape of AI education has transformed dramatically over recent years. With thousands of accessible resources, supportive communities, and innovative learning platforms emerging globally, non-technical professionals now possess unprecedented opportunities to transition into AI careers. Educational organizations and tech companies recognize this potential, investing in beginner-friendly courses and mentorship programs designed specifically for career changers.
This article explores ten critical factors determining whether non-technical learners can successfully acquire AI competency. From evaluating resource availability and mathematical requirements to examining community support and practical applications, we address comprehensive considerations enabling informed decisions. The convergence of talent shortages, accessible technologies, and supportive ecosystems creates genuinely viable pathways. Whether you’re contemplating career transitions, seeking professional growth, or exploring AI fundamentals, understanding these factors positions you confidently for success in artificial intelligence education and careers.
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Can a Person with a Non-Technical Background Learn AI? [10 Key Factors] [2026]
Summary Table of Key Factors
| Key Factor | Explanation |
| Availability of Beginner-Friendly Learning Resources | Over 40% of AI learners start without technical backgrounds, with platforms like Coursera and edX offering 15,000+ courses globally, making education accessible through diverse formats and free tier options. |
| Essential Mathematical Foundations Required | Approximately 70% of AI jobs list mathematics as prerequisite, though learners can acquire foundational competency in 3-6 months using visual learning platforms without requiring PhD-level expertise. |
| Programming Language Barriers and Solutions | Python dominates AI learning with 90% of projects using it; its English-like syntax enables rapid mastery for beginners, while no-code platforms eliminate coding requirements entirely. |
| No-Code and Low-Code AI Platforms | The no-code market grows 23% annually with 64% of enterprises adopting these platforms, enabling non-technical staff to build AI solutions using visual interfaces and drag-and-drop components. |
| Time Investment and Learning Pace | Non-technical professionals require 300-500 hours for foundational competency; 70% balance learning with full-time employment, achieving proficiency within 12-18 months through flexible part-time options. |
| Quality of Online Courses and Bootcamps | 87% of bootcamp graduates secure employment within six months; quality varies significantly, with mid-range programs ($300-$5,000) often delivering superior value compared to expensive alternatives. |
| Community Support and Mentorship | Learners with mentors demonstrate 65% higher completion rates and 40% faster skill acquisition; 78% of successful AI learners actively participate in supportive communities. |
| Practical Project-Based Learning Opportunities | Project-based learning improves retention by 75% compared to theoretical instruction, with 82% of successful learners attributing transitions to hands-on portfolio development. |
| Career Transition Feasibility | AI positions grow 74% faster than average jobs; 59% of organizations struggle filling roles, while 52% of current AI professionals transitioned from non-technical backgrounds. |
| AI Tools That Don’t Require Coding Skills | 80% of AI applications will use no-code platforms; 500 million people globally interact with consumer AI tools monthly, democratizing access across industries. |
1. Availability of Beginner-Friendly Learning Resources
Over 40% of AI learners start without formal technical education, with platforms like Coursera and edX offering more than 15,000 AI and machine learning courses globally – according to educational technology reports.
The landscape of beginner-friendly learning resources has transformed dramatically, making AI education more accessible than ever before. This democratization of knowledge has opened doors for non-technical individuals seeking to enter the AI field.
Online Learning Platforms
Major platforms have revolutionized AI education. Coursera, founded by computer scientists, now hosts comprehensive AI courses from top universities. Similarly, edX and Udemy provide affordable alternatives, with some courses priced under $15. According to learning analytics data, approximately 65% of online learners now utilize platforms like these to acquire AI skills without traditional degrees.
Diverse Content Formats
Learning resources extend beyond text-based materials. Video tutorials, interactive coding environments, and sandbox platforms allow hands-on practice without setup complications. YouTube channels dedicated to AI education have accumulated billions of views, indicating widespread accessibility.
Corporate and University Initiatives
Tech giants like Google and Microsoft have launched free AI courses specifically designed for non-technical backgrounds. Google’s AI Essentials program, for instance, reaches hundreds of thousands of learners monthly. Universities have also adapted their curricula, creating introductory modules that require minimal mathematical prerequisites.
Challenges Despite Abundance
However, the sheer volume of resources can overwhelm beginners. Quality varies significantly across platforms—some lack structured progression or adequate support systems. Additionally, many free resources may not provide recognized certifications that employers value.
Practical Accessibility
The most valuable aspect is hands-on practice availability. Cloud platforms offer free tier access, enabling learners to experiment without financial barriers. This practical exposure bridges the gap between theory and application.
The availability of beginner-friendly resources represents a fundamental advantage for non-technical learners. While abundance presents organizational challenges, the diversity ensures individuals can find suitable learning paths matching their pace, learning style, and career goals, substantially improving their potential success in AI education.
2. Essential Mathematical Foundations Required
Research from MIT indicates that approximately 70% of AI job descriptions list mathematics as a prerequisite skill. However, surveys show non-technical learners can acquire foundational math within 3-6 months – according to educational outcome reports.
Mathematical foundations form the backbone of artificial intelligence, yet they need not be intimidating for non-technical individuals. Understanding the essential mathematical concepts is achievable through structured learning and persistence.
Core Mathematical Areas
Non-technical learners must grasp linear algebra, calculus, and statistics. Linear algebra, crucial for understanding neural networks, involves vectors and matrices—concepts manageable without an advanced background. Calculus helps learners understand optimization algorithms that train AI models. Statistics enables interpretation of data patterns and model performance metrics.
Good News for Non-Technical Learners
The encouraging aspect is that complete mastery isn’t necessary. According to machine learning practitioners, most AI applications require intermediate-level mathematics rather than PhD-level expertise. Many successful AI professionals focus on practical applications while learning mathematics gradually alongside projects.
Bridging the Gap
Numerous resources specifically address math anxiety. Khan Academy and 3Blue1Brown offer intuitive visual explanations of complex concepts. These platforms transform abstract formulas into understandable visualizations, making mathematics less daunting for beginners.
Alternative Pathways
No-code AI platforms reduce mathematical requirements significantly. Tools like AutoML systems handle mathematical complexities automatically, allowing non-technical individuals to build functional AI models. However, understanding underlying principles remains valuable for troubleshooting and optimization.
Practical Integration
Rather than memorizing formulas, successful learners connect mathematics to real-world applications. Building projects simultaneously while studying math creates meaningful context. This approach, supported by educational psychology research, improves retention and motivation.
The Reality
While mathematics poses initial challenges, it’s far from insurmountable for determined non-technical learners. The key is recognizing that AI mathematics differs from academic mathematics—it’s applied, practical, and learnable incrementally. With proper resources and persistence, non-technical individuals can develop sufficient mathematical foundations within reasonable timeframes, enabling them to pursue meaningful AI careers and projects effectively.
3. Programming Language Barriers and Solutions
Data from Stack Overflow surveys shows that Python is learned by 62% of AI beginners due to its simplicity. In comparison, 45% of non-technical professionals report programming language as their primary learning obstacle – according to tech education research.
Programming languages often intimidate non-technical individuals, yet programming language barriers are increasingly surmountable with modern tools and approaches. The good news is that success in AI doesn’t require mastery of complex languages.
Python Dominance
Python has emerged as the preferred language for AI and machine learning. Its syntax resembles natural English, making it significantly easier for beginners compared to languages like C++ or Java. According to machine learning research platforms, approximately 90% of AI projects utilize Python, confirming its accessibility for newcomers.
Simplified Learning Curve
Non-technical learners benefit from Python’s forgiving nature. The language prioritizes readability over complexity, allowing beginners to understand code logic quickly. Online courses specifically designed for non-programmers can teach basic Python proficiency within weeks rather than months.
Alternative Solutions
Visual programming environments eliminate traditional coding requirements. Platforms like MIT App Inventor and Scratch introduce programming concepts through drag-and-drop interfaces. Similarly, AutoML platforms enable AI development without writing code, democratizing access for non-technical individuals.
Support Ecosystems
Massive communities provide incredible support. Stack Overflow, GitHub, and Reddit forums contain millions of answered programming questions. When non-technical learners encounter obstacles, solutions often exist within minutes of searching—a massive advantage over previous generations.
Integrated Development Environments
Modern tools like Jupyter Notebooks make learning interactive and intuitive. These environments allow learners to write code, see results immediately, and understand cause-and-effect relationships visually.
Overcoming the Barrier
While programming language barriers exist initially, they’re temporary obstacles rather than permanent blockers. Non-technical individuals can learn Python effectively through persistence and consistent practice. Starting with simple projects and gradually increasing complexity builds confidence and competence simultaneously. The combination of accessible languages, supportive communities, and innovative platforms makes programming achievable for virtually anyone committed to learning AI effectively.
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4. No-Code and Low-Code AI Platforms
According to Forrester Research, the no-code and low-code AI market is growing at 23% annually, with 64% of enterprises adopting these platforms to enable non-technical staff to build AI solutions, based on enterprise technology reports.
No-code and low-code AI platforms have revolutionized accessibility for non-technical individuals, eliminating the need for extensive programming knowledge. These platforms represent a paradigm shift in democratizing artificial intelligence development.
What Are No-Code and Low-Code Platforms?
These platforms provide visual interfaces where users drag-and-drop components instead of writing code. Low-code platforms require minimal coding for customization, while no-code solutions require virtually none. This fundamental difference makes AI development accessible to domain experts without technical backgrounds.
Popular Platforms
Microsoft Power Platform, Google’s Vertex AI, and IBM Watson offer intuitive interfaces for building AI models. Zapier and Make allow workflow automation using AI without programming knowledge. According to tech adoption studies, these platforms have reduced AI project development time by 40% compared to traditional coding approaches.
Real-World Benefits
Non-technical professionals can now create chatbots, analyze data, and build predictive models independently. Business analysts, marketing professionals, and domain experts leverage these platforms to solve problems directly, eliminating communication gaps with technical teams.
Democratization Impact
Organizations increasingly recognize that domain expertise matters more than coding skills. A healthcare professional understands patient needs better than programmers, making them ideal for building AI solutions in their field using no-code platforms.
Limitations to Consider
Despite advantages, these platforms have constraints. Complex AI projects may exceed platform capabilities, requiring traditional programming eventually. Additionally, relying entirely on no-code solutions can limit deep understanding of underlying AI mechanisms.
The Practical Solution
No-code and low-code AI platforms serve as excellent entry points for non-technical learners. They build confidence, demonstrate AI applications practically, and allow meaningful contributions without extensive technical training. Many learners eventually transition to traditional programming after understanding AI concepts through these platforms, creating a natural learning progression that respects individual backgrounds and learning speeds effectively.
5. Time Investment and Learning Pace
Studies from online learning platforms indicate that non-technical professionals typically require 300-500 hours to achieve competency in AI fundamentals, though this varies based on prior experience – according to educational completion data and learner outcome reports.
Time investment and learning pace represent critical factors determining whether non-technical individuals successfully transition into AI careers. Understanding realistic timelines helps learners set appropriate expectations and maintain motivation throughout their journey.
Realistic Time Expectations
The path to AI competency isn’t instantaneous. According to skill acquisition research, achieving foundational proficiency typically requires 6-12 months of consistent study. Intensive bootcamp programs compress this to 3-4 months through full-time commitment, while part-time learning extends timelines to 18-24 months.
Flexible Learning Options
Modern learners enjoy unprecedented flexibility. Part-time online courses allow working professionals to study evenings and weekends, eliminating career interruption. According to learning platform analytics, approximately 70% of AI students balance learning with full-time employment, proving progression is achievable without extreme sacrifice.
Personalized Pace Benefits
Unlike rigid classroom settings, online platforms accommodate individual learning speeds. Some learners grasp concepts quickly, while others benefit from repetition and extended practice. This flexibility significantly improves completion rates and learning retention compared to standardized timeframes.
Factors Affecting Learning Speed
Prior experience matters considerably. Individuals with adjacent technical backgrounds progress faster than complete beginners. Domain expertise in relevant fields—finance, healthcare, marketing—accelerates practical application understanding, compensating for technical knowledge gaps.
Practical Acceleration
Project-based learning accelerates progress substantially. Rather than passive theory consumption, building real projects while learning creates contextual understanding. Research on learning effectiveness shows this approach reduces time-to-competency by approximately 30-40%.
Sustainability Matters
Consistent, manageable study schedules trump intensive cramming. Learning AI requires neural network development—literally building brain connections—which happens gradually through repetition rather than marathon sessions.
The Bottom Line
While time investment and learning pace present challenges, realistic timelines and flexible options make AI education achievable for non-technical professionals; success depends less on total hours and more on consistency, motivation, and practical application. Most determined learners can transition into AI roles within 12-18 months through dedicated, balanced effort.
6. Quality of Online Courses and Bootcamps
Research from Course Report indicates that 87% of bootcamp graduates secure employment within six months, while course completion rates on platforms like Coursera average 12%, suggesting quality variations – based on education industry analysis and learner outcome data.
Quality of online courses and bootcamps varies dramatically, making careful selection crucial for non-technical learners. Understanding quality indicators helps distinguish valuable programs from substandard offerings that waste time and money.
Evaluating Course Quality
Legitimate platforms demonstrate instructor credibility, detailed curriculum structures, and peer-reviewed content. University-backed courses from institutions like Stanford and MIT carry established academic rigor. According to educational assessment studies, courses with structured progression and clear learning outcomes show 50% higher completion rates than unorganized alternatives.
Bootcamp Considerations
Intensive bootcamps promise rapid skill acquisition, typically within 12-16 weeks. However, success depends heavily on bootcamp selection. Accredited programs emphasizing project-based learning and mentorship outperform those prioritizing lecture-based content. According to bootcamp graduate surveys, approximately 75% report practical job-readiness from reputable programs, compared to significantly lower rates from lesser-known institutions.
Red Flags to Avoid
Be cautious of programs promising employment guarantees or unrealistic timelines. Quality courses acknowledge that learning requires sustained effort. Poorly designed programs feature outdated content, absent instructor support, or excessive theory without practical application.
Cost Versus Value
Expensive doesn’t automatically mean superior. According to learner satisfaction reports, mid-range programs ($300-$5,000) often deliver better value than premium options. Free courses from established platforms sometimes surpass expensive alternatives in content quality and community support.
Community and Support
Peer interaction and instructor accessibility significantly impact learning outcomes. Programs offering mentorship, discussion forums, and networking opportunities create accountability and motivation. Research indicates learners with active community engagement show 45% higher completion rates.
Making Informed Decisions
Before enrolling, review student testimonials, verify instructor backgrounds, and examine curriculum details thoroughly. Trial lessons or free introductory modules provide insight into teaching quality. The quality of online courses and bootcamps directly influences learning success, making informed selection essential. Choosing programs aligned with your learning style and career goals ensures maximum return on investment and higher achievement rates for non-technical learners pursuing AI careers.
7. Community Support and Mentorship
According to educational psychology research, learners with active mentor relationships demonstrate 65% higher completion rates and 40% faster skill acquisition compared to self-directed learners – based on learning outcome studies and community engagement analysis.
Community support and mentorship serve as invaluable catalysts for non-technical learners pursuing AI education. The psychological and practical benefits of engaged communities dramatically improve learning outcomes and career prospects.
Mentorship Advantages
Having experienced guides accelerates progress substantially. Mentors provide personalized feedback, clarify misconceptions rapidly, and navigate learners around common pitfalls. According to career development research, individuals with mentors achieve career goals 23% faster than those without guidance. For non-technical learners, mentorship bridges knowledge gaps and builds confidence during challenging transitions.
Community Engagement Impact
Active communities create accountability and motivation. Peer interactions normalize struggles, reduce isolation, and foster collaborative learning. Reddit communities like Machine Learning and specialized Discord servers connect thousands of learners globally. Survey data shows that 78% of successful AI learners actively participate in communities, indicating a strong correlation between engagement and achievement.
Knowledge Sharing Ecosystem
Communities enable reciprocal learning. As learners progress, they help others, reinforcing their own understanding. This creates self-sustaining educational ecosystems where collective knowledge accelerates everyone’s development.
Finding Quality Communities
Seek communities with active moderation, relevant expertise, and inclusive cultures. GitHub open-source projects welcome non-technical contributors. Specialized Slack workspaces and Discord servers cater to specific interests—whether healthcare AI, natural language processing, or computer vision.
Mentorship Resources
Formal mentorship programs exist through organizations like AI4ALL and Women in AI, specifically supporting underrepresented groups. Many bootcamps include structured mentorship as core components. Additionally, platforms like LinkedIn facilitate mentor connections based on shared interests and expertise.
Remote Accessibility
Modern mentorship transcends geographic boundaries. Virtual meetings, asynchronous feedback, and online communities make quality mentorship accessible regardless of location—a revolutionary change enabling global participation.
The Transformative Factor
Community support and mentorship transform isolated learning into collaborative growth. Non-technical learners benefit immensely from experienced guidance and peer encouragement, making community engagement not optional but essential. Success often depends less on individual talent and more on leveraging collective wisdom available within supportive communities effectively.
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8. Practical Project-Based Learning Opportunities
Research from learning science journals shows that project-based learning improves retention rates by 75% compared to theoretical instruction, with 82% of successful AI learners attributing career transitions to hands-on projects – according to educational effectiveness studies.
Practical project-based learning opportunities transform abstract AI concepts into tangible skills non-technical learners can demonstrate and build upon. This learning approach bridges theory and real-world application effectively.
Learning Through Doing
Building actual projects creates contextual understanding impossible through lectures alone. Non-technical learners grasp how AI models work by constructing sentiment analysis tools, recommendation systems, or chatbots. According to cognitive psychology research, this experiential approach generates stronger neural connections and improves long-term retention significantly.
Accessible Project Resources
Platforms like Kaggle host thousands of datasets and competitions where learners practice real-world skills. GitHub provides open-source projects welcoming beginner contributions. These platforms democratize project access—no expensive infrastructure required, just internet connectivity and determination.
Progressive Complexity
Structured learning pathways offer graduated challenges. Beginning with simple classification tasks, learners gradually tackle complex problems. This scaffolded approach prevents discouragement while building confidence incrementally. Survey data indicates 89% of learners prefer projects with increasing difficulty levels over static challenges.
Portfolio Development
Projects create demonstrable portfolios proving capability to employers. Non-technical backgrounds become less problematic when applicants showcase real solutions to genuine problems. According to hiring manager surveys, completed projects influence hiring decisions more than credentials for entry-level positions.
Collaboration Benefits
Group projects simulate workplace environments, teaching teamwork and communication skills. Many bootcamps emphasize collaborative projects, recognizing that professional success requires more than technical knowledge. Learning to articulate ideas, accept feedback, and integrate diverse perspectives proves invaluable.
Real-World Relevance
Projects addressing genuine problems—predicting disease outbreaks, optimizing supply chains, improving educational outcomes—create meaningful motivation. Non-technical professionals often possess domain expertise, making them exceptional project designers, turning background disadvantages into advantages.
The Catalyst Effect
Practical project-based learning opportunities energize non-technical learners by demonstrating tangible progress. Rather than accumulating certificates, learners build portfolios showcasing capabilities. This hands-on approach accelerates confidence development and creates genuine employment prospects, making projects indispensable to successful AI career transitions.
9. Career Transition Feasibility
According to McKinsey research, 59% of organizations report difficulty filling AI roles due to skill shortages, creating opportunities for career changers. In comparison, 52% of current AI professionals transitioned from non-technical backgrounds – based on workforce and hiring trend analysis.
Career transition feasibility represents an encouraging reality for non-technical professionals seeking AI careers. The current talent shortage creates unprecedented opportunities for qualified candidates regardless of educational background.
Strong Market Demand
The AI job market exhibits robust growth. According to labor statistics, AI-related positions grow 74% faster than average job growth across industries. This exceptional demand means employers increasingly prioritize skills and capabilities over traditional credentials, benefiting non-technical career changers significantly.
Diverse Career Pathways
Non-technical backgrounds enable unique career angles. Domain expertise becomes valuable—healthcare professionals build medical AI systems, financial experts develop trading algorithms, educators create learning platforms. These specialized backgrounds make career changers attractive candidates because they understand industry challenges intimately.
Realistic Timeline Expectations
Complete career transitions typically require 12-18 months of dedicated learning combined with practical portfolio development. According to career transition surveys, approximately 68% of successful changers land AI-related positions within two years of starting education. This timeframe balances thorough skill development with reasonable career momentum.
Entry-Level Opportunities
Numerous junior positions welcome non-traditional backgrounds. Roles like AI trainers, prompt engineers, data annotators, and AI ethics specialists value diverse perspectives. These positions provide practical experience while building toward more technical roles gradually.
Employer Perspectives
Forward-thinking companies recognize that technical skills are teachable while problem-solving abilities and industry knowledge prove difficult to develop. Many organizations actively recruit career changers, valuing their maturity, work ethic, and unique perspectives.
Transition Challenges
Initial positions may involve salary reductions or lateral moves. Persistence through this adjustment period proves crucial, as career trajectory typically improves significantly after 18-24 months of relevant experience.
Success Factors
Career transition feasibility improves dramatically with strategic positioning. Non-technical learners should emphasize transferable skills, leverage domain expertise, and showcase project portfolios demonstrating capability. The convergence of talent shortages, emerging roles, and employer flexibility creates genuinely viable pathways for motivated professionals transitioning into AI careers from non-technical backgrounds.
10. AI Tools That Don’t Require Coding Skills
According to Gartner, 80% of AI applications will be created using no-code or low-code platforms by 2030, with 65% of business users already employing AI tools without programming knowledge – based on enterprise technology forecasts and adoption trends.
AI tools that don’t require coding skills have emerged as game-changers, enabling non-technical individuals to leverage artificial intelligence independently. These accessible tools democratize AI development and reduce dependence on specialized technical expertise.
Consumer-Friendly AI Applications
Modern AI tools feature intuitive interfaces requiring zero programming knowledge. ChatGPT, Gemini, and Claude allow users to interact with powerful language models through simple text inputs. Image generation tools like DALL-E and Midjourney enable creative professionals to produce visuals without design backgrounds. According to user adoption data, approximately 500 million people globally interact with consumer AI tools monthly.
Business Intelligence Tools
Platforms like Microsoft Power BI and Tableau incorporate AI features enabling data analysis without coding. Users create dashboards, generate insights, and build predictive models through drag-and-drop interfaces. These tools democratize data-driven decision-making across organizations, empowering non-technical employees to extract meaningful patterns independently.
Automation Platforms
Services like Zapier, IFTTT, and Make connect applications and automate workflows using AI without requiring programming. Marketing professionals, project managers, and administrative staff automate repetitive tasks, increasing productivity significantly. Survey results indicate users save approximately 10 hours weekly through intelligent automation.
Specialized Domain Tools
Industry-specific AI tools address particular needs. Fliki creates videos from text, Grammarly enhances writing, and Synthesia generates presentations—all without coding requirements. These specialized tools prove invaluable for professionals seeking competitive advantages in their fields.
Accessibility Benefits
These tools level the playing field, reducing technical barriers to innovation. Non-technical entrepreneurs launch AI-powered startups, educators enhance student experiences, and healthcare workers analyze patient data—all without programming expertise.
Limitations
Relying entirely on user-friendly tools limits customization and deep understanding. However, they serve excellently as entry points and practical solutions for immediate needs.
Empowering Non-Technical Professionals
AI tools that don’t require coding skills represent accessibility breakthroughs, enabling non-technical individuals to participate meaningfully in AI transformation. From content creation to business analytics, these tools extend AI capabilities broadly, proving that technical backgrounds aren’t prerequisites for leveraging artificial intelligence effectively in modern professional environments.
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Conclusion
The evidence overwhelmingly demonstrates that non-technical backgrounds need not prevent AI learning and career success. Throughout this exploration of ten critical factors, we’ve discovered that accessibility, support systems, and practical opportunities abound for determined learners. According to DigitalDefynd’s educational analysis, modern learners enjoy unprecedented advantages compared to previous generations—from no-code platforms to global mentorship networks.
The convergence of market demand, educational innovation, and diverse learning pathways creates historically unique opportunities. With 52% of current AI professionals originating from non-technical backgrounds and 80% of future AI applications utilizing no-code solutions, career transitions become increasingly feasible. Success requires commitment, strategic learning planning, and leveraging available resources effectively—not innate technical genius.
Non-technical learners bring valuable perspectives, domain expertise, and diverse problem-solving approaches, enriching the AI field. As artificial intelligence continues reshaping industries and society, inclusive education ensures participation across backgrounds, strengthening innovation capabilities. Your non-technical background represents not a limitation but a distinctive advantage. With proper resources, persistence, and community support, transitioning into meaningful AI careers remains entirely achievable regardless of starting point.
