Have you ever wondered how many developers and major tech companies use AI-generated code in their software? Some openly embrace it, while others use vibe coding behind the scenes. Gartner predicts that 75% of enterprise developers will use AI assistants by 2028, which means that vibe coding will only grow. However, vibe coding is not without its hurdles — debugging, system complexities, and quality control still pose major challenges.
That’s where Dr. Leslie Kanthan and his company, TurinTech, step in. TurinTech is making development faster, more efficient, and scalable, solving these issues and pushing the future of coding forward. But how does TurinTech solve vibe coding’s most significant challenges? Stay tuned and keep reading to find out all the details.
What is vibe coding, and why is it becoming popular?
Vibe coding is a relatively new term that describes a modern approach to writing software that focuses on speed, creativity, and AI-powered automation. At its core, developers rely on the “vibe” and power of artificial intelligence, rather than focusing on the details of the generated code. The term was actually introduced by Andrej Karpathy, co-founder of OpenAI and former head of AI at Tesla, in February 2025 (so, literally yesterday).
Thus, instead of spending hours writing and debugging code manually, developers use AI-driven tools like Cursor, GitHub Copilot, Codeium, and others to generate, optimize, and refine code faster than ever. Vibe coding really does make software development much faster and more efficient by allowing engineers to generate, refactor, and parallelize code quickly, sometimes achieving even 100x speed improvements. This shift lets engineers focus more on product design and problem-solving rather than manual coding.
AI tools also reduce emotional attachment to code, making it easier to scrap and rewrite when needed. Moreover, vibe coding becomes more accessible, allowing people with a weak technical background or without traditional training to create efficient code.
Vibe coding biggest challenges
Even though the vibe coding approach speeds up the whole process of coding and lowers barriers to entry, there are still some powerful challenges out there that require human expertise. Some of them are:
- Debugging: AI tools struggle with debugging. Engineers still need to identify and fix bugs manually, as AI models aren’t yet effective at tracing code paths or logic errors.
- Systems engineering: Although vibe coding is great for rapid prototyping and feature development, it struggles with low-level systems engineering. Once a product reaches scale or hits product-market fit, more traditional engineering skills are required to handle complex, large-scale infrastructure.
- Quality control: AI-generated code can sometimes be inefficient or not meet specific quality standards. Engineers need to be able to evaluate and refine the code.
- Overreliance on AI: There’s a concern about losing deeper engineering skills or understanding, especially if engineers become too reliant on the AI without fully grasping what the code is doing.
But no worries! Here comes Dr. Leslie Kanthan and his company, Turin Tech, who have developed a powerful solution to address vibe coding biggest challenges. But first and foremost, let’s talk about who Leslie Kanthan is, the CEO and co-founder of TurinTech.
Dr. Leslie Kanthan’s professional path
Dr. Leslie Kanthan’s academic journey began at the University of Warwick, where he earned a Bachelor’s degree in Mathematics. His research interests included Knot Theory, Graph Theory, Combinatorics, and Numerical Analysis, with a dissertation focused on continued fractions. He then pursued a Master’s degree in Computational Statistics & Machine Learning at UCL, applying the Harmonic Equation for handwritten digit prediction. Following this, he completed another Master’s degree in Graph Theory at the London School of Economics and Political Science (LSE), focusing on Ramsey Numbers and combinatorial approaches. Dr. Kanthan then earned his Ph.D. in Mathematics and Computer Science at UCL, specializing in applied graph construction methods, locality-sensitive hashing, genetic improvement, and artificial intelligence under the guidance of Professors John Shawe-Taylor and Robin Hirsh.
His professional career began as an Algorithm Architect at Alcatel-Lucent, designing FIX engines and developing trading algorithms. He then worked as an Actuarial Analyst at Towers Watson before moving into AI research roles at Samsung Electronics Switzerland and iProov. His career in quantitative finance took off when he joined Credit Suisse as a Quantitative Researcher, where he worked for over a decade. During this time, he also consulted for Microsoft, Commerzbank, GMEX Group, and Orange Business Services, focusing on analytics, derivatives, and algorithmic trading.
In 2015, Dr. Kanthan co-founded DataSpartan, serving as a Managing Partner and leading research in AI and quantitative strategies. His expertise in AI led him to advisory roles at Crowdcube and Hexis Performance, contributing to AI-driven investment models and performance analytics. He also became an Associate Researcher at the UCL Centre for Blockchain Technologies.
How TurinTech started
Being highly involved in AI, Dr. Kanthan and future co-founders of TurinTech AI observed a big challenge in the industry: the complexity and inefficiency of optimizing AI code for machine learning models. They realized that while AI and machine learning had enormous potential to transform businesses, the optimization of AI models required specialized expertise and was often slow and resource-intensive. This made it difficult for many companies to fully embrace AI technologies.
Having worked in environments where technology could drastically improve business operations, Dr. Leslie Kanthan and his team wanted to automate and optimize the AI development process itself. They saw an opportunity to create smarter, more efficient tools that could enable enterprises to deploy AI solutions quickly and effectively, without needing deep technical expertise. This vision led them to the founding of TurinTech AI in the UK, where they set out to build a platform that could automatically optimize machine learning code, making it accessible and scalable for a wide range of businesses.
How TurinTech solves vibe coding challenges
TurinTech is tackling these challenges head-on by focusing on optimizing machine learning models, legacy systems, and generic software code. Dr. Leslie Kanthan emphasizes the importance of making AI more efficient, especially as businesses increasingly rely on computational resources.
shares Leslie Kanthan at the Tech Talks Daily Podcast. TurinTech addresses AI challenges in several ways:
- Optimizing AI models: The company has been a leader in machine learning model optimization, helping businesses reduce computational costs without compromising performance.
- Enhancing legacy systems: Many businesses operate on outdated infrastructure, making large-scale upgrades impractical. TurinTech focuses on improving the efficiency of existing systems rather than forcing companies to invest in new hardware.
- Reducing power consumption & costs: With AI models consuming increasing amounts of energy, sustainability is a growing concern. TurinTech optimizes battery consumption, memory usage, and CPU utilization to make AI applications more energy-efficient.
- Expanding beyond AI models: Leslie highlights a major shift in TurinTech’s approach:
shares Leslie Kanthan at the Tech Talks Daily Podcast. By focusing on both AI-driven and non-AI software optimization, TurinTech is ensuring that businesses can scale their AI adoption without spiraling computational costs. The future of AI isn’t just about innovation — it’s about efficiency, and it looks like TurinTech is leading the way.
In March 2025, TurinTech announced $20 million in funding to tackle inefficiencies in vibe coding. The company is launching Artemis, an “evolutionary AI” platform designed to optimize and validate enterprise codebases. Unlike traditional GenAI tools that focus solely on predicting code, Artemis refines and evolves it to enhance performance, security, and scalability.
TurinTech’s latest funding round includes a $15 million Series A led by Oxford Capital, bringing total funding to $20 million. The company already has early adopters, including major enterprises and banks, ahead of a full launch later this year.
Building an AI platform like TurinTech? See how we help founders turn machine learning ideas into real products.
The future of AI in сoding: insights from Dr. Leslie
While there are experts like James Cuda, the founder of Procreate, who are skeptical of AI’s role in creative fields, leaders like Dr. Leslie at TurinTech are embracing its potential to revolutionize software development and optimize existing systems. Dr. Leslie offers a unique perspective on the role AI will play in the future of coding, especially when it comes to vibe coding.
In his view, AI is not just a tool for automating repetitive tasks but a game-changer that will fundamentally reshape the way developers approach coding. For those who wonder is generative AI ready to replace designers or will AI replace developers, here’s what Dr. Leslie had to say about the future of AI and its impact:
says Leslie Kanthan at the Tech Talks Daily Podcast. Dr. Leslie emphasizes that AI is more than just a tool for streamlining existing systems — it’s a transformative force for the entire industry. He explains that AI will revolutionize the way we work by enhancing efficiency and accessibility across various domains. From software development and operational efficiencies to predictive analytics, AI will tackle challenges that were once deemed too complex or expensive. Ultimately, Leslie Kanthan believes AI will simplify our lives and enable businesses to scale faster.
