Peter Wang is the CEO and co-founder of Anaconda. Prior to founding Anaconda (formerly Continuum Analytics), Peter spent 15 years in software design and development across a broad range of areas, including 3D graphics, geophysics, large data simulation and visualization, financial risk modeling, and medical imaging.
As a creator of the PyData community and conferences, he devotes time and energy to growing the Python data science community and advocating for increasing data literacy around the world. Peter holds a BA in Physics from Cornell University.
With more than 35 million users, Anaconda is the world’s most popular platform to develop and deploy secure Python solutions, faster.
What initially attracted you to computer science?
I started coding at a young age, without a formal computer science degree. While initially drawn to it for the thrill of commanding a computer to perform tasks, my interest deepened when I discovered the creative possibilities – crafting games and expressing ideas. For me, a computer transcends mere functionality; it’s an endless canvas for self-expression. In the early era of computing, creativity knew no bounds, and there was a seamless flow between different pursuits. However, with the current industrialization and layers of abstraction, unleashing creativity has become more challenging.
Could you share the genesis story behind Anaconda, Inc?
My co-founder and I started Anaconda in 2012, but the origins of the business can be traced back to when we were software consultants. We saw the developing grassroots adoption of the Python programming language for business data analysis and knew that a revolution was under way. Industries that required heavy numerical computing capabilities like finance flocked to Python, and over time the language saw rapid adoption in healthcare, manufacturing, retail, and every industry pursuing advanced analytics to make better business decisions. But despite the widespread organic growth of Python, we felt the industry was missing the real story: the massive need for high-performance advanced analytics tools that could be harnessed by non-programmers. At first, investors were uncertain of programming languages or open-source ecosystems and didn’t see the value in the Python data community that Anaconda had stewarded. But this practitioner-led growth strategy ultimately led to Anaconda and the Python ecosystem rapidly gaining adoption across every industry all over the world.
Anaconda is committed to fostering open-source innovation, why is open-source so important?
I am a firm believer that transparency and collaboration are key factors for successful development of technology and solutions for society as a whole. Open-source not only guarantees transparency, but also enhances collaboration and fosters an innovation culture among developers. The more perspectives and knowledge there are working together to develop solutions, the better the outcome. The principles behind open-source closely align with Anaconda’s mission to democratize technology and enhance education as well – open-source software provides valuable learning opportunities for developers, students, and enthusiasts where they can study the code, learn best practices, and gain practical experience by contributing to open-source projects.
In 2022 Anaconda launched PyScript, a web-based tool for coding in the browser and deploying apps with the click of a button. Could you share some details regarding this tool and what makes it so powerful?
After debuting the open-source PyScript project last year as proof of concept, in March 2023 we released PyScript.com, a site that allows anyone to build rich, interactive, shareable Python-powered web applications directly in the browser. This flexible coding platform has a plug-and-play modular development environment and can create next-generation web applications with Python-powered data interactivity and computation, drastically reducing the entry barriers that make programming overwhelming for 99% of citizens who don’t have existing coding skills. With this launch, Anaconda is increasing accessibility by providing a framework that equips anyone to gain experience in Python development.
The data science industry has boomed over the last decade as data-driven decision-making has become the norm—boosting data scientists to #3 on Glassdoor’s 50 Best Jobs in America for 2022. But while the industry is thriving, there is still room to upskill the current workforce and remove existing barriers of entry to those curious about the world of coding. This launch was the first step in democratizing data science. Additionally, individuals and organizations that focus on upskilling and reskilling will always be at a competitive advantage. By providing an online platform that anyone can access, without the burden of downloading files and configuring environments, PyScript provides a great opportunity to learn Python, the most popular programming language in the world.
What are your views on the future of coding?
The evolution ahead entails a surge in overall code production, with a significant portion generated by machines. However, human validation will remain integral. The conventional image of programming – inputting code into a text file – will transform. The future of constructing information systems will diverge from traditional coding practices, embracing a landscape where code is generated. I also predict that emerging systems will center around data specification and modeling, reshaping coding as we know it today.
Anaconda now serves over 35 million users, what do you attribute this success to?
I believe that we have reached this scale of users by offering a wide variety of educational materials and tools catered to all types of users – ranging from students to professional coders. As technological innovation continues, there has continually become more need for Python skills in nearly every industry. With our mission to democratize Python, making coding and the fundamentals accessible to all, we’re able to provide the resources needed to build skills for jobs now and in the future.
One of your passions is expanding access to data literacy, could you share some details regarding your efforts with this?
I believe that if we reach students as they get started with data science, we can make more significant progress on our mission to achieve worldwide data literacy. To support that, Anaconda has started engaging with high schools in the US and globally to host a Data Science Expo that brings students together to showcase Python skills, share innovative projects, and potentially win college scholarships. Additionally, we recently introduced Anaconda Learning, which offers over twelve courses, granting students who successfully finish them a certificate that can enhance their prospects of securing employment or advancing in their educational journey. Anaconda Notebooks is also designed to help people immediately jump into data science and Python coding. In May of 2023, Anaconda acquired EduBlocks, a free platform bringing fundamental coding skills to K-12 students and beginner professionals. Through the acquisition, EduBlocks will further Anaconda’s mission to democratize data and Python skills for the future workforce. As data science and AI/ML models continue to gain prevalence in work and life, Anaconda can be the source for guidance and training to take advantage of this new world.
Why should the future of AI be completely open?
Similar to my sentiments around open-source, transparency and collaboration will lead to more successful development of AI technology and benefit the greater good for society as a whole. While there is no denying that the AI arms race is an exciting moment in technology, the widespread usage of AI models could flood the Internet with information not generated by real-world events that will contaminate future training data sets for future models. This will lead to a “model cannibalism” effect where future models amplify and are forever biased by the output of past models. At the rate of new models rolling out, ethical debates surrounding AI, such as legal/copyright concerns, and bias in model training can no longer remain on the back burner. With open development comes more accessibility, and the ability for a wider group of backgrounds, skillsets, and experience to work together – creating a domino effect towards more successful (and ethical) outcomes.
What is your vision for the future of AI?
I anticipate the rise of more compact, comprehensible AI models. Resolving issues related to content rights and copyright will be pivotal. Expect widespread adoption of these AI technologies in real business scenarios and customer experiences. The focus will shift to guiding and training AI for positive utilization. This transition can be compared to the evolution of engines – moving from large to small, with a newfound emphasis on motor applications.
We now have access to a form of “basic” intelligence capable of performing tasks that once demanded human expertise – not necessarily difficult, but requiring dynamic agility. These are use cases previously overlooked due to the need for human intervention, but with the advent of AI, the once challenging is now achievable.
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