In 2021, Fast Company listed Turing as one of the most innovative workplace companies. In great company with the likes of Asana, Atlassian, Gitlab, Slack, and Zoom, Turing defines itself as an international hiring platform that brings together remote software developers and leverages their unique AI vetting process to match the right teams with projects.
I met with Jonathan Siddharth, founder and CEO of Turing, to dive into his AI-powered tech services platform and his vision to unleash the world’s untapped human potential.
What catalyzed the birth of Turing came from the realization that traditional tech service firms were ill-equipped for the AI-driven era. Companies like Accenture continue to do manual sourcing, vetting of talent, manual matching. Turing is changing all this. According to Siddharth,
“We believe AI transformation is the new digital transformation. Every company is going to have to be an AI company and I’m sure you’re hearing the same where every company now needs an AI strategy. Every product is now an AI product. Every role is being transformed by AI. At Turing, we started the company because traditional tech services firms were simply not built for this. They continue to lack the efficiency that AI can bring. Turing is tech services reimagined from the ground up as an AI company. So, we asked ourselves, what would an AI powered Accenture look like?”
In 2020, at a time when the Pandemic mandated the move to remote work, Turing raised $32million in Series B funding with an ambitious plan to “help define the future of how companies source IT talent to grow.” Since 2018, the company has amassed a database of over 2 million engineers from over 150 countries with “approximately 100 or so engineering skills, including React, Node, Python, Agular, Swift, Android, Java, Rails, Golang, PHP, Vue, DevOps, machine learning, data engineering and more.”
Siddharth described how they uniquely transform and deliver this AI-powered Accenture. They leverage AI to autonomously identify, evaluate, and align suitable talent with corresponding opportunities. Secondly, they built Turing GPT, to accelerate the productivity of a software engineering team. As per Siddharth, “We combined that with our team of AI transformation experts who help companies in their transformation journey.” With a client list like Disney, Johnson and Johnson and Rivian, they can leverage Turing’s pre-vetted engineering talent for 1) staff augmentation, 2) custom projects designed to meet temporal requirements 3) and AI advisory services to assists companies in shaping their AI strategy and roadmap.
These operations are built over top of the Turing Talent Cloud—an expansive pool of over two million developers and growing, they claim as the world’s largest developer- focused database. This resource pool, combined with AI algorithms, streamlines the process of talent acquisition—sourcing, vetting, and aligning them with the right companies.
Growing up in Chennai, India, Jonathan Siddharth’s affinity for innovation became evident early on. He shared, “I was always fascinated by the ‘new’… I published my first research paper in AI when I was in my sophomore year, 2002. I wrote about using neural networks for self-driving cars.” After finishing college, he continued his AI research and pursued his PhD at Stanford, which was already ahead in Autonomous Driving. It was there that Siddharth crossed paths with his, now, cofounder, Vijay Krishan, CTO of Turing. “We both got bitten by the startup bug and we started company #1, which was an AI powered content recommendation company.”
The Spark in Finding Talent Outside of Silicon Valley
From this experience, they found it difficult recruiting great engineering talent. At the time they were at the heart of Silicon Valley, in Palo Alto, where they were fighting for the same limited talent pool as Google, Yahoo, Microsoft and Facebook. Out of necessity, they looked beyond Silicon Valley’s caliber of talent. “It was one of the best things we did”. That was 2012 and it was a move that demonstrated the potential for remote work long before it would bring to bear in 2020. “We were fortunate to work with some wonderful engineers from Poland, Canada, India, China and other parts of the world. And that was really the key to making the company successful. So, my first startup succeeded because of remote work in distributed teams.”
Following the acquisition of his first venture, Siddharth reflected on the persistent challenge they had conquered. “Afterwards, I took some time off to figure out what do next, and it became clear that this problem that we had solved for ourselves in my first startup was such a big unsolved opportunity.”
This realization laid the foundation for the inception of Turing in 2018, with the goal of building this AI powered tech services company “where both Fortune 500 companies as well as startups could push a button to spin up their engineering team instantly.”
Reflecting on Turing’s trajectory, Jonathan acknowledged the factors that have propelled the company’s growth. “With AI being used for automatically vetting engineering talent, matching them, we are thinking about ways to use AI to increase the productivity of a software engineering team. I just feel fortunate that Turing benefited from 2 tailwinds. The first was the tailwinds around remote work and now it’s the tailwinds around AI.
Optimizing Engineering Teams: Turing’s Paradigm of Flexibility and Efficiency
To be clear, Turing is not a recruiting company. The database of engineers are contractors loaned out for staffing or project needs. Does Siddharth believe there is a market to place typically higher-demanded software engineers for temporary projects vs. allowing companies to hire them outright?
Siddharth sees a shift in demand today, “So, my view is that the best way to build a tech company today is for 20 to 40% of your engineering headcount to be powered by long-term full-time contractors like from a platform like Turing. I wouldn’t say you should do that for 100% of your engineering headcount, but 20 to 40% and there are some studies that say that it should maybe even be 50%.”
He highlighted the importance of speed and flexibility and the advantages from Turing’s approach: “Imagine you were building an AI company like a Copilot for example. Let’s say you wanted to build like an iPhone app and Android app and a web app. Now, the old way of doing that would mean you have a budget for one engineer… that would build all three in sequence.”
He explained there is a better way that provides an organization exactly what they need: “But you don’t have to do that anymore. You could come to Turing and say, ‘I’m going to need to launch this on iOS, Android and the web.’ So, you’ll take three engineers from Turing’s talent cloud. They’ll building all three in parallel.”
This speed to market advantage allows the organization to apply resources only where they’re needed. Assume that once all three applications have been launched and the company decides the iOS app has achieved product market fit, therefore they will move more resources towards further developing that product with a backend engineer and simultaneously, wind down development on the web and Android app. This exemplifies the agility that Turing provides to scale up and scale down engineering teams as easily, effectively foregoing the cost of hiring, training and firing if they were to be employed directly by the organization.
Siddharth elaborated on their recent growth and their trajectory:
With their most recent funding round, Turing has secured a valuation cap of four billion dollars. Their prior funding round resulted in a post valuation of 1.1 billion dollars. Cumulatively, Turing has raised approximately 144 million dollars and has achieved unicorn status.
Siddharth explains these funds are earmarked primarily for expansion in enhancing their AI-powered vetting and matching engine, with a concentrated effort on research and development. An upcoming endeavor is the development of an AI-powered self-service system that allows individuals to construct their engineering teams through interactive consultations with Turing.
Siddharth describes the company’s impact: “The Turing team functions as a unit supercharged by an AI-powered exoskeleton.” With further sales and marketing efforts they are poised to build this AI-powered Accenture and take on their rival which has had the benefit of tenure and success.
Reflecting on the Engineering of the Future
The changing landscape for technology towards general AI will emerge the need for engineers to learn new languages and to adapt to disruptive environments that will reveal themselves in greater frequency. What will the role of the engineer look like in the next decade?
Siddharth responded with enthusiasm, stating, “Absolutely, there has never been a more opportune time to pursue a career in software engineering or computer science.” He suggested that computer science and AI education should be incorporated at the middle school level, alongside subjects like English and mathematics and advocated for introducing AI education at this stage given its growing relevance.
Siddharth emphasized a significant shift in the value proposition for software engineers. “The return on investment (ROI) for becoming a software engineer has now multiplied by 10,” he asserted. He attributed this dramatic increase to the projected 10-fold boost in software engineers’ productivity. This expected surge in productivity, according to Siddharth, will pave the way for a surge in entrepreneurial endeavors over the coming years.
Reflecting on the evolution of the startup, Siddharth recalled that a decade ago, startups often required a seasoned and skilled engineer alongside business experts. However, contemporary technological advancements such as AI-assisted tools enable a broader range of professionals to initiate startups. “Let’s say there is a talented journalist. She could start her AI company standing on the shoulders of some of these AI assisted tools.”
He foresees an accelerated growth path for junior engineers, positing that they could attain mid-level engineer proficiency in a shorter span. “Our head of engineering feels a mid-level engineer can now operate at the level of a senior engineer. AI is just going to amplify the productivity of everyone, and the key to all of this is everyone learning AI.”
Siddharth emphasized the importance of widespread AI education. He advocated for individuals acquiring at least a basic understanding of AI, as it is becoming a requisite for almost every job. He envisaged a future where AI integration is ubiquitous and diverse roles are infused with AI elements.
Anticipating the growth of AI’s potential, Siddharth expressed excitement about the continuous advancement of AI tools. He cited Turing’s own experience, noting a 30% increase in productivity using AI-accelerated tools. Jonathan Siddharth is optimistic as he looks forward to the transformative possibilities that AI holds for industries and professions.
Only time will tell whether the broader enterprise market is ready to move towards the newly built Turing paradigm. In the meantime, this company has slowly paved the way to streamline the process of talent discovery, evaluation and alignment that has the potential to reimagine the power of remote engineering teams.
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