Jonathan Siddharth


About Jonathan Siddharth

Jonathan Siddharth is the CEO and co-founder at Turing, world's largest Intelligent Talent Cloud that uses AI to source, vet, and match developers worldwide.

Generative AI LLMs
AI Services

13 Generative AI and LLM Developments You Must Know!

Generative AI and LLMs have transformed the way we do everything. This blog post shares 13 developments in the field that are set to take the world by storm this year.

The tech world is abuzz with innovation, and at the center of this whirlwind are generative AI and large language models (LLMs). Generative AI is the latest and, by far, the most groundbreaking evolution we’ve seen in the last few years. Thanks to the rise of powerful LLMs, AI has shot onto the world stage and transformed the way we do everything—including software engineering.

These innovations have begun to redefine our engagement with the digital world. Now, every company is on an AI transformation journey, and Turing is leading the way. 

In this blog post, I have shared a few things related to generative AI and LLMs I find cool as an AI nerd. Let’s get started. 

1. Optimizing for the next token prediction loss leads to an LLM “learning” a world model and getting gradually closer to AGI.

What does this imply? 

This refers to the LLM training process. By optimizing for the next token prediction loss during training, the LLM effectively learns the patterns and dynamics present in the language. Through this training process, the model gains an understanding of the broader context of the world reflected in the language it processes. 

This learning process brings the LLM gradually closer to achieving artificial general intelligence (AGI), which is a level of intelligence capable of understanding, learning, and applying knowledge across diverse tasks, similar to human intelligence.

2. The @ilyasut conjecture of text on the internet being a low-dimensional projection of the world and optimizing for the next token prediction loss results in the model learning the dynamics of the real world that generated the text.

Ilya Sutskever, cofounder and former chief scientist at OpenAI, suggested that text on the internet is a simplified representation of the real world. By training a model to predict the next word in a sequence (optimizing for the next token prediction loss), the model learns the dynamics of the real world reflected in the text. This implies that language models, through this training process, gain insights into the broader dynamics of the world based on the language they are exposed to.

3. The scaling laws holding and the smooth relationship between the improvements in diverse “intelligence” evals from lowering next-word prediction loss and benchmarks like SATs, biology exams, coding, basic reasoning, and math. This is truly emergent behavior happening as the scale increases.

As language models scale up in size, they exhibit consistent patterns, also known as “scaling laws holding.” Improvements in predicting the next word not only enhance language tasks but also lead to better performance in various intelligence assessments like SATs, biology exams, coding, reasoning, and math. This interconnected improvement is considered truly emergent behavior, occurring as the model’s scale increases.

4. The same transformer architecture with few changes from the “attention is all you need” paper—which was much more focused on machine translation—works just as well as an AI assistant.

“Attention is all you need” is a seminal research work in the field of natural language processing and machine learning. Published by researchers at Google in 2017, the paper introduced the transformer architecture, a novel neural network architecture for sequence-to-sequence tasks. 

Today, with minimal modifications, this transformer architecture is now proving effective not just in translation but also in the role of an AI assistant. This highlights the versatility and adaptability of the transformer model—it was initially designed for one task and yet applies to different domains today.  

5. The same neural architecture works on text, images, speech, and video. There’s no need for feature engineering by ML domain—the deep learning era has taken us down this path with computer vision with CNNs and other domains.

This highlights a neural architecture’s adaptability to work seamlessly across text, images, speech, and video without the need for complex domain-specific feature engineering. It emphasizes the universality of this approach, a trend initiated in the deep learning era with success in computer vision using convolutional neural networks (CNNs) and extended to diverse domains.

6. LLM capabilities are being expanded to complex reasoning tasks that involve step-by-step reasoning where intermediate computation is saved and passed onto the next step.

LLMs are advancing to handle intricate reasoning tasks that involve step-by-step processes. In these tasks, the model not only performs intermediate computations but also retains and passes the results to subsequent steps. Essentially, LLMs are becoming proficient in more complex forms of logical thinking that allow them to navigate and process information in a structured and sequential manner.

7. Multimodality—LLMs can now understand images and the developments in speech and video.

LLMs, which were traditionally focused on processing and understanding text, now have the ability to “see” and comprehend images. Additionally, there have been advancements in models’ understanding of speech and video data. LLMs can now handle diverse forms of information, including visual and auditory modalities, contributing to a more comprehensive understanding of data beyond just text.

8. LLMs have now mastered tool use, function calling, and browsing.

In the context of LLMs, “tool use” likely refers to their ability to effectively utilize various tools or resources, “function calling” suggests competence in executing specific functions or operations, and “browsing” implies efficient navigation through information or data. LLMs’ advanced capabilities have now surpassed language understanding, showcasing their adeptness in practical tasks and operations.

9. An LLM computer (h/t @karpathy) made me reevaluate what an LLM can do in the future and what an AI-first hardware device could do.

A few months ago, AI visionary Andrej Karpathy touched on a novel concept that created waves across the world: the LLM Operating System.

Although the LLM OS is currently a thought experiment, its implications may very well change our understanding of AI. We’re now looking at a future not just built on more sophisticated algorithms but one that is based on empathy and understanding—qualities we’ve originally reserved for the human experience.

It’s time we rethink the future capabilities of LLMs and gauge the potential of AI-first hardware devices—devices specifically designed with AI capabilities as a primary focus. 

10. Copilots that assist in every job and in our personal lives.

We’re living in an era where AI has become ubiquitous. Copilots integrate AI support into different aspects of work and daily life to enhance productivity and efficiency.

AI copilots are artificial intelligence systems that work alongside individuals, assisting and collaborating with them in various tasks. 

11. AI app modernization—gutting and rebuilding traditional supervised ML apps with LLM-powered versions with zero-shot/few-shot learning, built 10x faster and cheaper.

AI app modernization is all the buzz today. This process involves replacing traditional supervised machine learning apps with versions powered by LLMs. The upgraded versions use efficient learning techniques like zero-shot and few-shot learning through prompt engineering. Moreover, this process is faster and more cost-effective, delivering a quick and economical way to enhance AI applications.

12. Building fine-tuned versions of LLMs that allow enterprises to “bring their own data” to improve performance for enterprise-specific use cases.

Building customized versions of LLMs for enterprise applications is on the rise. The idea is to “fine-tune” these models specifically for the needs of a particular business or organization. The term “bring your own data” suggests that the enterprise can provide its own dataset to train and improve the LLMs, tailoring them to address unique challenges or requirements relevant to their specific use cases. This focuses on adapting and optimizing LLMs for the specific needs and data of an enterprise to enhance performance in its particular context.

13. RAG eating traditional information retrieval/search for lunch.

Advanced generative AI is outperforming traditional information retrieval/search. If you’re considering leveraging it, think about

-how you should be applying generative AI in your company

-how to measure impact and ROI

-creating a POC before making it production-ready

-the tradeoffs between proprietary and open-source models and between prompt engineering and fine-tuning

-when to use RAG

and a million other technical, strategic, and tactical questions.

So, what do these LLMs AI developments mean for your business?

The world has changed. AI transformation has become indispensable for businesses to stay relevant globally. Turing is the world’s leading LLM training services provider. As a company, we’ve seen the unbelievable effectiveness of LLMs play out with both our clients and developers. 

We’ll partner with you on your AI transformation journey to help you imagine and build the AI-powered version of your product or business. 

Head over to our generative AI services page or LLM training services page to learn more.

You can also reach out to me at

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By Feb 19, 2024
Unicorn Announcement-03 (1)
For Employers

Hello Remote-First World. Turing Is Now a Unicorn! is now a unicorn following the Series D funding, bringing the total raised to over $140 million and Turing’s valuation to more than $1 Billion.

The last 12 months have been an incredible ride. If I had to sum it up in one word, it would be hypergrowth. Today, we’re happy to share with the world that we’ve raised our Series D financing round, bringing our total raised to over $140 million and Turing’s valuation to more than $1 billion. Turing has entered unicorn territory, scaling rapidly since our Series B in November 2020.

We Grew 9x Over the Last 12 Months  

Our unicorn journey came as a result of fast growth on multiple fronts. Our developer pool grew 9x, increasing our developer community to over one million. Hundreds of companies now build on top of Turing including Coinbase, Redbull, and Reddit. is a unicorn

We Grew 9x Over the Last 12 Months

We Partnered With World-Class Investors to Grow Even Faster and Build a Category Defining Company

WestBridge Capital, our Series B lead investor, also led our Series D financing round. Our seed round lead investor, Foundation Capital, and new investor, StepStone Group, also participated. WestBridge Capital is a $7 billion fund with deep expertise across SaaS and IT services. They have invested in global IT service companies such as Cognizant Technology Solutions and Global Logic. With under $4 billion under management, Foundation Capital, has invested in companies like Netflix, Uber & Solana. Greenspring Associates is a late-stage growth equity fund with $22 billion under management with investments in leading talent clouds like WorkRise (RigUp) and Trusted Health.

It’s been great partnering with Sumir Chadha, co-founder and MD of WestBridge Capital, and Ashu Garg, General Partner at Foundation Capital, on our Board. We’re delighted to welcome John Avirett from StepStone Group, whose strong thesis on the power of verticalized talent clouds made StepStone a natural fit. 

Other investors in the round include AltaIR Capital, HR Tech Investments LLC (an affiliate of Indeed), Brainstorm Ventures, Frontier Ventures, Modern Venture Partners, and the Plug and Play Scale Fund. 

Turing’s oversubscribed unicorn round raised over $140 million in capital. A subsequent SAFE opened at a $4 billion valuation cap is also oversubscribed. 

You can read more about Turing’s unicorn journey in the exclusive TechCrunch story or press release. is a unicorn

Sumir Chadha, M.D. WestBridge Capital, on

We Are Building a World-Class Team 

The incredible team at Turing has worked super hard to reach this significant milestone. This success would not have been possible without the impactful contributions of the Turing team (including our executive team – my co-founder Vijay, Zan, Prakash, Sudarshan, Akshay, Cathleen, Eric, Deepak, Aditya), our board, our investors, and our amazing customers and developers. We are especially grateful to our early customers, developers, and team members who took a bet on Turing and believed in our mission from the very beginning. Thank you. 

If this sounds like fun, join our growing team! Email or check out our open jobs here. We’re hiring for product, growth, engineering, data science, sales, marketing, operations, and finance. 

Turing’s Growth Has Accelerated in a Remote-First World

Now every company is in a race to reap the benefits of remote engineering talent. 

But remote is hard and traditional solutions weren’t built for this.

Headhunters and staffing agencies lack global reach and have no specialized vetting for engineers. Recruiting marketplaces provide hit or miss quality, lack vetting, and are gig-focused, making them unable to attract the best talent. IT services companies are limited to sourcing from local talent pools rendering them incapable of attracting the best talent.

We asked ourselves a simple question: could we replace all of this with software? And we did.

Turing’s Intelligent Talent Cloud Helps Companies Easily Hire the Best Software Developers in the World 

Our platform:

  • Sources engineers planet-wide and vets them to a Silicon Valley standard.
  • Leverages AI to optimize the matching of developers with companies.
  • Makes it easy for companies to manage and collaborate securely with remote talent.

Turing’s creating a new category, that we call the Intelligent Talent Cloud. It’s a distributed team of developers in the cloud, sourced by software, vetted by software, matched by software, and managed by software. is a unicorn

Spin up Your Engineering Dream Team with Turing

Turing does all the heavy lifting intelligently through sophisticated machine learning. 

Software-driven intelligence is present in Turing products such as automated smart vetting, where machine learning algorithms determine how to best vet a developer on various skills and levels. Similarly, our matching process also leverages AI for search ranking to choose the best developers to recommend for any open job. is a unicorn

Turing’s Intelligent Talent Cloud Helps Companies Easily Hire the Best Software Developers in the World

Join Us on Our Mission to Unleash the World’s Untapped Human Potential

Before Turing, an individual’s ability to contribute to the world was limited by geography. Today, if you’re a talented software developer you can work for the best companies in the world, not just the ones near your home. Instead of people moving to where the jobs are, the intelligent talent cloud brings jobs to the developer. Bridging this talent-opportunity gap and eliminating the geo lottery is good for the world. I am excited for all the new amazing inventions, products, drugs, tools and innovations we will see as a result of this shift. 

We have an opportunity to build a once-in-a-generation company. We are experiencing a generational shift in the way we work, right before our eyes. We are fortunate to be building a platform right at the heart of this shift. 

Let’s make the world a better place by unleashing the world’s untapped human potential. 

Join the Intelligent Talent Cloud Movement



Jonathan, Founder & CEO

P.S.: We are fortunate to partner with an incredible set of investors who have been super helpful at every step of our journey.

Thank you for being awesome partners –  WestBridge Capital, Foundation Capital, StepStone Group, Modern Venture Partners, HR Tech Investments LLC, Frontier Ventures, AltaIR Capital, PNP Scale Fund, Mindset Ventures, Founders Fund, Chapter One Ventures, Plug and Play Tech Ventures, UpHonest Capital, Ideas & Capital, 500 Startups Vietnam, Canvas Ventures, B Capital, Peak State Ventures, CapitalX, Stanford StartX Fund, Amino Capital, Spike Ventures, Visary Capital, Brainstorm Ventures, Joint Journey, Gaingels, Adam D’Angelo, Gokul Rajaram, Cyan Banister, Beerud Sheth, Dmitry Chernyak, Lorenzo Thione, Manish Narula, Aditya Jami, Artem Bosov, Sanya Ohri, Maxim Shipilov, Mikhail Fisher, Steven Hellman, William Hughes, Josh Browder, Nirav Shah, Andy Raskin, Bakht Niyazov, Anes Kaldybayev, Christopher Nguyen & Ruby Chen, Dave Franke, Eduard Galyamov, Elena Petrova, Evgenii Prensniakov, Shariq Rizvi, Kirtika Ruchandani, Maksim Matcin, Manik Gupta, Marina Polskaya, Mykhailo Raitsyn, Nikolai Guzakov, Nikolay Kaginyan, Oleg Bogumirskiy, Solovev Sergeevich, Stephen Osborn & Meredith Osborn, Valentine Zavgorodnev, Timofei Andrianov, Anna Mikhaylova, Yanovskiy Oleg, Alevtina Beloglazov, Siqi Chen, Yi Ding, Sunil Rajaraman, Parakram Khandpur, Kintan Brahmbhatt, Cameron Drummond, Kevin Moore, Sundeep Ahuja, Auren Hoffman, Greg Back, Sean Foote, Kelly Graziadei, Bobby Balachandran, Ajith Samuel, Aakash Dhuna, Adam Canady, Steffen Nauman, Gordon Chang & Victoria Sandin, Sybille Nauman, Eric Cohen, Vlad V, Marat Kichikov, Piyush Prahladka, Manas Joglekar, Vladimir Khristenko, Tim and Melinda Thompson, Alexandr Katalov, Joseph and Lea Anne Ng, Jed Ng, Eric Bunting, Rafael Carmona, Jorge Carmona, Viacheslav Turpanov, James Borow, Ray Carroll, Suzanne Fletcher, Denis Beloglazov, Tigran Nazaretian, Andrew Kamotskiy, Ilya Poz, Natalia Shkirtil, Ludmila Khrapchenko, Ustavshchikov Sergey, Maxim Matcin, and Peggy Ferrell. 

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By Dec 23, 2021