How Superteams.ai Helps Cloud GPU Companies Attract Developers

‘‘Artificial Intelligence, deep learning, machine learning — whatever you're doing — if you don't understand it, learn it. Because, otherwise, you're going to be a dinosaur within 3 years.’ – Mark Cuban

Introduction

In July 2023, Meta open-sourced its Llama2 large language model (LLM), including for commercial usage, giving some serious competition to ChatGPT. In the AI race that kickstarted after OpenAI released ChatGPT in November 2022, Meta came in a little late. Prior to Llama2, there were several other open-source LLMs that had caught the limelight, such as Falcon 40B, BLOOM, Vicuna, Guanaco and others that developers could build upon.

However, with the release of Llama2, Meta has suddenly offered developers, researchers, and the open-source community a powerful alternative to OpenAI’s ChatGPT API, a closed and proprietary model which has been growing in popularity amongst developers looking to integrate AI into their platforms. 

LLMs aren’t the only piece of machine learning (ML) technology that has grown in popularity amongst developers. If you study the ongoing activity on Hugging Face, Github, Hacker News or Twitter, and track the research papers being published on arXiv, you will quickly start discovering the various strands of work currently going on in the AI and ML ecosystem. 

Here are some, to give you an idea: 

– Text-to-Image models

– Text-to-Video 

– Text-to-Music, Text-to-Audio and Text-to-Speech

– ASR or Automatic Speech Recognition

– Object Recognition

– Recommender Systems

– Predictive Modelling and Forecasting

– RPA, or Robotics Process Automation

– Decision Systems

– Natural Language Processing and Sentiment Analysis

– Emotion Recognition

McKinsey estimates that generative AI will add $2.6-4.4 trillion annually across all the use cases they analyzed in their report titled, ‘The Economic Potential of Generative AI’. Compare this to the entire GDP of the UK, which stood at $3.1 trillion in 2021. 

A Massive Opportunity for Machine Learning Platforms, Hyperscalers and Cloud GPU Companies

There is an enduring idiom that comes to mind: ‘When in a gold rush, sell shovels.’ No matter which application or model eventually wins, they have to all eventually use GPUs. If NVIDIA’s surging stock price and market capitalization is not evidence enough, look at the bullish stance that numerous cloud GPU providers have been taking of late to capture this once-in-a-lifetime opportunity. 

As AI’s seismic impact causes a ripple effect across industries, it is increasingly clear that the ones who truly stand to win are the ones selling shovels – the GPU makers, the ML platforms, and the Cloud GPU companies. 

In this ecosystem, the most important piece of hardware is the GPU, where NVIDIA took an early lead, and currently has nearly 95% market share in the AI computing space, with very few competitors that operate at their scale. 

The layers above the GPU hardware business, however, is still an open arena. Azure, GCP, AWS and other cloud computing giants are all in a race to capture a significant slice of revenue driven by this emerging technology. However, even other early disruptors who make life easy for developers may stand to gain significant leverage from this AI disruption currently underway. 

The key to the game boils down to attracting a significant percentage of the 26-28 million developer base, who will eventually experiment and enter the AI ecosystem to upskill themselves or build new applications or embed AI into existing systems. 

Question therefore is, what strategy should you use as a platform to attract developers in 2023? 

Below, we have outlined several tactics we are helping our clients implement through our incredibly powerful Content + R&D Lab approach. 

Tactic 1: Create Learning Resources

One of the key opportunities during times of disruption or otherwise, is through simply providing educational content that developers look for, in a particular niche. 

Machine Learning, Deep Learning and Artificial Intelligence has brought on a whole new list of concepts that most developers currently are not familiar with. As the urgency to pick up this emerging new technology grows amongst the developer community, creating a pool of learning resources that helps developers familiarize and experiment with latest open source models, datasets and frameworks that’s aligned with the company’s platform offering is a tactic that can help draw in early developers. 

Let’s see some examples below: 

– There is an entire glossary of terms that beginners in AI and DL wouldn’t be familiar with. For instance, many developers do not yet understand the difference between supervised, unsupervised and semi supervised learning.

– Guidance on how the training process works, how to select the right model for the job, how to custom train, fine-tune and how to avoid ‘catastrophic forgetting’. These are concepts that can save developers time, and help them understand the considerations they need to take into account for building ML applications.

– The underlying hardware architecture of GPUs, such as how Tensor Cores work, or the difference between CUDA and Tensor Core, or how GPU memory bandwidth affects performance. 

– The popular frameworks for building AI / ML applications, and deep-dives in functionalities offered by AutoKeras, Pytorch and others. Understanding the NVIDIA NGC Catalog and how using NVIDIA APIs can help developers.

With our clients, we continually track over a 1000+ twitter handles, Github and Hacker News, and see the trending and emerging discussions in the space. For instance, the day Llama2 was open-sourced by Meta, we had already conducted an experiment and written about it. 

This helps our clients stay on the top of the trends, and attract developers who are keen to try out immediately after the launch of a new tech. 

Tactic 2: Explaining Emerging AI Technologies

When the transformer architecture was first introduced in the 2017 paper, ‘Attention is All You Need’, it was a groundbreaking moment for the Deep Learning world, and gave birth to a whole new paradigm by which DL models were being trained. 

In the near future, nearly every organization would need to embed AI/ML into their ecosystem to improve customer experience, automate workflows, increase efficiency and save costs. 

Doing so would require their technology teams to be aware of the latest developments in the AI ecosystem. Content that curates and explains the latest technologies can create a powerful funnel for Cloud GPU companies and Hyperscalers, and can inform their sales strategy. 

Several organizations we have been tracking have already taken steps to do so. For instance, Paperspace, which was recently acquired by DigitalOcean, has done a fabulous job of explaining the emerging technologies through a distributed pool of AI researchers and technical writers. Others have started weekly newsletters that help developers and CTOs keep in touch with the latest AI developments.

At Superteams.ai, we have built an entire network of such individuals who can help track and write for organizations, and ensure that they can compete in the long run through an organic funnel. 

Tactic 3: How-to Guides on Building Deep Learning and Generative AI Solutions

This is where the work we are doing with our partnering clients has been showing incredible promise. 

As beginners in the machine learning domain enter the ecosystem, they are constantly looking to try out and experiment with building new applications that helps them upskill and gives them a way to build expertise through practice. 

With our partnering Hyperscaler clients, we are bringing in a network of researchers, developers and technical writers, who have already conducted such experiments in the past, and can easily write how-to guides and showcase to developers a step by step approach to building Generative AI applications. This becomes a powerful resource through which an infrastructure or AI/ML company can unlock a plethora of use-cases through examples. 

For instance, individuals from our talent pool recently conducted an experiment where they created an entire video, from idea to final version, including the script, audio and video, through Generative AI. 

Similarly, they recently showcased how a developer from a SaaS company can custom train a chatbot and build a conversational AI technology that is trained on their domain. 

The best thing about this approach is that it’s written by an early developer, for an early developer, similar to how developer bounty programs have worked in the past. 

Tactic 4: Showcase Through Experiments

In addition to the tactic written above, it is also possible to create completely new use-cases that apply to domains where AI can have massive future implications. 

This tactic can be fine-tuned according to the niche which our client operates in, and we devise an entire strategy and schedule in collaboration with them.

As an example, a group of researchers-developers from our team are currently working on showcasing how LLMs can have security issues if someone corrupts the dataset through injection of noise that’s not perceptible to the human eye. 

Similarly, with one of our partnering clients, we are exploring the potential of AI in discovering bacteria clusters from blood sample images.

In a way, experimentation can only happen through a lab-like mindset, an approach we are actively exploring through our MediaAI initiative. Our eventual ambition is to build a virtual networked lab model, where we conduct experiments in collaboration with our clients, and explain our work to the vast pool of developers and entrepreneurs looking for ideas and opportunities. 

Tactic 5: Live Events Explaining an Emerging Generative AI Tech

Last, but not the least, a key tactic that’s being used by a number of our partners is to host hour-long live events where developers can learn how to launch, build, train new models on our clients’ infrastructure. This allows for Live Q&A sessions, code explanations and creates a space for discussions where our partners can easily talk about their USPs and why developers should choose them. 

This is not a new strategy, of course. Technology companies have done this since video conferencing solutions emerged. However, in a post-Covid world, this has become a significant part of the sales process. 

Many companies have the capability in-house to conduct such events. However, doing so requires planning, preparation, and occupying time from key resources. Therefore, our partnered companies often pull us in to facilitate the process and help host and conduct the event along with them. 

Our developers recently conducted an event where they showcased how Stable Diffusion can be privately trained and used to generate on-brand images at scale. The flow of such an event would be the following: 

– Introduce the company and the speakers

– Announce an offer or promo code or credits for participants who stick around to the end

– Discuss the experiment or walkthrough that would be showcased

– Showcase the final outcome that the developers would see

– Deep dive and showcase the steps 

– Demonstrate the final outcome and discuss challenges, if any

– Q&A discussion 

– Finally, give participants a way to get started right after, using the offer or promo code or credits

The entire event can be live-streamed to LinkedIn, YouTube and other platforms. It can also be recorded and shared with existing clients, a standard approach. 

The part where Superteams.ai adds the biggest value in this workflow is helping with the experiment, generating creatives and how-to guides, and creating learning resources from the experiment.

Conclusion

With the emerging potential of AI and Machine Learning technologies, we are looking at an once-in-a-lifetime opportunity for Cloud GPU and Hyperscaler companies to build long-term leverage. In this article, we have discussed the importance of building a strategy through which to draw in the most vital resource of all - the developer. Our approach helps companies do so in a meaningful way that, we believe, works as an additional set of tactics to pure SEO-play. 

Feel free to connect with us for a discussion on how we can collaborate, or write to us at info@superteams.ai.