By Kesava Reddy, Chief Revenue Officer, E2E Networks
Large Language Models (LLMs) have emerged as a game-changer for businesses of all sizes, but their impact is particularly transformative for startups. To understand why, let’s look at the kinds of leverage startups have against established players and why AI is a great enabler for them.
Fledgling startups often navigate a landscape of limited budgets and tight timelines, even when fighting for the same customer base that a much bigger industry player might be after. Incumbents have brand recognition, vast resources, and established distribution channels. Yet, in many instances, innovative, tech-driven startups have disrupted entire industries.
How Do Startups Win?
So what leverage does startups have against larger incumbents? Speed is a key factor. Unburdened by legacy systems, startups can adapt and iterate quickly. This agility allows them to address unmet customer needs or offer a superior user experience, stealing market share from larger companies.
Startups also win through a higher tolerance for risk. They can experiment with disruptive technologies and business models. This willingness to embrace calculated risks allows them to find a foothold in overlooked markets or revolutionize existing ones. While incumbents may be slow to adapt, a nimble startup can seize the opportunity and become the new industry leader. Startups also have the ability to focus their attention on a niche market, and become a category leader there before a larger corp is able to customize their offering for that market. As venture capitalist Mary Meeker puts it, successful ones tend to “Move fast and break things.”
So, in many ways, the key for startups to win comes from their agility. This is where AI, and specifically LLMs, is emerging as a game changer for startups. Let’s look at some of the advantages that LLMs offer startups, and why they are revolutionizing the startup building process.
Faster R&D through LLMs
LLMs are like turbochargers for a startup’s agility. One instance of how they help is through accelerating the R&D cycle. Developing new products and features is a time-consuming process. LLMs, however, have been shown to be highly capable as coding assistants, helping developers code faster, identify bugs more quickly and innovate on new features more rapidly. In fact, as McKinsey found out in a study, developers can go up to twice the speed on coding tasks when utilizing Generative AI coding assistants.
Examples of some AI coding assistants that are increasingly being used by startups are CodeLlama and StarCoder, which can help supercharge the product building process. Increasing number of startups are deploying these open LLMs, connecting them with tools like Visual Studio Code, and giving developers the ability to innovate faster. The result is faster R&D, quick product launches, and rapid iterations based on feedback.
LLMs for Building Personalized Customer Experiences
A second example of how LLMs are increasingly being used by startups is for building personalized customer experiences. Using LLMs like Mistral, Llama2, Falcon or Solar, alongside an architecture that’s called Retrieval Augmented Generation (RAG), startups can quickly build Conversational AI chatbots that can leverage historical customer interaction data, and tailor their responses to the customers accordingly.
Since LLMs are great at natural language understanding (NLU) and natural language generation (NLG), these chatbots can communicate far more effectively with customers than the automated bots we have seen in the past. Moreover, with the emergence of Indic-language LLMs like OpenHathi, KanLlama, or Tamil Llama, and APIs provided by MeitY’s Bhashini, startups are looking at serving India’s massive user base in their native language, thereby transforming customer experience.
LLMs As Marketing Assistants
Another way in which startups are leveraging AI and LLMs is by harnessing them for creating marketing materials. LLMs are great at creating first drafts of blog posts, social media copies, translations, and even at personalizing messages for different audiences. They function especially well when a startup trains an open LLM on a company’s brand language, and provides it access to the company’s marketing collateral through a RAG architecture. This helps the LLM generate on-brand responses with high accuracy.
LLMs As Analysts
Finally, numerous startups are harnessing LLMs to analyze unstructured data. Historically, we have had SQL databases and other structured data sources that could be programmatically analyzed. However, for unstructured data like candidate resumes, research documents, and vendor contracts, companies have had to historically employ human labor, and that would often turn out to be operationally expensive for startups.
With LLMs, it is now possible to build a data analysis pipeline which not only analyzes a document, but also provides the right sources and references. This helps reduce costs dramatically, and gives a startup capabilities similar to what a larger player would have access to due to their human workforce.
Future Notes
The synergy between startups and Large Language Models (LLMs) is still young, but its potential to disrupt industries is immense. LLMs are poised to become invaluable co-pilots for human developers, designers, and marketers, seamlessly integrating into their workflows. Cloud-based access to powerful GPUs like H100 and A100 clusters will democratize AI, allowing even bootstrapped startups to leverage cutting-edge capabilities. This will blur the lines between startups and established players, fostering a more level playing field.
The future belongs to startups that can effectively leverage the power of AI and harness it to build agility and stay ahead of the curve.