4 Things AI Startups Need to Consider

The competition of the AI market is heating up. Companies that can remain innovative and manage their expenditure strategically will become industry front-runners. 

Whether you’re building your own AI, purchasing a model, or choosing a path that includes both, common issues of building an AI startup stem back to managing cash flow while maintaining innovation. Wherever you are in your startup journey, this blog is for you. Below, we’re covering four things to consider when building an AI company.

1. A Competitive Go-To-Market Strategy

Pricing and Packaging as an AI Vendor

As your company evolves, you will need to start charging for your products and services. However, developing the correct pricing and packaging isn’t always the simplest of tasks. And it’s even harder for an AI startup - you have to balance expensive infrastructure with competitive pricing all the while growing user adoption. 

The key to landing on the right price is to figure out your niche while also testing the market. Important questions to answer in pricing and packaging are looking at your own costs and runway, analyzing what competitors (both legacy and new) are charging, and learning more about your customers’ budgets. 

Positioning the Platform

Differentiating in a hyper-competitive market centers on finding a niche. Some AI companies will only have the bandwidth to provide singular outcomes while others will be prepared to offer multiple transformative solutions. At the very least, many AI success stories are companies that have launched a single solution, have clearly identified a use case, and are applying their AI technology effectively.

Correct positioning will give you better strategic insight. You’ll be able to:

1. Understand how your company will make revenue 
2. Determine how to scale your solution 

Deeply understanding and defining your go-to-market fit will give your company the edge over the competition when communicating value propositions. 

2. Infrastructure That Scales

Preparing for Rapid Growth

Scaling AI infrastructure is a massive undertaking. Initially, a large proportion of your budget will be focused on either acquiring or building your AI model and subsequently training and maintaining it. Once your AI model (or models) is ready for your customers, your budget will focus more on meeting infrastructure demand and scaling with your users. 

The Infrastructure Costs of Self-Built AI

Self-built AI models have certain benefits over externally sourced models. A company can have full control and direction over the model's training and IP rights over the collected data.

Alternatively, the cost of a self-built model is high, and it will take some time for the technology to advance enough for the cost to reduce and for the approach to become more accessible. 

Scaling with User Adoption

AI technologies have high latency requirements and large processing demands. These high requirements stem from data being accessed simultaneously, possibly thousands or millions of times per second. Data is being pulled, processed, and accessed all within microseconds and sometimes over large geographical expanses. To achieve such a high standard of processing, latency, and reliability, the most advanced tools are required.

Add tools and technologies to your tech stack that will give you some buffer as you grow. Especially in the early stages as you test and iterate your product, you may need to scale up or scale down your demand. As your customer base grows, you want tools that you can grow into. Maybe it’s packaging that lets you level up as needed or mechanisms to reassess needs over a contract. Ultimately, you want to maintain a high-quality product offering while keeping costs contained. 

Common AI Infrastructure Expenses

CPU (Central Processing Unit): The brain of a computer is the CPU. This system communicates with software applications and executes the software instructions.

GPU (Graphics Processing Unit): GPUs handle multiple computations simultaneously. They are ideal for parallel processing of AI algorithms and accelerate AI workloads.

TPU (Tensor Processing Unit): TPUs increase the performance of AI operations. They can be incorporated into hardware to boost tensor operations. 

FPGAs (Field-Programmable Gate Arrays): FPGAs allow hardware to be configured specifically to benefit computational tasks of AI applications.

ASICs (Application-Specific Integrated Circuits): ASICs can be tailored for specific AI tasks, improving performance and efficiency.

NNPs (Neural Network Processors): Critical for many AI applications NNPs accelerate the speed of computations.

Based on market reporting, the costs associated with this infrastructure can range from tens of thousands of dollars to millions of dollars.

3. Security and Growing Regulations

Ethics in AI Technology

As the popularity of AI grows, so does the debate over its ethical applications. What data is used, how is it used, how will it be applied, what influences the model and many other questions are on the top of minds for your customers. AI companies should establish an ethics board or at the very least develop an ethical usage of AI outline. This outline can dictate your company's position on AI within their market and help communication with customers. 

The main categories to clearly define in your ethical AI outline are: 

1. The ethical nature of your data and models and what powers them
2. The correct, ethical usage of your technology 

Data Processing and Collection

With any kind of data, there are regulations on how a company can store and use certain types of data. You’ll need to remain up to date with the industry regulations that oversee your use cases. Highly regulated industries like healthcare and financial services will have different regulations from retail or travel. Actively share the regulation details with your teams internally and include details in your company guidelines as you manage, store, and process vast amounts of data. 

Global Data

With the introduction of GDPR and other data-gathering limitations, there needs to be processes in place for AI when entering a global market. This can add additional expenses when sourcing data centers overseas and implementing additional security and compliance measures. Define the countries where you plan to sell your technology and determine the data requirements. 

Data Security

You need security for your own data, security for your customers’ data, and security for your customers’ customers’ data. There are different industry certifications that guide you on the right steps to take for security compliance. Ensure the other tools and technologies you use hold these certifications and work to earn them for your company.  

Data Accuracy

Collecting and storing data is one thing but the accuracy of the data can make or break the success of your product. You want to collect accurate data, find ways to filter out the inaccurate elements and include tags and additional fields that give you more insight. Over time, you’ll also want to institute data cleanliness initiatives to determine how often you review your data and when to remove old records. Clean data sets with accurate labeling are critical to a successful AI model.

Changing Regulations

We continue to see increased calls for tightening of regulations on AI-based companies and technologies. Larger companies such as Meta and Google have pushed back on these responses but regulations will arrive and depending on the governing policy it could have huge impacts on your company, industry verticals, or the go-to-market of your AI technology. When developing your AI go-to-market, make sure that it's flexible and has multiple channels for revenue.

4. Strategic Financial Management

Cash Flow Management

Regulating cash flow is a difficult task when AI infrastructure has such an expensive overhead and initial investment. As your product becomes more complex your infrastructure grows more expensive, and as your customer base grows, the demand for accessing the product increases operational expenses. 

Many startups are managing their cash flow by financing their larger investments. With debt financing, you can spread a large upfront cost into smaller monthly payments, avoiding one big hit to cash flow. The delayed or smaller, regular payments allow you to find a balance between your growing revenue and ongoing expenses. An added bonus, this process helps your company maintain cash liquidity that can be strategically reinvested elsewhere to continue growth.

Agility in a Fast-Paced Market

The AI market is growing fast and companies will need to capitalize on rapidly developing innovation.  Access to capital that can increase your agility and help you scale up the moment you need it can help you stay ahead. Gynger can approve financing in as little as 24 hours, allowing infrastructure purchases to be swift. 

Flexibility on Repayments

When acquiring new technology, some of your vendors may offer different packages and payment options. Typically, you’ll be able to negotiate better deal terms when committing to pay in full upfront. Gynger makes it easy for you to do this while still getting the benefit of repaying your technology purchases within terms that best suit your business - whether it’s monthly or net terms.

Financing For Your Customers

Gynger’s financing power can also be leveraged for selling your AI-powered products and solutions. AI startups have the opportunity to stand out in the marketplace and exceed customer expectations by bringing the best of embedded financing into their platforms. When navigating the complexities of selling products in a busy AI market, offering easier and quicker ways for customers to pay will give your company the edge over similar technologies. Embedded financing with Gynger ensures you receive full payment of your products upfront and you collect on payments instantly.

Extending Cash Runway Will Empower Your AI Adoption

While embarking on building an AI product in a competitive market, the four areas we covered will help you prepare for success. A competitive go-to-market strategy, infrastructure that scales, keeping up to date on security and regulations, and managing your finances strategically each give you areas to stay ahead of the competition. Although they each have costs, the key to maintaining rapid innovation will lie in your company's ability to remain cash-fluid. 

Rather than lowering your available capital with big, upfront expenses, explore financing options that support you and your business in building the strongest infrastructure and access to the best resources for developing an AI.

Don’t let these large expenses get in the way of your innovation, make your strategic AI purchases with Gynger.