Reality of Bootstrapping

sasha uritsky

Aug 17, 2015

Going after investors? Do you know that less than 1 percent of startups actually raise VC (or angel) capital, which means that the vast majority are self-funded. Yet the main reason for it simply lies in the inability of most companies to find investors.

Bootstrapping, however, has several strategic advantages for your company's future growth. Perhaps the biggest is retaining the majority of shares and control over the strategy and direction your company is moving towards.

It also teaches financial discipline. Bootstrapping at the start helps to understand the importance of revenue and cash flow, as opposed to unabridged product development, and keeps you connected to your company's financial reality. Only when profitability increase do you then green-light new opportunities, increased risk-taking, and growth acceleration.

In reality, the founders are expected to be flexible. While entrepreneurs have certain intentions and philosophies when they are starting out, a hallmark trait for successful founders is the ability to adapt to changing environments and opportunities.

Sometimes, that means waiting a long time to generate the financial metrics that really matter, revenue and profit. By challenging your leadership team to focus on building the business organically and figuring out how to make the company consistently profitable on a model that can scale without VC capital, you make your company more valuable to future investors.

Interested in reading more? Check out our other blogs:

Abundance of Information Often is a Liability

A massive change has occurred in the world during the last ten to twenty years. Until recently and throughout the history of mankind information was hard to access. Obtaining and sharing information was either a laborious process or impossible, and the underlying assumption was that information can never be enough.

Today, of course, we have the opposite picture. Not only information is easily available, it keeps pouring in from a growing number of sources, and we continuously find ourselves in situations when there is more information than we want or able to process.

A major task we, as species, are facing is therefore how to reduce or filter out relevant information. It is, to repeat, in direct opposition to the task we’ve been accustomed to during all previous centuries, which was how to obtain information.

Since this change took place only recently, within a lifetime of one generation, we didn’t have time to develop efficient set of procedures to address the new problem. But the work has started and will only accelerate with time.

WHAT IS AI TRAINING

AI training is a critical part of conversational AI solutions, a part that makes AI software different from any kind of software previously created.
AI training is not coding.
Unlike all other existing software which is fully coded.

Let us consider a simple example:
We create chatbots for two companies, one company is selling shoes, another is selling cars. From the software standpoint it is one chatbot solution running as an online service accessed remotely or a program available locally. In both cases they are two identical instances of the same software (one instance for the shoes company, another for the cars company).
Yet, for the first company the chatbot is supposed to talk about flip-flops, summer shoes, high heels and so on. For the second company, however, the chatbot is not expected to know any of that. Instead, the chatbot should be able to support conversations about car brands, car models, should know how to tell Toyota Camry from Toyota Corolla, etc. This shoes and cars knowledge is not programmable. It is trainable. It is not coded, instead it is a part of language processing capability that AI solutions like chatbots have. And herein lies the major differentiation and advantage of the AI solutions compared to traditional software.

How to train AI?
There are several ways to do it. Sometimes AI system can train itself, improve its linguistic ability over time. It also can be trained by professional linguists. And in some cases, by the users. The latter is the desirable scenario because businesses know better than anybody else what they want their chatbot to talk about.
It is not easy, given the existing state of AI technology, and usually requires a high level of technical knowledge. You may have heard mentions of intents and entities in chatbot discussions. These are examples of linguistic elements AI training is currently based on.
Without proper understanding of what these linguistic elements are and how language acquisition process works in existing AI systems it is better to leave AI training to professional linguists.