What if I say that doing market research for startup or more precisely, market analysis is the backbone of any business? All startup owners must invest a good amount of time and effort in this sole activity. Being a startup owner, you must understand what actually market research for startups is all about. In simple terms, it is the process of gathering qualitative and quantitative data to assess the overall situation and structure of the market, which in turn helps you gauge the possibilities of ensuring long-term strategic gains for your startup. Once the data is with you, you can use mathematical and statistical tools to analyze and interpret it.
Here is our Q1 2017 summary report on the Artificial Intelligence startup sector. The following report includes a startup landscape overview, graphical trends and insights, and recent funding and exit events. We are currently tracking 1,731 Artificial Intelligence (AI) companies in 13 categories across 69 countries, with a total of $13 Billion in funding. Click here to learn more about the full Artificial Intelligence market report.
In recent years, the term Big Data has become the talk of the town, or should we say, the planet. By definition, big data analytics is the complex process of analyzing huge chunks of data, trying to uncover hidden information -- common patterns, unusual relationships, market trends, and above all, client preferences. All these are taken into careful consideration and big decisions are made based on the calculations, with high hopes of success. When described as such, it seems that an average entrepreneur would simply jump at the opportunity to use big data for their startup, yet that is not as common as one may expect. We're going to offer several possible explanations for why startup owners are not keen on investing in Big Data .
"Startups" in semiconductor chip design space had been a rarity since the dot-com crash in the early 2000s. Chip design requires massive development cost as design cycles are multi-year long with dependence on (1) expensive EDA (Electronic Design Automation) tools for design and (2) foundries for manufacturing -- both of which are highly advanced technologies with very few players in the world. Long design cycles from the conception of an architecture specification to its tapeout (tapeout is when a chip design is frozen & sent to a semiconductor foundry for manufacturing) plus time it takes to develop a SW stack to program new architectures further delays the point of revenue generation for such companies. Initial high investment costs with delayed revenue and delayed improvement in gross-margin had caused major market consolidations after the 2000 dot-com crash and had made semiconductor chip startups less attractive for venture capital funding. However the advent of AI in the last 8 years with its unique computational requirement has exposed newer opportunities for domain-specific ASICs to be, once again, a high-risk-high-gain proposition for venture funding. Introduction of Tensor Processing Unit (TPU), which is a chip designed specifically for Deep Learning (DL constitutes most of AI these days), by Google in 2017 demonstrated the possibility of building a domain-specific chip solution by a new player (new in terms of building ASICs) and cross validated the presence of a lucrative market for investors.
Few venture-capital investors have forgotten the story of Pebble: In 2012, after every VC firm on Sand Hill Road had passed on investing, the smartwatch startup raised more than $10 million on crowdfunding site Kickstarter. It was an unheard-of amount for a crowdfunding campaign, and the resulting hype made Pebble an internet sensation. Then the VCs, suffering from FOMO, begged Pebble to let them invest. The startup eventually raised a total of $59 million. Investors have been loath to repeat the mistake ever since.