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What are the main obstacles that are preventing AI startups from scaling up? – Thought Leaders




By Salvatore Minetti, CEO, Fountech.Ventures

The promise of artificial intelligence (AI) has undoubtedly captured the imagination of many investors over the past decade. Fuelled by strong public interest, the technology has become a real force for good, promising to deliver solutions with potential to solve some of the world’s biggest issues.

Relative to other emerging technologies, AI companies were the leading investment category globally in 2019, securing over $23 billion in financing according to Tech Nation.

However, AI companies require more than just investment to truly thrive in the current climate. Indeed, the issue is not so much the shortage of start-ups as it is the shortage of scale-ups.

To truly push this discipline forward, it is time that we ramp up our efforts to nurture only the most innovative businesses towards long-term success, so that they can become formidable companies. This begs the question: what are the obstacles holding AI businesses back from growing beyond the start-up phase?

Determining ‘true’ AI businesses

It is no secret that the tag ‘AI’ has become ubiquitous, with companies using the term left, right and centre in order to secure investment. The problem with this is that some companies without AI at their core are holding back progress in the sector at large, hindering the development of progressive solutions.

These issues with semantics make it more difficult for investors to determine which businesses actually use ‘true’ AI, and which don’t. Indeed, a recent MMC Ventures report revealed that two fifths of Europe’s AI start-ups don’t actually use AI in any of their products. Examples like this serve to highlight how pervasive the misuse of the term is. Undoubtedly, conflating the meaning of a product or service can not only lead to overspending and poor execution, but also a business’ ultimate downfall when it is outcompeted by those with more clarity and focus.

Investors would therefore do well to avoid this fate by vetting companies thoroughly early on in the process. This can be achieved by asking key questions, such as ‘does this company derive its competitive advantage from the use of AI?’, and ‘will this company propel the sector forward?’. This way, resource can be spent more valuably on companies with scalable technical solutions and real competitive edge.

Start-up stumbling blocks

In the deep-tech arena, ambitious young teams generally have the determination and technical expertise required to design and create an innovative product. However, powerful concepts aren’t always enough to guarantee the success of a new business venture, and too much focus on the technology could stymie its progress.

The lack of clear metrics for AI startups is particularly challenging; it is difficult to measure what makes a ‘good’ AI company. The hype surrounding AI and its growing popularity has also given rise to fierce competition, which means that founders need to be particularly attuned to the obstacles they will face.

Some fundamentals are important for every business. For one, entrepreneurs must be able to demonstrate that they are addressing a large and important problem – and show why they are in the best position to solve it. Perhaps even more importantly, businesses need to establish whether people will be willing to pay good money for their solution.

AI start-ups will generally fall at many of the same hurdles as their more traditional counterparts. Another CB Insights report revealed the most common reasons that budding entrepreneurs might fail on their way up to the top, which included a lack of market need for the product, not having the right team, and being out-competed by other businesses.

The first of these demands particular attention: the blight of so many tech startups is that they build the product, and then hope that somebody wants it. A failure to take the appropriate steps at the outset to understand the potential fit and demand means that the final product doesn’t ultimately capture the attention of the target market.

For AI businesses, however, there are additional elements that must also be considered. The team should be able to demonstrate that their AI is truly adding value to the data they are using – and not just being used as a smokescreen. Does the AI help explain patterns in the data, derive accurate explanations, identify important trends and ultimately optimize the use of the information?

If not, they must question whether they should really be selling themselves as an AI startup. There is a real risk that resources will be spent needlessly on building and marketing a solution that does not truly solve a problem using artificial intelligence. Ultimately, such businesses are likely to lose their vision over time and will fail to live up to the mark they might have envisaged for themselves. They may also struggle to secure funding; after all, most VCs will not want to risk an investment into a technology that is ambiguous.

Young teams also tend to face roadblocks when it comes to the financial side of things: AI start-ups are either under-funded from the outset or burn more cash than necessary. To achieve sustainable growth, fledgling companies need to be able to plan beyond the development budget and create a scalable commercial model that will stand the test of time. Granted, this is no easy feat with limited business nous.

Nurturing AI start-ups to success

Many of these missteps boil down to the fact that start-ups often fall short where appropriate mentorship and business acumen are concerned. Indeed, most would benefit from some additional expertise to navigate common stumbling blocks.

It is fundamental therefore that company founders work with third-party advisors to compensate for any gaps in knowledge. Young teams need mentors to help manoeuvre unfamiliar territory, and to provide additional legal, financial, and logistical guidance.

Ultimately, simply financing a project just isn’t enough. It is essential that we work to provide a more holistic model to support fledgling AI start-ups, so that companies are set on the path to commercially scalable projects. It is only by providing specialist support and assistance with the more fundamental aspects of business – as well as access to talent, capital and peer networks – that we can really push the needle forward in pioneering AI technology.

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Salvatore Minetti is the CEO of Fountech.Ventures, which acts as venture builder and investor for deep tech and AI startups. With a presence in Austin, Texas, US, and London, UK, the company supports startups through the stages of ideation, development, commercialisation and funding.


Appen’s State of AI Annual Report Reveals Significant Industry Growth




Appen Limited (ASX: APX), the leading provider of high-quality training data for organizations that build effective AI systems at scale, today announced its annual State of AI Report for 2020.

The State of AI 2020 report is the output of a cross-industry, large-organization study of senior business leaders and technologists. The survey intended to examine and identify the main characteristics of the expanding AI and machine learning landscape by gathering responses from AI decision-makers.

There were multiple key takeaways:

  • While nearly 3 out of 4 organizations said AI is critical to their business, nearly half feel their organization is behind in their AI journey.
  • AI Budgets greater than $5M doubled YoY
  • An increasing number of enterprises are getting behind responsible AI as a component to business success, but only 25% of companies said unbiased AI is mission-critical.
  • 3 out of 4 organizations report updating their AI models at least quarterly, signifying a focus on the model’s life after deployment.
  • The gap between business leaders and technologists continues, despite their alignment being instrumental in building a strong AI infrastructure.
  • Despite turbulent times, more than two-thirds of respondents do not expect any negative impact from COVID-19 on their AI strategies.

One of the key findings is that nearly half of those who responded feel their company is behind in their AI journey, this suggests a critical gap exists between the strategic need and the ability to execute.

Lack of data and data management was reported as a main challenge, this includes training data which is foundational of AI and ML model deployments, so, unsurprisingly, 93% of companies report that high-quality training data is important to successful AI.

Organizations also reported using 25% more data types (text, image, video, audio, etc.) in 2020, compared to 2019. Not only are models getting more frequent updates, but teams are using increasingly more data types, and that will translate in an increasing need for investment in reliable training data.

One key indicator of exponential growth of AI was the rapid YoY growth in AI initiates. In 2019, only 39% of executives owned AI initiatives. In 2020, executive ownership of AI skyrocketed to 71%. With this increase in executive ownership, the number of organizations reporting budgets greater than $5M also doubled.

Global cloud providers gained significant traction as data science and ML tools compared to 2019. This may be due to increased budget and executive oversight. What is even more impressive is the increase of respondents who are reporting using global cloud machine learning providers which are identified as: Microsoft Azure (49%), Google Cloud (36%), IBM Watson (31%), AWS (25%), and Salesforce Einstein (17%). Each of these front runners saw double-digit adoption increases vs 2019, proving that as more companies are moving to scale, they’re looking for solutions that can scale with them.

Something of which AI developers may want to take note of is the variability in languages used to build models has also shifted from 2019. While Python remains the most used language in both 2019 and 2020, SQL and R were the second and third most commonly used language in 2019. However, in 2020, Java, C/C++, and JavaScript gained significant traction. Python, R, and SQL are often indicative of the pilot stage, while Java, C/C++, and JavaScript are more production stage languages.

To learn more, we recommend downloading the entire State of AI and Machine Learning Report.

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AI Drug Company Exscientia Raises $60 Million in Series C Funding 



The British AI drug development company Exscientia has raised $60 million in a Series C financing round. It was led by Denmark’s Novo Holdings, which is a new investor, along with existing investors Evotec, Bristol Myers Squibb, and GT Healthcare Capital.

The funding round comes as there is a growing interest in AI-driven drug discovery, and it will help the company expand internationally, with a special focus on a presence in the United States. 

Prof. Andrew Hopkins is CEO and founder of Exscientia. 

“This investment highlights the increasing commitment to the potential of AI to transform drug discovery and the excitement that we have garnered around the innovative work we are doing at Exscientia,” said Hopkins. “We have now demonstrated multiple times that our platform can accelerate the time between the start of an idea and a viable new drug candidate for patients in need of treatments, fast tracking the entire R&D process.”

Robert Ghenchev is Senior Partner and Head of Novo Growth, which is the growth equity arm of Novo Holdings. 

“Through its impressive track record to date, Exscientia has demonstrated the value of combining deep scientific expertise with cutting-edge technology capabilities to significantly accelerate drug discovery,” Ghenchev said. “Novo Holdings’ investment underscores our interest in supporting technology companies that enable life science research and innovation, and our commitment to this area. We see significant opportunity for the company to further grow its participation in the drug discovery ecosystem and are excited to work with the Exscientia team in realising this vision.”

Dr. Werner Lanthaler is the Chief Executive Officer of Evotec. 

“Evotec was the first strategic and operational investor in Exscientia and we are extremely pleased it has made such impressive progress since that time and to be part of this significant fund raising. Evotec and Exscientia, together, look forward to realising further synergistic potential in innovative drug discovery,” Lanthaler said.

The company raised $26 million, 18 months back, from investors including Bristol Myers Squibb and Evotec.

What is Exscientia?

Exscientia is based in Oxford, and the company uses artificial intelligence for small molecule drug discovery. Earlier this year, the company partnered with Sumitomo Dainippon Pharma (DSP) to develop the first-ever, precision-engineered drug designed using AI. The drug serves as a treatment for obsessive-compulsive disorder (OCD), and it entered Phase 1 human clinical trials. The entire project lasted less than 12 months, in a process that traditionally takes 4.5 years with conventional methods. 

Since then, Exscientia has forged multi-project partnerships with Bristol Myers Squibb, Bayer, Rallybio, and GT Apeiron. 


The company has also been involved in developing treatments for COVID-19. Throughout the entire world, pharmaceutical companies and biotech industries have partnered up to focus on developing a vaccine and treatment for the virus.

Back in March, Exscientia announced that the company was collaborating with Diamond Light Source, a science facility based in Oxfordshire, as well as with drug-developer Calibr, in order to come up with antiviral treatments for COVID-19. 


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Sony Establishes A New Sony Artificial Intelligence Global Division




Online portals like Slashgear and others have brought the news that tech giant Sony is launching a new global division which will have its focus on developing artificial intelligence, The goal is to apply MA through gaming, photography and even cooking. It is planned that the new division will have offices throughout the world. As reported, Hiroaki Kitano, President, and CEO of Sony Computer Science Laboratories will be the global head of Sony AI. Peter Stone will be the US chief, a computer science researcher with a storied background in Reinforcement Learning and Robotics.

The news coincides with Sony’s official press release about the research agreement the company signed with Carnegie Mellon University (CMU) on joint work to develop AI and robotics. Their initial efforts will focus on cooking and delivery.

Already trying to strengthen their AI-development efforts, Sony has brought back  Aibo robotic dog with the new version set to take “full advantage of things like cloud processing, facial recognition, and learning AI’s to make the pup more realistic and engaging.” While Aibo development on the surface might seem like “ fairly playful interpretation of artificial intelligence,” Sony’s purpose behind the toy’s development is to explore  “how such technologies can ‘unleash human imagination and creativity with AI,’ tapping into all of the company’s different divisions in the process. It’ll also look beyond Sony’s existing businesses, to figure out new potential applications.”

Using this as a stepping stone, Sony’s teams in Japan, the US, and Europe plan to launch “three flagship projects’” one of which is the aforementioned gastronomy research with CMU. The other two planned fields are gaming and imaging&sensing.

Sony points out that it is focusing on gaming as games are widely used in AI experiments, and “and game worlds are a commonly implemented research method to develop artificial intelligence.’ Of course, there is a more direct application on Sony products as the new AI teams could “help the PlayStation team develop smarter in-game characters.”

For imaging and sensing, Sony aims to produce new types of sensors that can be used as “sense organs” for artificial intelligence. “In that case, we can consider AI systems in a wide variety of scenarios, from real-time data analysis to robots, to agents within a server space. Each of those scenarios has different requirements in terms of processing time or level of input necessary.” In that field, as Endgadget notes, Sony has already made strides in products like the A6600 camera, where it contributes to an impressive improvement in autofocus.

Discussing its plans to develop the AI-use in gastronomy, Sony says that it intends to “harness AI and robotics from the perspective of “getting closer to creators,” and with the aim of expanding the creative options/creativity of chefs. Top-level chefs can gain inspiration for new recipes by interacting with AI, and we also want to help all of those who prepare food in their day to day lives to enjoy that process of creation even more.”

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