Fintech platform BharatPe has launched its ‘ESOP Cheque Cash Karo’ scheme that will allow its employees to sell back their shares from the first vesting back to BharatPe. This comes at a time when people are facing a financial crisis due to the cutbacks in salaries.
The company’s decision to launch the scheme comes at the right time during this pandemic. Over 6% that is more than $25 million of BharatPe’s overall equity has been allotted to the ESOP pool, which is given to the employees along with their appointment letter.
As reported by CNBC (1), BharatPe said in a statement,
“ESOPs carry zero strike price. The vesting is front-ended in favor of employees with 25% vesting on year 1 & thereafter 2% every month. The employees are not required to exercise the ESOPs on leaving & can time it with a liquidity event anytime up to 5 years.”
According to KPMG, ESOPs have been used effectively to lure, retain, motivate, and compensate employees. There are many startups that are buying back shares from their employees and rewarding them in return.
Eighteen months back, RazorPay also bought back their ESOPs of employees.
Supporting employees financially via buybacks
An ESOP is essentially an employee benefit plan under which an employee has the right to acquire shares at a discounted value without any underlying obligation to do so.
Ashneer Grover, Co-founder & CEO at BharatPe, said that in the Indian ecosystem, ESOPs have been regarded as one of the most abused and misunderstood instruments. Verbal grants, back-ended vesting & last-minute changes even to have eroded employee faith in ESOPs.
Geetanshu Singh, an employee and the Head of Engineering at BharatPe, said that the buyback of ESOPs would help in supporting the current and even the ex-employees during this pandemic.
BharatPe is encouraging its employees to bank their first ESOP cheque and enjoy the created cash value in the bank.
One BharatPe ESOP = Rs 700k
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Messaging Software Startup Aampe Raises Rs 13 Crore From Sequoia India Surge
- Aampe is a Singapore-based startup that embeds experiments within routine messaging to turn consumer communication into a business growth engine.
- The startup will use the funding for product development to serve global customers.
Singapore-based personalized messaging software startup Aampe has raised $1.8 million (or about Rs 13.2 crore) in funding led by Sequoia India scale-up program Sequoia India Surge.
The startup with fresh funding plans to accelerate its growth momentum and product development to serve the customers globally.
Last week, US-based venture capital firm Sequoia Capital had announced the names of 17 startups, including Aampe, Kyt, Pagarbook, Plum, among others.
The selected startups received a total of $45 million in their financing rounds from Sequoia Surge and other investors.
From the last few months, Sequoia scale-up program is actively investing in startups from industries, including edtech, fintech, consumer tech, health tech, developer tools, B2B marketplaces, and tech for small and medium-sized enterprises.
Commenting on the development, Paul Meinshausen, Co-founder, Aampe, said, “As a data scientist, I’ve struggled repeatedly across multiple companies with the quality of tools for user messaging. Naive automation has been prioritized over reliable inference and quality data generation. We’re building Aampe to make first-class data science serve one of the most important responsibilities of any business: speaking and listening to customers,”
“While this is true to some extent, the usefulness of data also degrades quickly — and many companies underestimate the importance of continuously generating new and high-quality data. Aampe’s APIs do this and feed that data back into product development at both strategic and tactical levels,” he added.
Founded in July 2020, by Sami Abboud, Schaun Wheeler, and Paul Meinshausen. Aampe uses machine learning to personalize messages and communication for customers, helping businesses drive better customer retention and growth.
Since its inception, Aampe has acquired customers across Asia, including India, Singapore, Myanmar, and Indonesia.
The startup helps product managers to unlock the power of data and continuous experimentation to deliver the business value.
Aiming for seamless integration, Aampe’s APIs, and reinforcement learning pipelines and models can be easily plugged into messaging and communication providers that companies already use. This allows product managers, data scientists, and growth marketers to avoid expensive and time-intensive engineering projects.
Aampe also has its offices in Antwerp (Belgium) and Raleigh (USA).
with inputs from PTI (Press Trust Of India)
Tata inches closer to make foray into Online Grocery Biz
Unless something really goes bad, Tata Group should be making an official foray into the online grocery biz by as early as next week. Sources privy to the matter claim that salt-to-steel conglomerate has almost closed on buying nearly 80% stake in BigBasket for $1.3 Bn. The deal reportedly values India’s largest online grocery store at almost $1.6 Bn.
According to several news reports, the Tata Group will acquire existing investors’ stake, which comes to around 50-60%. Additionally, the Mumbai based corporate giant will buy 20-30% new shares of BigBasket. This will take Tata’s stake in the company to 80%.
How BigBasket Benefits from Tata Acquisition deal
With Tata likely to become a majority stakeholder in BigBasket once the deal is officially signed, the latter will be in a much more commanding position to take on JioMart. What the Tata Group eventually brings for BigBasket is the funding muscle as well as the reputation of a large conglomerate and deep experience in scaling the business.
Probably no one could have matched Reliance’s funding prowess than the Tatas.
BigBasket in Numbers:
- Revenue & Losses in Fy20: Revenue = Rs 5,200 Cr & Losses = Rs 902 Cr
- Revenue & Losses in Fy19: Revenue = Rs 3,200 Cr & Losses = Rs 348 Cr
- Daily Orders: 3,00,000
- GMV: $1 Bn
How Tatas will benefit from BigBasket Acquisition Deal
With BigBasket acquisition deal, Tatas will get a massive market share in India’s online grocery Biz in one shot. The deal is also likely to become a launch pad for Tatas to foray into the e commerce biz in a big way. This foray is likely to happen through a super app, which is likely to be launched once the company seals the BigBasket.
With this super app, Tatas are aiming to dabble into the burgeoning e-commerce sector.
Sources claim that Tatas are sniffing a serious growth in the e-commerce in the post-Covid scenario.
Genesis Therapeutics raises $52M A round for its AI-focused drug discovery mission
Sifting through the trillions of molecules out there that might have powerful medicinal effects is a daunting task, but the solution biotech has found is to work smarter, not harder. Genesis Therapeutics has a new simulation approach and cross-disciplinary team that has clearly made an impression: the company just raised a $52 million A round.
Genesis competed in the Startup Battlefield at Disrupt last year, impressing judges with its potential, and obviously others saw it as well — in particular Rock Springs Capital, which led the round.
Over the last few years many companies have been formed in the drug discovery space, powered by increased computing and simulation power that lets them determine the potential of molecules in treating certain diseases. At least that’s the theory. The reality is a bit messier, and while these companies can narrow the search, they can’t just say “here, a cure for Parkinson’s.”
Founder Evan Feinberg got into the field when an illness he inherited made traditional lab work, as an intern at a big pharma company, difficult for him. The computational side of the field, however, was more accessible and ended up absorbing him entirely.
He had dabbled in the area before and arrived at what he feels is a breakthrough in how molecules are represented digitally. Machine learning has, of course, accelerated work in many fields, biochemistry among them, but he felt that the potential of the technology had not been tapped.
“I think initially the attempts were to kind of cut and paste deep learning techniques, and represent molecules a lot like images, and classify them — like you’d say, this is a cat picture or this is not a cat picture,” he explained in an interview. “We represent the molecules more naturally: as graphs. A set of nodes or vertices, those are atoms, and things that connect them, those are bonds. But we’re representing them not just as bond or no bond, but with multiple contact types between atoms, spatial distances, more complex features.”
The resulting representation is richer and more complex, a more complete picture of a molecule than you’d get from its chemical formula or a stick diagram showing the different structures and bonds. Because in the world of biochemistry, nothing is as simple as a diagram. Every molecule exists as a complicated, shifting 3D shape or conformation where important aspects like the distance between two carbon formations or bonding sites is subject to many factors. Genesis attempts to model as many of those factors as it can.
“Step one is the representation,” he said, “but the logical next step is, how does one leverage that representation to learn a function that takes an input and outputs a number, like binding affinity or solubility, or a vector that predicts multiple properties at once?”
That’s the work they’ve focused on as a company — not just creating a better model molecule, but being able to put a theoretical molecule into simulation and say, it will do this, it won’t do this, it has this quality but not that one.
Some of this work may be done in partnerships, such as the one Genesis has struck up with Genentech, but the teams could very well find drug candidates independent of those, and for that reason the company is also establishing an internal development process.
The $52M infusion ought to do a lot to push that forward, Feinberg wrote in an email:
“These funds allow us to execute on a number of critical objectives, most importantly further pioneering AI technologies for drug development and advancing our therapeutics pipeline. We will be hiring more top notch AI researchers, software engineers, medicinal chemists and biotech talent, as well as building our own research labs.”
Other companies are doing simulations as well and barking up the same tree, but Feinberg says Genesis has at least two legs up on them, despite the competition raising hundreds of millions and existing for years.
“We’re the only company in the space that’s working at the intersection of modern deep neural network approaches and biophysical simulation — conformational change of ligands and proteins,” he said. “And we’re bringing this super technical platform to experts who have taken FDA-approved drugs to market. We’ve seen tremendous value creation just from that — the chemists inform the AI too.”
The recent breakthrough of AlphaFold, which is performing the complex task of simulation protein folding far faster than any previous system, is as exciting to Feinberg as to everyone else in the field.
“As scientists, we are incredibly excited by recent progress in protein structure prediction. It is an important basic science advance that will ultimately have important downstream benefits to the development of novel therapeutics,” he wrote. “Since our Dynamic PotentialNet technology is unique in how it leverages 3D structural information of proteins, computational protein folding — similar to recent progress in cryo-EM — is a nice complementary tailwind for the Genesis AI Platform. We applaud all efforts to make protein structure more accessible such that therapeutics can be more easily developed for patients of all conditions.”
Also participating in the funding round were T. Rowe Price Associates, Andreessen Horowitz (who led the seed round), Menlo Ventures, and Radical Ventures.
Facial recognition tech: risks, regulations and future startup opportunities in the EU
Facial recognition differs from the conventional camara surveillance, as it is not a mere passive recording, but rather it entails identification of an individual by comparing newly capture images with those images saved in a data base.
The status in Europe
Although facial recognition is not yet specifically regulated in Europe, it is covered by the General Data Privacy Regulation – GDPR – as a means of collecting and processing personal biometric data, including facial data and fingerprints. Therefore, facial recognition is only possible under the criteria of the GDPR.
Biometric data provides a high level of accuracy when identifying an individual due to the uniqueness of the identifiers (facial image or fingerprint) and a great potential to improve business security.
The processing of biometric data, which is considered sensitive data, is in principle prohibited with some exceptions, such as, for reasons of substantial public interests, to protect the vital interest of the data holder or another person, or if data holder has given its explicit consent, to name some.
Moreover, other factors such as proportionality or power imbalance are considered to determine if it is a valid exception, for instance, facial recognition can be considered disproportionate to track attendance in a school, since less intrusive options are available. Also even when the data holder has explicitly consented to the processing of biometric data, consideration should be given to potential imbalance of power dynamics between the individual data holder and the institution processing the data. For instance in a student and school scenario, there could be doubts as to whether the consent of the parents of a student to the use of facial recognition techniques, is freely given in the manner intended by the GDPR and therefore, a valid exception to the prohibition of processing.
One of the challenges in this field is that the underlying technology used for facial recognition, for instance AI, can present serious risks of bias and discrimination, affecting and discriminating many people without the social control mechanism that governs human behaviour. Bias and discrimination are inherent risks of any societal or economic activity. Human decision-making is not immune to mistakes and biases. However, the same bias when present in AI could have a much larger effect.
Authentication vs. identification
Obviously biometrics for authentication (which is described as a security mechanism), is not the same as remote biometric identification (which is used for instance in airports or public spaces, to identify multiple persons’ identities at a distance and in continuous manner by checking them against data stored in a database).
The collection and use of biometric information used for facial recognition and identification in public spaces carries specific risks for fundamental rights. In fact, the European Commission (EC) has warned that remote biometric identification is the most intrusive form of facial recognition and it is in principle prohibited in Europe.
So where is all this going?
What should prevail: the protection of fundamental rights, or the advancement that comes with invasive and overpowering new technologies?
New technologies, like AI, bring some benefits, such as technological advancement and more efficiency and economic growth, but at what cost?
Using a risk-based approach the EC has considered the use of AI for remote biometric identification and other intrusive surveillance technologies to be high-risk, since it could compromise fundamental rights such as human dignity, non-discrimination and privacy protection.
The EU Commission is currently investigating whether additional safeguards are needed or whether facial recognition should not be allowed in certain cases, or certain areas, opening the door for a debate regarding the scenarios that could justify the use of facial recognition for remote biometric identification.
Artificial intelligence entails great benefits but also several potential risks, such as opaque decision-making, gender-based or other kinds of discrimination, intrusion in our private lives or being used for criminal purposes.
To address these challenges, the Commission in its white paper on AI, issued in February this year, has proposed a new regulatory framework on high risk AI, and a prior conformity assessment, including testing and certification of AI facial recognition high risk systems to ensure that they abide by EU standards and requirements.
The regulatory framework will include additional mandatory legal requirements related to training data, record-keeping, transparency, accuracy, oversight and application-based use, and specific requirements for some AI applications, specifically those designed for remote biometric facial recognition.
We should then expect new regulation coming, with the aim to have an AI system framework, compliant with current legislation and that does not compromise fundamental rights.
Opportunities for startups?
Facial recognition technologies are here to stay, therefore, so if you are thinking about changing your hair colour, watch out as your phone might not recognize you! With the speed in which facial recognition is growing, we should not wait too long for new forms of ‘selfie payment’.
Facial recognition is already been used quite successfully in several areas, among them:
- Health: Where thanks to face analysis is already possible to track patience use of mediation more accurately;
- Market and retail: Where facial recognition promises the most, as ‘knowing your customer’ is a hot topic, this means placing cameras in retail outlets to analyze the shopper behavior and improve the customer experience, subject of course to the corresponding privacy checks; and,
- Security and law enforcement: That is, to find missing children, identify and track criminals or accelerate investigations.
With lots of choices on the horizon for facial recognition, it remains to be seen whether European startups will lead new innnovations in this area.
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