Opportunity

The diamond industry today faces numerous challenges related to diamond sourcing and quality. Lab-grown diamonds are infiltrating the natural diamond market, both illegally and legally. Because of this, both consumers and suppliers are increasingly wary of claims of authenticity. Compounding the problem, consumers are increasingly aware of and sensitive to social issues affecting supply chains of everything they purchase. Diamonds are no exception. The image of diamond jewelry has been tarnished by awareness of “conflict diamonds”, diamonds from regions of strife such as Sierra Leone and Congo that come with negative social externalities. This changing consumer preference has led to industry decline for nearly a decade

Certifying the authenticity and origin of each diamond is a big challenge. The supply chain is several layers deep, with diamonds changing hands and crossing borders several times before reaching the end user. In 2017, wholesalers and distributors bought over $5.7 billion worth of rough diamonds from De Beers. Next, the diamonds are sold to various polishing and cutting companies, smaller retailers, and to big retailers. With the diamonds changing hands several times from mining, sorting, distributing, cutting, polishing, manufacturing, and finally to retail stores, end-customers do not confidently know where diamonds originally come from. Even when diamond jewelry changes hands, often in the process it is easy for a diamond to be replaced with a lab-grown or lower quality diamond. Our solution is to utilize blockchain to track these diamonds and with the use of smart contracts to make diamonds traceable.

 Solution

By allowing each step in the diamond supply chain to verify and track their product in an immutable ledger, we can add significant barriers to fraudulent diamond sources. At each point that a diamond changes hands would involve an entry into the blockchain, creating a record of which diamonds each entity was responsible for. This entry would include the id of the source, the recipient, and id of the diamond being sold. Each diamond could be given a unique id number by laser engraving. These engraved ids are already in place in many certified diamonds, but finding out what the origin of the diamond is based on this inscription is a not an easy matter. By logging each step of the supply chain in a trustworthy, public ledger, an individual considering a diamond purchase can easily verify that the diamond is represented throughout each step of the supply chain.

Commercial Value and Promise

Our next step in testing this idea out would be to consult with industry experts about its economic promise, build out the blockchain system, and begin building buy-in with consumers to incentivize producers and retailers to join our system. Once we have interested parties in at least one place in the entire supply chain, we would want to create our first entries, from mine to ring. Ideally this test would come with as much buzz and publicity as we can generate to get more consumers interested, giving incentive to suppliers to join, getting our flywheel moving.

For the system infrastructure, we would need to incentivize someone to run the mining computers to generate the nonces. This could be done by charging a small fee for each transaction on the chain, much like is expected to happen once final bitcoin has been mined.

Product Promise

For our initial discovery phase, we have conducted interviews with several experts in the diamond industry to verify our idea and proposed plan. This has led to some good feedback and it seems like it is a possible solution.:

Aman Parikh, owner of diamond wholesale business: “Definitely some scope to use this technology. The Kimberly Process modeled by the UN has tried to execute this to prevent nations with blood diamonds from selling their products. This could be more efficient to implement once the diamonds have been cut and polished as creating an ID on a diamond which is larger when extracted from the mine, will be harder”. Further, insight from Aman, led us to understand that currently on purchasing diamonds from a wholesaler, companies do not know how many times the diamond exchanges hands or is sold in between. This process would also have the potential to eliminate some middlemen from the process which would help the end customer.  

Name Withheld, First year student at Booth who recently purchased diamond ring for his to be fiancé – “Sounds interesting, I would have an easier time purchasing a diamond and not paying an unnecessary premium at a Tiffany’s. I struggled with making a decision about my diamond purchase because I wasn’t sure that the jewelry store on Wabash was giving me a legitimate diamond.”
As for the technology’s promise for tracking a commodity product throughout the supply chain, we can look to a proof of concept from another industry: Tuna. This industry is using blockchain to help reduce the amount of illegal fishing in a similar way to our own proposal. This lends credence to the idea that we can use blockchain to reduce another negative externality of an industry.

Opportunity:

LawGeex is a software-based Israeli start up founded in 2014 that uses artificial intelligence software technology to automate and increase the speed and accuracy of contract review for businesses. LawGeex has built a powerful AI neural network through training their software with thousands of legal documents. As the legal needs for firms increases in size and scope, companies find themselves needing to review complex legal documents frequently for everything from NDAs to purchase agreements, the time and money spent to review these documents is rapidly increasing.

LawGeex does not seek to replace lawyers nor is it a comprehensive legal solution, but it bills itself as automating the approval of mundane, routine legal documents.  The idea is to free up time for employees to work on more creative tasks and to also save on legal fees when lawyers are involved. Additionally, it seeks to find troublesome clauses or verbiage in the contract that humans might miss which could also be costly to the firm. In essence, they seek to answer the question “Can I sign this?”. With a market opportunity that expands to nearly every business worldwide, LawGeex demonstrates a powerful and scalable platform.

Solution :

LawGeex uses Artificial Intelligence to improve and speed up the legal and contract-building/editing process. The technology they use analyzes incoming contracts and suggests edits based on the company’s policies and business requirements. A business can set boundaries on legal clauses it will accept or deny, and LawGeex’s AI software learns to efficiently approve or suggest edits to contracts. If edits need to be made, LawGeex’s platform allows customers to edit contracts quickly and consistently on the platform. Overall, the company looks to speed up and simplify the legal review and approval process (making it under 60 minutes) while remaining safe and secure.

Effectiveness, Commercial Promise, and Competition:

LawGeex offers easy to use, cloud based platform that can start using without installation or internal IT support needed. LawGeex has customers in over 15 countries, including eBay, Deloitte, and major banks and insurance companies. LawGeex technology completes the review and approval process of simple legal documents for businesses in less than 60 minutes. For the average customer this presents 80% time saving. In a supervised study, the AI product and 20 experienced layers reviewed five non-disclosure agreements. The lawyers were did the task accurately in 85% of the time, while the AI was correct 94% of the time and did the task approximately 200 times faster.

Legal services are costly for companies but necessary for running a business. Automating them presents a big commercial promise for the technology. According to studies, ineffective control and management of supplier contracts costs businesses $153 billion a year in terms of missed savings opportunities and increased risk. LawGeex’ service is attractive for customers because it decreases legal costs by speeding up the contract approval process. It can also appeal to legal advisers by automating repetitive tasks and leaving them more time to focus on the more interesting aspects of their work. In addition, LawGeex allows customers to specify company language and required clauses and alerts the users when those are missing. This can benefit small businesses that may lack the time and expertise to monitor the process. New legal hires can also be trained faster using LawGeex interface.

Several competitors include Agrello, Thought River, Legal Robot, and platforms such as OpenLaw which is a US and Swiss collaboration on Ethereum.

Suggestions / Improvements:

First and foremost, LawGeex should continue to refine its algorithms as it gets more and more data in order to improve accuracy. LawGeex currently focuses on “everyday and in the way” contracts such as NDAs, service agreements, and other low risk contracts. As a next step, LawGeex should also look to expand into more complex contracts and legal documents. Further, LawGeex should continue to expand its customer base and potentially develop industry expertise by evaluating contracts versus a company’s standard language/ fallback positions as well as industry benchmarks. That said, this may run into privacy concerns (i.e., aggregating data across many companies). Another area for improvement is to make LawGeex personalized for each user. Even within companies, lawyers operate differently – some lawyers will want to dig into and examine certain areas more than others. As such, LawGeex could learn for each individual user and flag items of particular importance for that user (as opposed to just for the company). Lastly, LawGeex primarily focuses on businesses. LawGeex could try marketing themselves to law firms – this will be an uphill battle as LawGeex will take away billable hours and disrupt the industry, but some law firms may see the value.

Sources:

• LawGeex: https://www.lawgeex.com/features/

• “AI-Based Automated Legal Contract Reviews Startup Raises $12 Million,” https://www.calcalistech.com/ctech/articles/0,7340,L-3736298,00.html

• “This Software Works Just as Accurately as Your Lawyer–Only 200 Times Faster,” https://www.inc.com/kevin-j-ryan/lawgeex-artificial-intelligence-software-beats-lawyers.html
• “Managing Contract Risk,” http://www.prosidian.com/assets/pdfs/Managing%20Contract%20Risks %20-%20Importance%20Of%20Contracts%20As%20A%20Risk%20 Management%20Tool.pdf   
• Agrello: https://www.agrello.org/how-it-works#crowd
• Openlaw platform: https://www.artificiallawyer.com/2017/07/31/openlaw-launches-new-smart-contract-platform-on-ethereum/
• ThoughtRiver, https://thoughtriver.com/
• Legal Robot, https://www.legalrobot.com/

 

Opportunity

The surveillance and security services industry in North America recorded revenues around $34 bn in 2017 and is constantly growing. A significant part of this comes from security services in private enterprises, especially for small and medium sized enterprises, where shoplifting is a large concern. For an average store for each item stolen they have to sell 50 more of the same product in order to make up for the loss.  Machine learning techniques and feedback loops can help human-machine ensembles solve this problem. Smart sensors can alert human surveillance centers, allowing organisations to devote less resources towards security. Moreover, analyzing behaviour using these mechanisms will allow security companies and businesses to reduce costs incurred through thefts.

 

Proposal

We will provide a minimal number of video cameras to each store. The cameras will be embedded with video recognition software that can process and analyze movements, postures and behavior patterns. Smart sensors will complement our cameras by being able to record ambient information such as the number of people in store and temperature.

 

This will in part be in areas where behavior is necessitated to be monitored. The cameras will be able to provide real-time alerts when suspicious behavior is observed, which can be integrated with a smartphone to be able to swiftly respond. The system will also be able to monitor safety related behaviors and send alerts to law enforcers. For example the system will immediately alert the police and send them footage if it detects an individual who is unlawfully carrying a weapon.

 

Another potential future use case that be leveraged in addition to the security feature would be to roll an autonomous retail store experience for customers visiting the store. To facilitate this service, retailers could create smart contracts that would authorize automated payments, discounts and enforce rules against returns or disputes depending on the customer’s attributes (e.g loyal customers gets rewards and more wiggle room etc..). This would eliminate the need for a human at the retail store and eliminate wait times at check-outs and dramatically mitigate a major felon amongst shoplifters — “sweethearting” — the collusion of a shoplifter and the checkout clerk.

 

Empirical Demonstration of Commercial value and promise

The cost savings that will occur from this product will be immense for businesses that regularly experience shoplifting or errors to their products. This would eliminate the need for a human or a conventional sensor whom would not have the same accuracy as our product in capturing the people with unpaid items leaving their store. In addition, this would make it harder for the employee to manipulate for his own self.

 

Furthermore, the product does not violate the privacy of the customers visiting the store as it is merely a surveillance camera that records the movement and behavior of the individuals in the store. This could expand to monitor other unwanted behaviors such as terrorism, reckless driving and any other behavior that is valuable to be reported to the business or organization to enable them to take action. The real value comes from the ability to report in real-time rather than the retrospective method used from a conventional camera’s past records especially for behaviors that need a swift response.

Perhaps the one added-value feature that would be revolutionary for law enforcers would be the ability to have superior facial recognition through these cameras. This would help law enforcers identify people in complicated situations where there is a large crowd and save them the time to repetitively watch the video for clues, saving them enormous resources that could be utilized to other areas.  

 

Challenges

We think that it might be difficult to convince small businesses to equip themselves with our ZeroChain cameras and sensors when they do not realize the cost savings incurred by our product. Moreover, they would need to be convinced that our product actually works better than conventional tools already available in the market.

 

Our mitigation for this case would be that we could charge these businesses our equipment at cost and only charge them a marginal subscription fee. We would then charge them each time our camera successfully detects an individual that could have potentially incurred losses to the retailer. This would make our customers feel comfortable knowing that our interests are aligned with theirs. Additionally, big retailers such as Target have accelerators focused on retail technologies, and others such as Walmart have shown interested in investing in an autonomous and seamless customer experience through acquisitions by Walmart Labs.

 

Sources:

http://www.wpri.com/target-12/local-stores-use-artificial-intelligence-to-catch-shoplifters/1136106605

 

https://www.fastcompany.com/3064771/can-this-ai-powered-security-camera-learn-to-spot-fishy-behavior-as-it-happ

 

https://www.technologyreview.com/s/608765/i-tried-shoplifting-in-a-store-without-cashiers-and-heres-what-happened/

 

https://venturebeat.com/2017/11/22/ai-could-make-video-surveillance-a-proactive-crime-fighting-tool/

 

Team Members:

Mohammed Alrabiah

Tuneer De

Mikhail Uvarov

Colin Ambler

Lindsay Hanson

Background – What the art industry looks like, and what is the process high level

The arts and collectibles market is estimated at 50B to 70B USD globally, and is growing steadily given that a bigger and bigger population is showing interest in the market. Current transactions in the market take place through different avenues:

1. Auctions and gallery sales: Items can be offered for sale by current owners, artists or art traders. For valuable pieces, sales are often preceded by an authentication process that is both costly and prone to human error.
2. Black market trades: Sales on the black market happen through unofficial channels, usually and involve stolen pieces or counterfeits. Such pieces are rarely authenticated as buyers don’t want to admit recourse to black market channels.

As such, the overall market is inefficient and unstructured: no single ownership and authenticity database exists.

Opportunity – Where are there pain points in the current process?

The art and collectibles market has always been the subject of frauds and forgeries, it is the second-largest unregulated market after illicit drugs. According to the FBI’s art crime unit, art frauds and forgeries represent $6 billion in annual losses – 2016 was labeled the “year of the fake” due to the great amount of art forgery scandals.

There are 3 main pain points in the art and collectibles market:

1. Authentication: The lack of a central title agency makes it difficult to determine past ownership which leads to increased difficulty in establishing authenticity and price. Since we cannot trace it, we cannot know if it is authentic and establish a fair price.
2. Proof of ownership: Ownership is a key element in establishing a work’s authenticity. Transaction records today are still paper-based most of the time, which makes it easy to get lost, destroyed, altered or stolen.
3. Provenance: Provenance is the history of ownership of a work. It includes information about who owned it in the past, where has it been exhibited, the process the art went through from the former owner (seller) to the buyer, etc. The lack of proper provenance opens the door to forgeries, stolen art, or illegal acquisition.

Proposed Solution – How can a blockchain address those pain-points

A blockchain network can be used by art dealers and owners to preserve the evidence of ownership of a piece of art. Specifically, the blockchain would record the following:

1. The ownership and transmission history of the particular art piece
2. Hashes of related documentation, for example photographs, past appraisals, receipts, restoration records, etc

The blockchain provides a way for everyone to verify and validate an ownership through tracking the ownership and transmissions in an open and immutable history. For illustration purposes, below is a sample user case:

A person owns an art piece by Picasso. She gets it authenticated by an art professional and appraised by an art appraiser. Using a proprietary photography app which utilizes convolutional neural network technology, she takes a few photos of the art piece and uploads the documents and photos onto the blockchain. Now, she wants to sell the art piece to a new buyer. The new buyer will be able to view through the blockchain not only the record of ownership, but also the authentication record and the photos, which makes it unnecessary for the new buyer to spend the resources to authenticate the piece again by an independent art professional. This also gives the buyer the piece of mind that she is buying an item of good provenance which supports the value of the piece.

Commercial Promise

In 2016, the fine art market recorded $45bn worth of sales. Each of these art pieces sold required appraisals and validations at least once, requiring extensive time, effort, and money to sell the pieces. In addition to the cost of appraisal, these sales are also exposed to under/over valuations by appraisers and even forgeries which add to the overall cost of owning and selling art. Since art sales continue to grow, particularly as an offering within wealth management, we see a great value of using blockchain in this industry now and in the future.

In 2017, according to a study by Deloitte 88% of wealth managers said that art and collectibles should be included as a part of wealth management offering. In 2016, $1.6 trillion USD of ultra-high net worth wealth was allocated to arts and collectibles, and this is expected to grow to 2.7 trillion in 2026. Although wealth managers see the fine art industry as a growing field, there are still some major hurdles that must be overcome such as lack of regulation, expertise, authentication, forgeries, etc.

In addition to high cost of appraisal, under/over valuation of the pieces due to these appraisals could cost art collectors a great deal in the future. From 2011-2015, the Art Advisory Panel of the Commissioner of Internal Revenue reported that 58% of the works they reviewed were valued improperly. In general, overvaluation occurs when works of are sold or given as a charitable donation and undervaluation occurs in estate planning when tax implication can be high.

Given the potential size of the industry (1.7 trillion with 40-50bn sales), saving even a small percentage on each sale due to improved standards/guidelines from blockchain would save millions each year and alleviate many reputational concerns currently associated with the industry.

Benefits/Challenges

The main benefits of using this new process to validate art using machine learning and blockchain are that there would be standardized way of validating art, also that costs will start to decrease after the process becomes massively used and finally that through blockchain the property registration will remain over time and will help to track owners and also serve as a source of validity for each piece of art

In terms of challenges, the first one will be to convince specialists and collectors that this process is as effective as an expert eye to validate art. Second I would think there would be some resistance from brokers that now win commissions based on transactions related to selling or buying art. Also, another challenge will be that the first registration of property in blockchain would have to be done by a person. That person should have to be controlled in order to ensure he is entering the right information in the system and not colluding with other people to generate false property data. Finally an important challenge will be funding because this company will require investment to develop and buy the machines that will perform the machine learning analysis and also investment to develop the blockchain system to keep the information of property.

Potential Competition

Firstly, the commoditization of machine learning tools via the APIs provided by large Tech firms such as Google, Amazon, and Microsoft pose a computational challenge to the sustainability of this venture. Counterbalancing this is the dataset we would seek to acquire in image capture, which would represent some form of barrier to entry in terms of time, effort, and categorical knowledge on the Arts, i.e. knowing which galleries to visit and what art collections to prioritize. Secondly, there are direct competitors in this space such as The Codex Protocol, who are a series-A funded venture focused on creating a blockchain network supporting auctions and the exchange of ownership certificates. There are also smaller ventures which focus on using recently developed machine learning techniques to verify art or identify forgeries, though accuracy is currently limited to ~80% in better cases. Additionally, existing brokerages such as Sotheby’s or Christie’s could acquire or develop internally a similar system of augmented authentication and transaction processing via blockchain, though such projects may reside outside their core competencies.

Sources

https://news.artnet.com/art-world/artificial-intelligence-art-authentication-1156037

http://ackermansfineart.com/art-market-regulation-badly-needed/

http://authenticationinart.org/pdf/literature/Day-Gregory-Explaining-the-Art-MArkets-Thefts-Frauds-and-Forgeries-And-Why-the-ARt-MArket-Does-Not-Seem-to-Care.pdf

https://www.nytimes.com/2017/04/23/arts/design/dont-blame-the-russians-tax-judge-tells-sothebys-expert.html

http://1uyxqn3lzdsa2ytyzj1asxmmmpt.wpengine.netdna-cdn.com/wp-content/uploads/2017/03/TEFAF-Art-Market-Report-20173.pdf

 

Automated Agreements: CarWolf

Opportunity

Numerous parties could benefit from a system whereby complete car operating and service history for the entire auto market is maintained in a secure, verifiable, permission-ledger system. Insurance providers would be able to characterize risk more accurately for an individual driver by not only collecting data on how the driver uses the car (e.g. Progressive Snapshot), but also by having a complete picture on the “health” of the car, regardless of whether the car was new or used. While CarFax attempts to compile some of this information, it is extremely limited in scope. In addition to insurance providers, car manufacturers, with an accurate picture of a car’s usage, would be able to schedule tailored service, as opposed to following a set maintenance interval, regardless of whether the car was new, used, or “certified-new.” Over time, the manufacturers would be able to develop warranties that are more in line with actual needs.

While the above discussion highlights the direct benefits of such a system, there is another obvious indirect benefit from such a system: the complete historical record of all car operating and service information would solve the problem associated with asymmetric information in the “market for lemons.” Neither buyer nor seller would have a one-sided information advantage, leading to the creation of value by more closely matching demand with supply, and price with value.

Solution

Our solution is to employ blockchain technology in a permissioned-ledger system to create an immutable record of every car’s history by attaching operating and service data to the vehicle identification number (VIN), thereby providing car manufacturers with actionable car operating data and solving asymmetric information problems on behalf of both insurance companies and consumers. The CarWolf system consists of two key components: the actual distributed-ledger system and a “black box” collecting car operating information.

While the CarWolf distributed-ledger system would compile all car information via blockchain technology and be permissioned to protect all parties involved, the “black box” would plug into the service port in a car and access car operating information, similar to Progressive’s Snapshot. However, unlike Snapshot, Wolfshot ™ would collect the information and periodically upload it to the blockchain via a mobile app, providing an immutable record of the car operating data. Unlike Progressive Snapshot, the CarWolf system would also require maintenance providers to upload service performed, whether routine or unscheduled, to the blockchain in order to attach a more complete picture of the car’s health. Maintenance providers would be incentivized to upload information either based on being the service department in a dealership (therefore doing so on behalf of the car manufacturer) or by being in the “provider network” for a particular insurance company. The insurance company would benefit from having a more complete picture of a car’s history from which to assign risk and value the car, and the maintenance provider would benefit from being the recipient of more business. Finally, title information would get uploaded to give a complete picture of car ownership.

Design of demonstration

To demonstrate the value of the business, we would want to highlight three key points: first, that system can collect complete car operating data; second, that the available information would vastly exceed currently available sources to solve information challenges for manufacturers, insurance providers, and car buyers; lastly, that the system would be able to handle the quantity of information provided in an immutable ledger. Therefore, the demonstration would use a car with Wolfshot installed and compile information over a 6-month period. Concurrently, the car owner would provide service records over the same period of time to provide a point of comparison of information available under current mechanisms in the most optimal use case. The car and current documentation would then be made available to a set of manufacturers, insurance providers, used car dealers, and prospective buyers, whereby all parties would be asked to estimate the particular metric for their given interests (e.g. the insurance provider estimates monthly premiums while the used car dealer estimates the resale price). Finally, the complete CarWolf data would be made available to another set of the above parties performing the same tasks. The difference between the estimates would quantify the benefit of CarWolf for the various applications.

Pilot Program

Given that the success of this program is dependant on having a large amount of cars in the network and participation from both insurance companies and auto shops, the pilot program would likely begin by partnering with an insurance provider to kick start the program. The insurance companies would offer customers a discount on their policy for using Wolfshot in their car. Wolfshot would not only capture information about how the car was driven, but also record information reflective of the overall health of the car, such as how frequently the oil is changed, wear on the brakes, accident history and all work done on the car. This information would then be stored on the CarWolf blockchain. The program would then track how much the car was resold for over time and the adjustments in users insurance policies based off of the information from Wolfshot. By the end of the pilot we would ideally have collected enough data to show that there were significant savings from the more accurate resale values and adjusted insurance rates to expand to other insurance companies.

Commercial Viability

Given the size of the used car market (~40 million cars sold annually) and the extent to which comparable businesses presently thrive, it is clear that there is a demand for vehicle information to inform better buying and insuring decisions.  Since several businesses have demonstrated success by attempting to address this concern, it appears likely that an incorruptible, immutable, and more complete vehicle history has value in the market. The biggest barrier to commercial viability is driving adoption of the Wolfshot device.  Without new data, such as that automatically generated by the Wolfshot device, the business is little more than Carfax made faster, more secure, and more efficient through the use of blockchain. Vehicle manufacturers who fare well in reliability rankings would stand to enhance their favorable positions via the longevity, value-retention, and reliability data CarWolf would provide, while vehicle manufacturers who tend to fare poorly in reliability rankings would be incentivized to increase their performance in these metrics as they face higher degrees of transparency and accountability in the market.  Privacy-conscious car owners may hesitate to feed car and driving information to a third party without a proper incentive and measures to mitigate their privacy concerns. Therefore, the success of the venture depends, at least in part, on broad adoption of the Wolfshot device. The design of the Wolfshot device, and that design’s ability to mitigate privacy and trust concerns from the consumer, are critical to this broad adoption. One component of the value proposition to the car-owner is the prospect of selling their vehicle for a higher price in the used car market. Further incentives would involve reduced rates with insurance providers and at local partner garages in the CarWolf network.

Sources:

Blackbox https://www.moneysupermarket.com/car-insurance/how-does-black-box-insurance-work/

Used Vehicle Sales Look Set to Hit All-time High in 2016

https://www.cnbc.com/2016/12/05/used-vehicle-sales-look-set-to-hit-all-time-high.html

Progressive Snapshot

https://www.progressive.com/auto/discounts/snapshot/snapshot-common-questions/

CARFAX

http://carfax.com

Edmunds 2017 Used Car Report

https://static.ed.edmunds-media.com/unversioned/img/car-news/data-center/2017/2017-used-car-report.pdf

Automotive OEM Warranty Report

http://www.warrantyweek.com/archive/ww20180405.html

Team Members:

Thomas DeSouza, Matthew Nadherny, Patrick Rice, Samuel Spletzer

Opportunity

Blockchain technology for the use of rewards distribution presents a large opportunity, especially within the content and social media space. There are numerous forums that exist today where users can share news and other content. From Facebook newsfeed to Twitter to Reddit, users are inundated with posts the moment they log in. However, there hasn’t yet surfaced a real solution for monetizing the sharing of content or the voting of quality by readers. Monetization would accomplish two different goals: a) incentivize more users to post and evaluate posts and b) incentivize higher quality in posts overall.

User-generated content creates billions of dollars of value for the shareholders of social media companies (i.e. the intermediaries). In 2016, global revenue from social media was $32.8 billion. In 2019, global revenues are expected to reach $47.6 billion.

Recently however, existing platforms like YouTube have modified their monetization rules, reducing the level of transparency and causing consternation among nearly all the channels. The new monetization rules have resulted in a loss of $100 a year in advertising revenue per content creator. Losing even a tiny profit can dissuade a small creator who may be just getting started, and the majority of YouTube comprises small creators. Not focusing on the small creators could create a significant change in the platform as a whole, since a majority of people on the platform are up and coming content creators and a shift could trigger a waterfall effect.

“It discourages everyone else from building a channel from the ground up, subscriber by subscriber, week by week, the way so many original YouTubers did,” says Matt Wallace, a writer and YouTuber with a modest audience.

 

Solution

Steem was developed as a blockchain-based rewards platform for publishers to monetize content and grow community. It’s the first application that attempts to accurately and transparently reward individuals who make subjective contributions to its community, for example by submitting blog posts or voting on articles, images and commentary, and consequently increasing their popularity. Steemit is the proof of concept created to enable users to participate in these use cases.

 

Steem is based on two founding principles.

 

• The most important principle is that everyone who contributes to a venture should receive pro-rata ownership, payment or debt from the venture. This principle is the same principle that is applied to all startups as they allocate shares at founding and during subsequent funding rounds.
• The second principle is that all forms of capital are equally valuable. This means that those who contribute their scarce time and attention (i.e., “sweat equity”) toward producing and curating content for others are just as valuable as those who contribute their scarce cash.

Users are paid half in “Steem Power,” a token that supercharges voting power, and half with Steem Dollars, a token worth about three USD.

 

Steem scores on a number of achievements. First, it enables the creation of a community where users can share and read about what is important to them. Second, it allows for a ranking of content, where the most interesting and pressing issues appear first. This is also a byproduct of awarding users with quality posts and higher usage more Steem Power. Third, it generates revenue for the company, income for users, and more value to Steem.

 

In addition to powering the Steemit community, Steem is a platform that can be used by any company to power the development of its online communities. Additional online communities powered by the Steem blockchain are likewise tied to Steem’s tokens, and therefore those communities’ successes confer benefits to all other Steem communities because they can enhance the perceived market value of those tokens. As a result, because Steem is essentially a platform for building platforms and because those platforms all experience positive externalities as they become individually successful, Steem is inherently predisposed towards virtuous cycles of user adoption and increasing value as it gains traction.

 

Effectiveness & Competitive Landscape

Through the use of Smart Media Tokens (SMTs) and a decentralized approach, Steem has successfully distributed $40.15M in rewards to over 920.8K users of its social media application, Steemit. Besides Steem, there are a number of blockchain-based platforms that allow developers to build and deploy decentralized applications, such as Bitcoin, Ethereum and EOS. While some of these may be more established than Steem, none are as closely tied to the performance of content creation. In addition, whereas Bitcoin relies on mining to generate new currency units, the Steem network creates new tokens everyday and automatically distributes them to creators based on community voting.

 

Within the social media space, Steemit’s openness and merit-based system continues to attract users and differs from centralized, content-driven social networks, such as Facebook and YouTube, whereby censorship and lack of transparency over monetization, has been a major concern for creators and publishers. Although Reddit and Medium implement similar upvote/downvote systems and have a large built-in user base (making it easy for bloggers to be discovered), the same problem of being bound by the platform’s rules and generating value for the site versus the contributor, still exists. As such, there has been an influx of interest in cryptocurrency-backed social networks, with companies such as Synereo, Akasha and YoYow, operating within the same space as Steemit. However, Steemit has a significant incumbent advantage. Given the importance of network effects and the perceived value of a Steem token, which would have more credibility than one established by a newcomer, creators are more incentivized towards publishing quality content on Steemit over others.

 

Improvements & Suggestions

In its current form, Steem rewards those who either invest cash or make valuable contributions to the community. Doing either of these will give a user more Steem Power (SP), which in turn gives them more influence over what content is elevated to the top. In other words, it is possible to purchase power. This problem could be mitigated by Steem-powered platforms capping the influence a user can have on the visibility and popularity of user-generated content (UGC). For example, users could choose to view and sort UGC in two different ways – number of views and amount of Steem Power. This would help prevent “viral” individuals from gaining outsized amount of influence on the platform.

 

Another potential improvement that would help Steem gain traction is implementing and enforcing rules around etiquette. For example, there are currently no rules against upvoting your own content, plagiarizing content, or spamming posts or content in a single day. Once Steem gains critical mass, there will be an inevitable influx of users who abuse the system. Thus, Steem-powered platforms will need to create well-defined rules, such as encouraging surrounding acceptable behavior and flagging content for copyright violations. This can be enforced by moderators, which are common in many other UGC-based platforms.

 

Because Steem is a tool designed to accelerate adoption for platforms as well as to increase the quality of users’ contributions to them, we can see that its blockchain technology could lend itself to many of the traditional spaces that depend on network effects and user communities. Along these lines, one future application we envision for Steem is online gaming. Success in online gaming is heavily dependent on creating a sizable, engaged community of users who participate in any number of ways including creating in-game content (“mods”), broadcasting online matches on platforms like Twitch and YouTube (“streaming”), creating user communities that play together on a regular basis, and creating fan art to display passion for the game. Individual games using freemium models that are the most successful in creating these communities are able to generate revenues in excess of $100 million per month, but most struggle to gain initial user traction because of the difficulty in demonstrating that their communities are welcoming to all users and sustaining users’ interest over a long period of time. In addition, even wildly successful gaming platforms like Steam (not affiliated with Steem) have struggled to create monetary systems to reward users for their contributions. By enabling gaming platforms to reward their users by linking popularity, performance, and community interactions to influence or in-game currency, Steem can accelerate online gaming communities’ growth and incentivize more sustained, positive interaction within them. This would increase the amount of time users spend engaged with certain platforms, and the cross-platform reward system can further foster user loyalty. This could also give game developing companies another metric they can use to evaluate and refine future iterations of their games.

 

Thus, our recommendation is that in the same way that Steem has created the Steemit community as a proof of concept for developers to use in creating social media platforms, we recommend that Steem work to create a version in other complementary industries, such as online gaming.

Sources

https://steemit.com/faq.html

https://steem.io

https://www.finder.com/steem

https://www.wired.com/story/the-social-network-doling-out-millions-in-ephemeral-money/

https://www.statista.com/statistics/562397/worldwide-revenue-from-social-media/

https://steemit.com/steem/@steemrollin/steem-where-does-the-money-come-from

https://www.wired.com/story/youtube-monetization-creators-ads/

https://www.wired.com/story/decentralized-social-networks-sound-great-too-bad-theyll-never-work/

https://steemit.com/steemit/@johnnyfdk/why-i-started-posting-on-steemit-instead-of-medium-reddit-or-johnnyfd-com

https://steemit.com/steemit/@thecryptofiend/the-complete-steemit-etiquette-guide-revision-2-0

https://steemit.com/steemit/@catharcissism/4-sites-likes-steemit-to-keep-your-eye-on

https://steemit.com/blockchain/@claudiop63/any-steemit-competitor-out-there-a-layman-s-perspective

http://www.kotaku.co.uk/2015/07/09/how-valve-messed-up-paid-mods-on-steam

https://www.forbes.com/sites/davidthier/2018/03/22/report-heres-how-much-money-fortnite-battle-royale-is-making/#37036c2870ff

https://abovethelaw.com/2018/02/how-blockchain-just-may-transform-online-copyright-protection/

https://coincentral.com/what-is-steem/

https://steemit.com/steemit/@thecryptofiend/how-to-improve-steemit-my-thoughts

 

Team

Siddhant Dube

Eileen Feng

Nathan Stornetta

Tiffany Ho

Christina Xiong

 

 

 

Opportunity: the incredible decrease in gene sequencing cost and the promise of blockchain could create a new era of precision medicine.

The dramatic decrease in the cost of sequencing a human genome may well end up the most compelling and consequential technology advance of the past decade. Ten years ago, it cost $10M to sequence a genome. Illumina, the leader in sequencing, promises a $100 genome in the next few years and several promising startups are pursuing credibly higher accuracy and long reads at $100 or less.

Cataloguing and understanding the genome promises a wide range of significant, valuable advancements in human health. With better genetic information, personalized, precise therapies can be developed to treat diseases. It can be used to help people make optimal choices about medical treatments and daily health decisions, such as diet and exercise. Massive genomic datasets can be used to help pharma companies develop better, more effective drugs.

While many of these opportunities are not yet realized, pharma companies are currently paying large sums for genomic datasets to help with drug development. In 2012, Amgen acquired the company deCODE and its 2,600 sequenced genomes for $415M. Genentech paid 23&Me $60M for access to its database. AstraZeneva and Regeneron have both launched multi-million dollar programs to sequence 2M and 500K genomes, respectively.

Solution: use blockchain’s augmented agreements to create a marketplace for genomic data

Nebula Genomics aims to seize this opportunity by creating a marketplace for genomic and phenotypic data. While the cost will fall soon, sequencing currently costs about $1,000 per genome; luckily pharma companies are willing to pay significantly more per genome than that.

A thornier problem is privacy. A person’s genomic sequence is critical, private information. Bank accounts and social security numbers can be changed; genetic makeup cannot. Luckily, the emergence of blockchain protocols allow for a secure, distributed transaction that compensates the person for the data they contribute and gives participants a stake in the network value created by Nebula’s platform.

Nebula’s marketplace:

Pharma companies pay fiat money for tokens, which they pay to Nebula Participants. Nebula Participants pay in tokens to get their genomes sequenced by Nebula, which plans to sequence through partners.

Importantly, Participants store that data themselves, using encryption tools provided by Nebula. This ensures control over data and security as the data isn’t pooled in a central location.

Participants can use Nebula’s genomic self-analysis tool to gain insights about their genome. As more large-scale genomic studies are done, this analysis tool will improve over time, making the platform more valuable to users.

Pharma companies then buy data from users using the Nebula platform. This can be one-time purchase of genomic and phenotypic data or requests for more longitudinal data, such as health records of fitbit data. Participants are paid in tokens, ensuring anonymized transactions. Finally, Participants can sell tokens for fiat money on the market as Pharma companies will regularly pay fiat money for tokens.

Personal Genomics companies are middlemen:

Nebula is a data marketplace, facilitated by sequencing and a blockchain protocol for distributed, anonymized compensation:

Feasibility, risks:

Feasibility relies in large part on acceptance, adoption, and regulation of cryptocurrency. Risks are tied to the booming, but imperfect blockchain and cryptocurrency market. Considering the large amount of money recently invested in cryptocurrency and blockchain companies, it appears that market is here to stay — but, in the event of a serious falling-out of the market or adverse regulatory intervention, Nebula will face challenges. To Nebula’s credit, they aren’t a speculative currency and their business model is greatly enhanced by blockchain protocols.

 Competitors

23&Me and Ancestry are two leading personal genomics companies. They only sequence a person’s exome, or the 1.5% of the genome that we know codes proteins. At this stage, we think the rest of the genome plays little role in our biology, but most scientists admit that the rest of the genome is likely more important than we know. Many now believe — and Nebula’s asserts — that sequencing the entire genome is important. Critically, Nebula’s collection of phenotypic data and structure that compensates participants clearly differentiate it from all personal genomics companies.

LunaDNA is another competitor that adopts a similar model to Nebula, but serves as a middleman and lacks Nebula’s quality team.

 

Sources

Nebula White Paper: https://www.nebulagenomics.io/assets/documents/NEBULA_whitepaper_v4.52.pdf

MIT Tech Review: https://www.technologyreview.com/s/610221/this-new-company-wants-to-sequence-your-genome-and-let-you-share-it-on-a-blockchain/

Fortune: http://fortune.com/2018/02/09/blockchain-genetic-testing-nebula/

Wired: https://www.wired.com/story/solve-genomics-with-blockchain/

CNBC: https://www.cnbc.com/2018/02/08/harvard-genetics-pioneer-will-monetize-dna-with-digital-currency.html

Exome vs Genome: https://www.jax.org/news-and-insights/jax-blog/2016/september/genomes-versus-exomes-versus-genotypes

 

 

 

 

 

Problem: Broken Supply Chains

A small product such as an iPhone can have thousands of different components creating a complex supply chain. Making the product is also a complex process involving setting up contracts and coordinating with numerous suppliers and distributors. During the production process, a product may travel through many different nodes. As an example, the base components of an iPhone could be made at one factory and the finishing components at another factory. When there are defects in the product, it may be difficult to pinpoint when the defect occurred or who is responsible for it because there is no secure ledger that reliably tracks every action performed during the production/supply-chain process.

In short, the often-limited transparency of where a product or component is in the supply chain can make it difficult to address issues related to loss, misdelivery, or decision-making.

Solution: Blockchain in the Supply Chain

To solve these issues, our start-up will integrate blockchain technology into the tracking process of the product’s construction. As a product moves from one location to another, an entry will be made into the ledger system, which tracks when the product left the facility, what state it was in, and who handled the delivery. Once the product is received by the next party, that party will modify the block with information regarding the receipt time, state of the product, as well as any modifications that it makes. During this process a contract can be updated for the parent company to monitor that their suppliers are maintaining the agreed-upon standard of materials being used in the product.

For example, when Apple receives glass covers for their phones from a supplier, the supplier will fill in details regarding the state of the glass and the details of the transaction. Apple can then verify that the agreed-upon units are received as well as the condition they are received in. When auditing the supply chain in case of any disputes, it will be easy to see where mistakes happened and who is culpable.

Furthermore, blockchain technology would apply to both the financial transaction as well as physical transactions of products. In order to facilitate the tracking of the physical transactions, RFID or a similar technology would need to be applied to every shipment.

Ultimately, this innovation could also aid in 1) better tracking against counterfeit products, 2) ensuring compliance with regulations for all components, 3) minimizing transition costs at each stage of production and manufacture, and 4) identifying and mitigating myriad inefficiencies along the supply chain.

Pilot: Apple

In order to demonstrate the application and value of blockchain technology in complex supply chains, we propose using Apple’s iPhone as a pilot.  Every component of Apple’s iPhone would be tagged, either at the individual product level or at the bulk shipment level. As components are assembled and change hands from manufacturer to manufacturer, the transactions would be tracked using blockchain technology on a permissioned ledger. As each transaction happens, both Apple and the manufacturer can enable the supporting financial transactions and smart contracting to occur as well.

Photo credit: Accenture Strategy

Value for both Suppliers and Manufacturers

End-to-end visibility and immutable smart contracts can enable value for both Apple as well as its suppliers, especially in the case of disputes. For a company like GTAT, which supplied sapphire for the screen of iPhones, a contract that is secured by blockchain might have helped save them from bankruptcy. In the original contract that GTAT had with Apple, the company was not allowed to make any modifications to the iPhones. There were also components of the contract that they did not have firm agreements on. Apple was not obligated to buy any of the sapphire GTAT made and as such, when Apple pivoted to a different supplier, GTAT had no way to make Apple buy their product. GTAT was obligated to accept and fulfill any orders by Apple for sapphire glass, but didn’t have any clause stating that Apple always needed to buy the product. GTAT ended up having to pay $439M to Apple for the right to stop supplying sapphire when Apple terminated the partnership, leaving GTAT an obligation to payback the $439M advance used to fund GTAT’s new production facility. [6]

Future

After a pilot with Apple’s iPhone, we plan on expanding to other Apple products.  Once the application has proven itself with the full suite of Apple products, we would then either expand to other companies with similar businesses, such as Samsung or GE or sell this product to Apple. Ideally, we would expand to other businesses and eventually other industries with complex supply chains, including food and beverage, retail, and oil and gas.

Risks and Competition

Primary risks of this innovation are 1) that the technology is still in its infancy, 2) the complexity of the technology and its applications, 3) the decentralized nature of supply chain and logistics for electronics components and the differences in regulations and compliance in different markets where components are produced, and 4) stakeholder adoption across the supply chain will likely prove challenging.

In terms of competition, there are other start-ups emerging in this space, such as companies like the MIT-founded Eximchain [7], and Cloud Logistics. Foxconn, the manufacturer of the iPhone, has developed an early-stage blockchain prototype [8], and Samsung Electronics has a blockchain platform underway. Additionally, Amazon has undertaken projects internally that could pose competition to our product.

Sources:

[1] Deloitte: Using blockchain to drive supply chain transparency https://www2.deloitte.com/us/en/pages/operations/articles/blockchain-supply-chain-innovation.html

[2] NY Times. Blockchain: A Better Way to Track Pork Chops, Bonds, Bad Peanut Butter? https://www.nytimes.com/2017/03/04/business/dealbook/blockchain-ibm-bitcoin.html

[3] How Blockchain Will Transform The Supply Chain And Logistics Industry

https://www.forbes.com/sites/bernardmarr/2018/03/23/how-blockchain-will-transform-the-supply-chain-and-logistics-industry/2/#7c5625f6416c

[4] Blockchain for the Electronics Manufacturing Services Supply Chain

https://www.ibm.com/blogs/insights-on-business/electronics/blockchain-ems-electronics-manufacturing-services-supply-chain/

[5] Why Blockchain is a Game Changer for Supply Chain Management Transparency

http://www.supplychain247.com/article/why_blockchain_is_a_game_changer_for_the_supply_chain

[6] http://fortune.com/2014/10/29/apple-and-gtat-what-went-wrong/

[7] https://www.coindesk.com/mit-founded-startup-raises-20-million-for-supply-chain-blockchain/

[8] https://medium.com/@hermione1/foxconn-to-blaze-the-trail-blockchain-for-electronics-6742abc8aea1

[9] https://btcmanager.com/samsung-electronics-to-employ-blockchain-technology-to-augment-supply-chain-network/

Team members:

Pavlina Plasilova, Kelly de Klerk, Yuxiao Zhang, Aziz Munir, Megan McDonald

Opportunity: the growing EV market exacerbates existing challenges for electric utilities.

 

Electric Vehicles (EVs) are a rapidly growing market that’s expected to grow to significant size in the next decade. Below charts are from the International Energy Agency’s Global EV Outlook 2017 report and Edison Electric Institute’s prediction that the US will have 7M EVs by 2025.

 

Energy Utilities are taking notice. Utilities actively encourage EV adoption as they view it as an important future revenue stream. Southern California Edison’s report on EV adoption notes that utilities care about the location, charging schedule, and habits of EV owners. And, for good reason — EV households have significantly different energy use profiles:

Finally, the rise of renewable energy production in the grid has created new challenges for utilities. Utilities have long faced a deepening “duck curve,” which is greatly exacerbated by the above EV charging profile.

The electric utility market is a government-sanctioned geographic monopoly, and EV charging is fast becoming an important part of that market. We propose a solution to augment that market through tracking EV user habits and incentivizing behavior to flatten the duck curve.

Solution: Platform to augment the EV charging-Utility market and flatten the duck curve

We take inspiration from the NREL researcher that predicted and studied the duck curve: Paul Denholm. In an interview with Vox, he states that if utilities could provide the right price incentives, EV charging could be used to flatten, rather than exacerbate, the duck curve.

We propose creating a platform that connects the electric vehicle’s system to the cloud and use the data collected to collaborate with the utility companies in order to optimize the value for all parties involved. The platform would analyze the driver’s habits and calendar, predict future behavior, then advise the driver when to charge based on both optimized pricing and predicted future driving requirements. It would provide utilities with a representative data set with which they could predict demand and optimally price to encourage flattening the duck curve.

Because this platform would require both widespread deployment of software on EVs (which isn’t yet easily done) and cooperating with an electric utility, we think an EV OEM is best positioned to develop a joint venture with a utility and implement our platform. Therefore, we are proposing this solution for Tesla to incorporate into the software for its electric vehicles. This would be mutually beneficial for drivers, utility companies, and Tesla:

• Drivers: the application would help drivers remove the decision-making process of when and where they should charge their electric vehicle. Using a surge-pricing model, drivers would be incentive to charge their vehicles at locations that best fit their specific priorities (i.e. cost, time, range anxiety). For example, drivers would be able to plug their car in overnight and the system would then optimize at which times that night the car would be charge (i.e. 1am-3am and 5-6:30am).
• Utility companies: By leveraging the data generated by the system, the utility companies would be able to predict electricity usage and optimize energy production and maintenance of its assets in different areas. It would also help them set pricing to encourage smoothing of demand surge throughout the day. Lastly, eventually, utility companies will be able to optimize where to expand when and where to invest in the CapEx of expanding charging stations throughout a particular current city or expand to a new one.
• Tesla: the main benefit for Tesla is the differentiating advantage that it gives them over competitors. For example, Tesla could use the data to negotiate a cheaper electricity rate with utility company, attracting consumers to choose their EV rather than competitors.

Feasibility is increased if an OEM implements this solution.

As the density of EVs which have the solution installed increases within a specific area, the platform will gain more profound insight into the usage patterns of geographically clustered consumers. By understanding their preferred routes, planned trips and holidays, and charging habits, the platform will suggest the most appropriate time for the consumer to charge their EV based on their preferences (i.e. convenience vs pricing).

That considered, the feasibility of the solution rests primarily on the ability for the platform to be scaled. For success, there must be a representative sample in any and all given geographical locations that the solution is marketed for the necessary insights to be achieved.

Given that a lack of scale is the most challenging obstacle preventing a dataset significant enough to draw insights, the implementation will be best suited to rollout from an EV manufacturer. The OEM’s willingness to adopt the software and install as standard in all vehicles will provide the scale required, increasing the utilisation percentage as the EV market booms in the coming decade.

Pilot: Tesla uses OTA to implement a test-run in a small market

Tesla is currently the EV manufacturer best positioned to implement this solution. To begin the pilot, Tesla would implement the algorithm by releasing it as part of an over-the-air (OTA) update to a portion of its existing customer fleet. Data is collected both on the usage of each vehicle and the recharging preferences manually entered by end users. This approach will build on its existing data set of travel routes, providing a deeper understanding of consumer preferences for recharging based on their disclosed preferences and inputs on future travel plans by end users.

Once the customer-specific usage patterns of all Teslas within each specific geography is understood, Tesla can predict future usage patterns for where and when all Teslas are expected to travel and recharge, based on machine learning and AI modelling. This data will identify periods of high demand, which will be of particular interest to utility companies.

Tesla can then share these predictions with utility players, encouraging them to charge variable pricing within specific time periods of geographies. Variable pricing will allow utility providers to smooth out their demand curve and increase margins. In return, Tesla may wish to negotiate lower kWh rates at public charging locations

Competitors & Risks

There are many applications that help EV drivers find charging stations, but none have the ability to predict pricing or analyze a driver’s behavior to recommend charging time.

GM is the main Tesla competitor for plug-in EVs in the US; GM could implement a similar solution. However, this would probably help — it would get more utilities on-board with dynamic EV charging pricing to influence demand curves.

One critical risk is driver adoption of the service. Drivers face significant range anxiety, which is a key barrier to EV adoption (despite the irrationality of this fear). The temptation to plug in the EV and charge it as soon as the driver gets home from work is pretty strong. The application and utility can defeat this in three ways:

• Set prices to significantly incentivize changing charging behavior
• Track savings accrued — recognize good behavior
• Reliably predict the driver’s schedule to avoid an under-charged emergency

Sources

Global EV Outlook 2017 report: https://www.iea.org/publications/freepublications/publication/GlobalEVOutlook2017.pdf

SC Edison report on EVs: https://newsroom.edison.com/internal_redirect/cms.ipressroom.com.s3.amazonaws.com/166/files/20136/SCE-EVWhitePaper2013.pdf

SEPA report on energy grid preparedness for EVs: https://www.utilitydive.com/news/time-is-not-on-their-side-utilities-ill-prepared-for-ev-demand-sepa-finds/519530/

Vox Article with Denholm, discover of the duck curve: https://www.vox.com/energy-and-environment/2018/3/20/17128478/solar-duck-curve-nrel-researcher

Opportunity

The US lunch industry is in a state of flux with an ever-increasing number of businesses opting for in-office options vice eating in a restaurant to either increase productivity or provide additional work benefits. Traditionally, many companies have spent large amounts of money on office catering, which largely goes to waste.  Thus many firms are turning to technology to answer the question, “what should we have for lunch?” As such, firms now specialize in delivering individualized, restaurant-quality food directly to the workplace. These firms take advantage of the many restaurants offering a wide variety of food near urban offices. However, ordering lunch for a large group of coworkers can be a time consuming task, particularly when attempting to ensure everyone gets what they want.  Forkable seeks to bridge all of these elements and use machine learning and technology to match workers to restaurants (food) and vice versa.

Summary of Solution

Forkable uses customer inputted preferences, meal feedback ratings, and individually indicated tastes along with machine learning algorithms to learn the food preferences of office workers and then automate the food ordering and delivery process for individually satisfying office lunches.  This approach reduces the complexity of ordering lunch for large office co-worker groups by not only predicting what restaurant and food every person in the group will enjoy, but also completing the ordering, pickup, and delivery of those lunches. Forkable also allows for users to meet budget constraints and use human override if required. Additionally, Forkable eliminates the waste associated with catering corporate lunch as well as cutting down on the internal administrative cost of ordering lunch, saving companies money.

Effectiveness and Commercial Premise

Forkable is part of the emerging space that is the intersection of the $9B catering and $200B U.S. lunch markets. In this area, firms typically charge a delivery fee plus an order surcharge to generate revenue. As Forkable is still very much in the startup phase, it remains impossible to directly measure either the potential or effectiveness of Forkable. However, Fooda may provide a closer benchmark by delivering meals from a single restaurant, earning $48M in revenue in 2016. At the top end, Grubhub provides a measure of a large, public company that allows one to achieve the same results with more effort required from the user, generating $683M in revenue in 2017.

Competitive Landscape

Lunch catering is a crowded and diverse competitive landscape.  There are low barriers to entry and a wide variety of approaches to catering.  There are traditional businesses that have a limited tech presence, but will cook, deliver, and serve the food.  There are also a variety of well known tech-enabled businesses, like Grubhub, Postmates, and Uber, which focus just on the delivery of the food from restaurants to customers.  Other lesser known office-catering businesses include Fooda and Eat Club. Fooda’s business model involves bringing local restaurants into offices to serve a selection of food directly to the customers.  This model is interesting and can operate with low overhead, but a narrow selection from a given restaurant will not necessarily have something that everyone likes. Eat Club has a model that is similar to Forkable in that it allows customers to individually select their preferences.  In this way, it is similar to just ordering from a menu. It does not use machine learning to select dishes and restaurants which are best suited to customer preferences in the way that Forkable does.

Proposed Alterations to Increase Value

Forkable currently optimizes meal selection for the customer through the previously described preference-rating feedback loop. However, restaurant selection occurs by using websites known for ratings, such as Zagat, Yelp, and Foursquare, and selecting 10 restaurants in a given area from which to offer meals. As a result, the process could be improved by using machine learning to optimize restaurant selection. Forkable would take the upcoming week’s customers (which they know in advance) and identify the restaurants through machine learning within a predefined radius that provides the highest number of potential matches for the customer set. This process would accomplish two goals. First, it would increase the quality of the service and customer satisfaction as a result. Second, it would decrease costs through both restaurant search-radius optimization (i.e., pick an adaptable area that will minimize delivery costs) and decreasing the cost of customer acquisition/retention. By offering a higher quality service at lower cost, Forkable would increase its customer base, attract more restaurants, and be able to offer more options, all part of the virtuous flywheel associated with indirect network effects. As a result, this improvement would both create value as the emerging market size increases, as well as capture value by removing other close competitors such as Fooda and ZeroCater.

 

Sources

Forkable

https://forkable.com

Forkable Wants Companies to Forgo Buffet Style Lunches

https://thespoon.tech/forkable-wants-companies-to-forgo-buffet-style-lunches/

Grubhub 10-K

https://www.sec.gov/Archives/edgar/data/1594109/000156459018003852/grub-10k_20171231.htm

Food Tech Startup Marries In-office Catering with Recurring Revenue Model

https://www.forbes.com/sites/matthunckler/2017/06/08/food-tech-startup-marries-in-office-catering-with-recurring-revenue-model-to-surpass-3-million-arr/#1c8cfc2f2964

Team Members

Thomas DeSouza, Matthew Nadherny, Patrick Rice, Samuel Spletzer