Pitch: Personalizing your music experience

Background – What does the media industry currently look like, how is music selected in public places?  

Among  the media industry, music is the second most profitable medium worldwide with a 45% digital revenue share (with Games taking the first place with 60%). Moreover, music is completely going digital as the physical CD shipments  in the US have decreased from 940 million in 1999 to 88 million in 2017.

Consumers are not only shifting to digital music, but every year the time consumers spend listening to digital music increases. In less than 10 years the time spent listening to digital music increased approximately in 6 billion hours, and the gap between music streaming and music downloads has also been widening,  giving an opportunity to music platforms to implement and improve products that better match / suggest music to consumers in real time.

Today music is selected in the following way:

  1. B2B: Stores choose the music they play by taking into consideration the season of the year (holidays for example) and the average demographic of its consumers (age, gender, location, among others).
  2. B2C: At individual parties or reunions, consumers choose the music based on the preferences of the owner of the playlist, device and/or venue. The music is selected based on the assumption that the preference of all the guests is the similar to the preference of the owner.

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

There are 3 main pain points in the current way media is selected and played in public spaces:

  1. No feedback loop: Music and media is often selected and curated by an individual and consumed by a user group in a particular setting with a “best-guess” effort to match audience preferences.
  2. Impact of music on shopping habits: The current system is not optimized for the shopping habits of the individuals in the store but is based on broader, generalized rules of music selection such as aggressive rock for Sales or classical for perusing.
  3. Impact of music on bars/ clubs: Bars and nightclubs have disproportionate reliance on good music selection to attract and retain patrons, which is often repetitive or selected in line with staff wishes.

Proposed Solution – How can a AI address those pain-points?

We would hope to use the best practices of recommendation engines and network analysis to generate a group optimum playlist, optimizing on most satisfied customers rather than total social welfare. A playlist would auto-generate based on this predicted best possible sequence of songs and would be provided as an integrated option in traditional music players within the App Stores of Apple or Android. The pipeline steps are displayed below:

Commercial Promise

In terms of B2B the business model could be based on a subscription model application that companies use to gather information about the music preferences of the customers that visit their stores or that they have in their database. This information would bring value by improving the shopping experience, having the possibility to influence shopping habits and also for marketing purposes. This could be applicable to retail stores, bars and clubs. In terms of the B2C channel the technology could be used to improve the experience in private parties or reunions. In the B2C channel the business model would more based on partnerships with existing music streaming application to help them improve the service they offer.


Benefits: Immediate feedback loop in public places to create a more personalized experience for consumers, collection and analysis of data based on music choice effectiveness for various seasons/locations/events/demographics

Challenges: Issues identifying causation vs. correlation for level of music influence, potential negative public perception of using music to influence consumer feelings and actions, difficulty of introducing brand new music with little or no related data points

Potential Competition

The key players in the music streaming industry have the capability to develop a viable product, with music personalization being a key push in recent years. Spotify is the market leader in terms of its influential playlist selections.Rivals such as Pandora, Google Play Music and Apple Music are also investing in personalization of music services.

In the startup space, Muru is a company targeting the B2B space. It offers background music streaming services for venues, allowing the venue to create a music template that will keep playing the full duration that venue is open and the playlist will also evolve on the fly.










Pitch: Blockchain in the Art World

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.


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.