# Tokenomics Adviser - Industry Leaders {% embed url="" %} Today Warena is massively excited to announce the hiring of two industry professionals, Roderick McKinley and Diana Nikolova, to advise on the games’ tokenomics. Tokenomics are one of the most important aspects of any Play-to-Earn game. Indeed, it constitutes one the foundational pieces of a company. As such, it was a priority issue to have best-in-class advisors onboard to ensure that the tokenomics in Warena were sound. Mr. McKinley has been appointed as advisor to 18 blockchain projects in 2021 thus far. Those that have already completed their IDOs have raised $15m between them. He comes from a traditional finance background and is a CFA Charter Holder. He cut his skills modeling deals for infrastructure assets valued at billions of USD, and by helping business founders raise the first millions for their start-up ventures. Since pivoting into the blockchain space, he has applied his quantitative problem-solving skills to a diverse set of use-cases, including DeFi, NFT marketplaces, DEXes, blockchain games, and Layer-1 incentive design. Ms. Nikolova has 18 years of Investment Banking experience, having worked at Merrill Lynch, Deutsche Bank and UBS. She was also an Investment Director at the tech fund Kinetik and was involved from the early stages of development for Arrival, a £9.5B Nasdaq listed electric bus & van start-up. She took interest in blockchain and invested in ETH in 2018, and, having seen the sector mature, decided to use her expertise while leveraging her solid corporate finance experience to help blockchain startups get off to a strong start. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://warena.gitbook.io/warena/team-and-backers/tokenomics-adviser-industry-leaders.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.