I'll take a deep breath and approach this carefully. To find the stocks in the artificial intelligence sector that had the highest market cap in 2020, we need to:
1. Generate as many subqueries as needed to organize information. NOTE: ONLY generate it if necessary and make sure to group appropriately. DO NOT get this error: "SELECT list expression references column marketCap which is neither grouped nor aggregated at [1:62]"
2. Repeat step 1 as many times as needed
3. Join the `my_project.my_dataset.stockindustries` and `my_project.my_dataset.price_data` tables to list all of the fields including the date field(s) for `stockindustries` (if applicable), the date field(s) for `price_data` (if applicable). Remember, if you're doing a calculation, write out each field. Note: we may need to use subqueries for complex queries.
4. Perform calculations if necessary
5. Perform filters as necessary
6. Group stocks if necessary (most commonly to avoid duplicates)
8. Add additional filters or blocks to avoid common errors (like division by zero)
9. Limit the number of results (or use a default)
10. Include all of the dates/values used in the calculation
Now I will generate the query:
{
  "sql": "WITH ai_stocks AS (SELECT si.symbol, si.name FROM `my_project.my_dataset.stockindustries` si WHERE si.artificialIntelligence = TRUE), market_cap_2020 AS (SELECT pd.symbol, MAX(pd.marketCap) AS highest_market_cap_2020 FROM `my_project.my_dataset.price_data` pd WHERE pd.date BETWEEN '2020-01-01' AND '2020-12-31' GROUP BY pd.symbol) SELECT ai.symbol, ai.name, mc.highest_market_cap_2020 FROM ai_stocks ai JOIN market_cap_2020 mc ON ai.symbol = mc.symbol ORDER BY mc.highest_market_cap_2020 DESC LIMIT 25"
}