In Why Media Can’t Be Neutral, I speculated many reasons why media will inevitably exhibit bias. While the common man or woman (henceforth J6P) might agree with those reasons, I don’t expect them to accept the status quo since everyone needs objective sources to know the facts and make up their mind on social, economical and political matters.
What can J6P do?
Here are my personal best practices to navigate through the media and arrive at the truth on important topics:
- Get a handle on which media outlets are pro-Establishment and which are anti-Establishment. For example, Wall Street Journal and Bloomberg side with banks more often than not whereas TearSheet and Financial Brand unabashedly shill for fintechs (Ditto Fintech Mafia on X fka Twitter). I read one pub that shills for each side, and apply a suitable Finnegan’s Factor – “that number by which you multiply your answer to get the right answer” – to arrive at a neutralish take.
- Google the topic and skim the first three stories on the SERP, and, in parallel, ask ChatGPT for its take on the subject, and “triangulate” between these multiple outputs. PSA: Don’t ask ChatGPT to summarize articles – I got mixed results when I tried that on my blog posts (click here for an accurate summary and here for a terribly wrong summary).
- As I’ve said many times, Caveat Emptor is a feature, not a bug, in capitalism. Media is not different from other industries. One major source of “reader beware” in media is data. I frequently come across mislabeled charts, mixups between lakhs / crores / millions / billions, and a plethora of other gaffes in business journalism these days – even in publications with the cachet of FORTUNE magazine. It’s as though newsrooms have fired all their editors, let journos publish stories directly, and left it to their readers to do the editing.
Are these techniques foolproof?
In my experience, they suffice for STEM (Science Technology Engineering Maths) topics – involving physical objects that obey natural law, they have a finite distance from axiom to theorem aka truth.
But they’re not guaranteed to illuminate the truth in topics that involve human beings who are not governed by natural law. In social, economical and political issues, the chips may fall any which way, and more context can change the perception of truth. Ergo, pursuit of the neutral pov is an unbounded exercise. Reading six articles may not get the reader any closer to the truth than reading two articles.
Given that traditional media can never fully rid itself of bias, it’s natural to ask if there any new models of media that solve the bias problem?
Yes. There are two new media models that are less biased or at least make the direction of their bias evident.
One of them is real and the other is a figment of my imagination.
1. Trade on news
The traditional business model for media is driven by advertisements. It worked well until newspapers, radio stations and TV channels started losing ad revenues to social networks like Google and Facebook. The top players in the media industry were able to generate enough subscription revenues to compensate for the loss of ad revenues. But the medium and small players were not.
I wish media invested more in in-depth reporting. But the reality is that most publishers are struggling just to stay in business – @asteriskmgzn.
How about trading on news to make up for ad dollar loss?
Since traditional media gathers news from inside sources, it is forbidden by insider trading laws from trading on its stories.
What if there were a new way to source news that made it kosher to trade on it?
Turns out there is.
Enter Hunterbrook. The firm has two arms: media and hedge fund. The media affiliate does deep investigative pieces about companies and the hedge fund affiliate trades on them.
Hunterbrook Media publishes investigative and global reporting — with no ads or paywalls. When articles do not include Material Non Public Information (MNPI), or “insider info,” they may be provided to our affiliate Hunterbrook Capital, an investment firm which may take financial positions based on our reporting. Learn more here.
In addition to trading profits, a whole new world of revenue streams opens up for such media firms. According to Matt Levine, author of Bloomberg Money Stuff newsletter, if you can find out bad news about companies, there are many ways to make money:
- Short selling (i.e. trading)
- Class action lawsuit by customers
- Securities fraud lawsuit by shareholders
- SEC whistleblower program.
Media outlets following this model cannot use inside sources and must base their stories on publicly available information. Neither can they be dispassionate and indifferent to profitability, two key traits of traditional journalism.
We know this newsletter may not be seen as traditional journalism, which is generally known for being dispassionate, reliant on inside sources, and indifferent to profitability. We are proudly passionate. https://t.co/FrcdqSFdJE
— Matt Levine (@matt_levine) April 2, 2024
Hunterbrook Media differs from the traditional research departments of sell side brokerages in three ways:
- It employs low cost journalists rather than highly paid CFAs.
- While its exposès disclose its trading position, they don’t give BUY / SELL guidance to the market, so it’s not a financial advisor.
- It publishes its reports in the public domain instead of restricting their circulation among a closed group.
Hunterbrook does not trade on all of its news. Matt Levine ascribes at least four reasons for that.
4 likely reasons why Hunterbrook didn’t trade on its Posco / Myanmar expose:
1) News is bad for society but not for company
2) Story used inside sources
3) To take moral high ground
4) Short selling is currently banned in South Korea.
via @matt_levine.— Ketharaman Swaminathan (@s_ketharaman) May 20, 2024
Nevertheless, J6P could argue that its coverage would be biased towards trading-worthy topics.
It’s early days but the Hunterbrook news plus trading model could be the future of media.
2. Robot journalist
My imaginary Robot Journalist (RoJo) is equipped with a camera, sensors, and an LLM (Large Language Model), and is dropped in the middle of warzone or any other theater of action. The camera and sensors will gather unlimited inputs from Ground Zero. The LLM will process these inputs in real time, backed by the unlimited treasure trove of pretrained content it possesses.
Ergo, RoJo can produce extremely comprehensive stories very rapidly and without the constraints of context and deadline faced by human journalists.
To me RoJo seems like panacea for all evils with traditional media. But I doubt if it will satisfy everyone – some readers will always carp:
- Where was RoJo journalist dropped? If it was at the center of the theater of action, its narrative might be different than if it was dropped at the northern tip or southern tip.
- Which LLM does RoJo use? The ChatGPT story might differ vastly from the Claude story.
- Which image recognition AI does RoJo have? The article written based on images recognized via MediaNet might be different from that written based on images recognized via Inception.
- Which image generation AI does RoJo use? Since pictures are worth a thousand words, the story could change drastically depending on whether RoJo used DALL-E or Stability AI.
- Where was RoJo made? Made in China robot may be biased towards China and Made in USA robot may be biased towards USA.
Man may go to Mars but I doubt if the media bias problem will be fully solved in the forseeable future.
