[London School of Economics JournalismAI] How to think about AI for news?

Grounding

There are no easy answers because on one hand you have to play the AI game and on another hand you have to balance out reality. News is a low ARPU commoditized business.

History of bad decisions

We are left behind with lesser and lesser variables in our control to do something about our own business. Hence, as an industry, sometimes we choose to spend hard-earned cash on things that don’t improve competitive differentiation. Below are some common decisions:

Supply-side efficient innovation: In absence of a clear strategy to grow revenue (demand-side innovation), we choose to invest on copy-cat products that do the same thing but a little twist. Hence, most cash rich news companies have built their own custom-built CMS, grown it, realized it isn’t adding much and finally abandoned it. This problem is compounded by the fact that news companies struggle to build technology.

Novelty: To demonstrate that the iPhone could be further slimmed, Steve Jobs famously threw an iPhone prototype in water highlight air gaps. If such qualitative feedback adds to your differentiation then it is useful. However, sometimes we invest hard-earned dollars building products to accommodate such niche feedback from our leadership even though our audiences might not really care about it. For example: UI instead of UX and your method of journalism.

Surfing BigTech Waves

Rule-based complexity: Finally, because we are a low margin business, often business and product folks over-complicate the product by adding many custom rules to extract an additional 0.5% revenue. This makes the organization and product so complicated that no one wants to take the risk of changing it after that.

Obsession with new: My hunch is that at some level the nature of our industry — covering what’s new — influences what excites us. Hence, the news ecosystem has a graveyard of shallow POCs that do not meaningfully move the needle on anything constructive.

Competing against BigTech

Cash-flow versus Balance-sheet businesses: Most BigTech are balance-sheet businesses. They raise a tonne of capital from VCs to invest today and earn revenue in the future. In contrast, news tends to be a cashflow business, where we need to balance our P&L every year and we spend from what we typically earn.

Beyond the obvious power and leverage disparity, the reality is that big tech companies have immense cash reserves and technical skills that have managed to build and deploy AI at scale. We don’t.

The Generative AI Wave

Let’s evaluate the Generative AI wave through the above lens

Obsession with new

• Most major newsrooms have jumped into retooling their CMS with OpenAI.

Supply-side innovation

• Do we’ve the technical manpower or skill to build our own LLMs? No.

Will Using LLMs Help Me Save Editorial Costs?

Surfing BigTech’s waves

• By building on OpenAI, will a newsroom be able to build competitive differentiation? No. Because everyone else has access to it.
• In fact, LLM is the latest technology that is forcing newsrooms to move more inland into cost-heavy operations like fact-finding and writing the first draft. Many of the You’ll spend days investigating and in seconds it will get scrapped out.

Novelty

• Will we’ve newsrooms and editorial leaders obsessing over the nuances of prompt-writing so we can get it just right? We already do. Will it lead to competitive differentiation? No!

Economic viability

• Can we afford to build our own LLMs? It would take upwards of a few million USD in GPU server costs to train a LLM.
• Can a newsroom afford to build demand-side, i.e., audience-facing LLM products? No. Because the cost of serving a cached page from CDN is 1/1000th the cost of a meaningful Q&A with OpenAI APIs. The unit economics doesn’t add up.

Preparing for the AI Wave

I’d recommend picking one of two extreme paths — Compute-heavy or compute-light — while acknowledging that adopting AI is fraught with challenges.

Below are some suggestions for each group:

Strategy for the AI Wave

Let’s again revisit the realities of our business:

• Fundamentals of an editorial product business
• Editorial product businesses have been edged out of its core functions

Next, let’s review what what mainstream artificial intelligence already solves for! Once you see this, you’ll realize a commonality between what we’ve been edged out of and what mainstream AI already solves for, a.k.a., amplification.

Given this, here’s my suggestion:

The most obvious point is that…

Compute-light

Most compute light organizations should avoid building anything custom, move to WordPress and then integrate with tools that integrate with WordPress. Alternatively, you can piggy off one platform like SubStack — however, that’s risky! Most of these tools and platforms will give you clear tools eventually to automate or augment the amplification factors.

If you must invest your hard-earned cash in AI then I would highly recommend that you only invest in building competitive differentiation on the demand-side. Your audiences should recognize that differentiation and be willing to pay additional cash for it. For example:

• Owned Identity: May be if you’ve a unique visual drawing style, like Sketchplanations has, then train an AI to deepen this unique style.
• Owned Network: Nintee is a new media-enabled personal coaching that already uses AI to deliver content and audience engagement to audiences on Discord.
• Owned Media: You can use AI to publish professional work products like The Information does. Alternatively, you can use LLMs to publish summaries and catch click-baits as Artifact News does. Finally, if you’ve paywalled content that has not been scrapped, then like Skift you can launch bots that audiences can chat with.

Compute-heavy

Compute heavy newsrooms should invest in optimizing both driving and amplification factors

For example:

• A clear driving or differentiation factor that mainstream newsrooms can work on is time to market, i.e., how fast do they break news. For this, most functions post reporting and news gathering can be near automated using generative ai.
• On the amplification side, they can go multi-format and provide the same news in 20 different formats to micro-optimize gains from algorithmic distribution for each format.