“What should we be doing about AI?” It’s probably one of the most frequently asked questions from our clients. It’s…well…not an easy answer. But it’s a great discussion. I find the best way to tackle it is to start wide and then zoom in.
The AI question is the ultimate cost-benefit analysis. There are societal implications behind mass adoption of AI, or so we’re told. (Mass layoffs, dangerous hallucinations, misdiagnoses, security threats, etc.) There are also economic considerations both for organizations and, again, for society as a whole. Beyond the ethereal questions, it seems like all the available cash in the system is pouring into this sole endeavor that few can define and even fewer can utilize.
If the recent earnings reports from the mega tech companies tell us anything, it’s that AI, while still in its infancy, largely benefits massive organizations that leverage it within their existing ecosystem. There is no transformative, single-serving AI company or function successful enough to provide clues as to how this will evolve over time.
Sure, there are instances where AI systems have augmented independent processes and workflows. And there are unicorn startups that have made wild claims about vibe coding solutions from scratch and building agents to perform functions previously filled by humans. But there are fairy tales and then there are stories, and right now the fairy tales are winning the day. The hyperscalers have real stories, but they don’t necessarily translate to business use cases that are scalable and replicable within most organizations.
Point being, we’re still in the early innings and there will be more carnage than upside in the next few years.
The carnage will be on the AI product side. At some point, the meager revenues and negative earnings of the most prominent names will force a reckoning in this market. The high-profile names we know today will dwindle, tens of thousands of startups that make transformative and revolutionary claims will run out of runway, and a flurry of mergers will come to define the winners and losers over time.
Perhaps a company like Anthropic will emerge as self-sustaining. Perhaps not. OpenAI may have already flown too close to the sun, leaving their most brilliant engineering minds to ultimately scatter in the wind. Meta may never figure out how to monetize AI in a way that satisfies its shareholders.
In a carnage-like scenario where the core of names gets blown apart and talent moves around, the mega tech companies that provide the commercial online backbone in the economy (Google, Amazon) will pull away from the pack—even more than they already have.
They will…
- Absorb the pieces of AI startups that work and discard the rest.
- Figure out how to leverage and incorporate specific aspects of technology and systems to enhance their existing models.
- Hire the best and brightest talent in the market.
- And the greatest inventions yet to come will illuminate the true opportunities to transform the marketplace; from users to providers, the functional applications of AI will emerge as parts of the mega platforms that we already know.
Now let’s bring the discussion down to the real world, where relationships still matter and humans still build things.
When we think about macroeconomic metrics, we focus on labor, market share, earnings, growth, and margins. One of the scary promises of AI is that it will gobble up the labor market to eliminate redundancies and increase productivity. But more than anything right now, it’s gobbling up R&D capital and a significant portion of the attention economy. But 80% of Americans work for privately held companies, government agencies, or nonprofits. The battle for AI supremacy is being waged among the large public entities; here on the ground, things are a bit different.
As a sales, marketing, and technology solutions provider that services more than 100 companies across multiple industries, we have a front-row seat for these discussions. While the AI discussion is ubiquitous throughout our portfolio of clients, no two companies are in the same position. AI adoption is incredibly uneven. It’s partly because we’re in the “Isn’t that neat?” phase of what these tools can do.
“Isn’t that neat” doesn’t pay the bills or impress founders and C-suite executives who are responsible for the P&L and balance sheet.
However, I’m beginning to see those proverbial “green shoots” pop up through the soil. As a HubSpot Solutions Partner, we are seeing productivity gains and operational efficiencies appear within the platform. And this lines up pretty well with our macro observations about the role of AI in society and who the winners and losers will ultimately be.
If we take the startup unicorns out of the equation and block out the market noise surrounding the OpenAIs and Anthropics of the world, the conversation sounds very, very familiar. The large solution providers (that includes SaaS companies) that incorporate AI into their existing stack and expand/enhance their service offerings are going to dominate the conversation. Yes, I’m suggesting that the winners are going to be the same as today and that the big guys are going to get bigger.
In the past, large-scale technology disruptions have changed the course of history. They have displaced entrenched companies and entire industries. The assumption all along seems to have been that AI will do the same but on an accelerated timeline, and that the timeline itself would be the most disruptive element. The difference this time around is that our tech companies are playing dual roles of disruptor and disrupted. Following this logic, it means that they are the ultimate beneficiaries. Did we really ever think they would allow it to be any different?
An organization that has already embraced mature and robust technology is ahead of the game. The innovations that accelerate processes and increase productivity will be served up within the tech stacks that are adaptable, powerful, and secure. I’m not suggesting that we’re all clear to ignore AI; rather, keep one eye open for opportunities and changes within your industry while you focus on locking down your internal tech stack and systems. Because the very best AI solutions will be ones you hardly notice that seem like they’ve been there all along. |
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