This year marked a turning point for enterprise tech as spending recovered and the economy stabilized following years of rising interest rates and supply-chain disruption. While no one knows what lies ahead, here are five things we thought summed up a pivotal year.
Today: A look back at some of the most notable developments from the past year and the last Runtime Roundup of 2024.
Today: Salesforce continues its agentic AI push, Databricks secures one of the biggest funding rounds in tech history, and the rest of this week's enterprise funding.
Tech and media leaders are increasingly worried that the push to use generative AI to automate analytical business tasks could produce a generation of workers that never develop the foundation to do the job well at a senior level.
Over the last 18 months, businesses have been urged to adopt generative AI services sooner rather than later, or risk falling behind competitors that have embraced the technology. A small but noticeable amount of desperation is seeping into the pitch as companies start to realize that adopting those services and taking away the moments that make up a dull day could have far-reaching effects on how they hire, train, and promote junior staff members.
Honeycomb co-founder and CTO Charity Majors captured this well in an essay last week entitled "Generative AI is not going to build your engineering team for you." Over the last several weeks of travel across the Bay Area, I've also had several conversations with tech and media leaders who are increasingly worried that the push to use generative AI to automate analytical business tasks could produce a generation of workers that never develop the foundation to do the job well at a senior level.
"Software is an apprenticeship industry," Majors wrote. "You can only learn by doing…and doing, and doing, and doing some more."
The same is true in many other industries that are looking to AI in hopes of reducing the number of junior staffers needed to produce basic reports about how a marketing campaign landed, or analyze earnings results across a given sector, according to folks I've recently talked with.
Much of that is busywork, and seems like the kind of thing generative AI was designed to save us from. But it's also how people learn.
"Giving code review feedback to a junior engineer is not like editing generated code. Your effort is worth more when it is invested into someone else’s apprenticeship," Majors wrote.
This is called the "to be sure" section: there's no doubt that in general, automation has eliminated countless business tasks that have freed up time for other, more worthy pursuits.
Nobody closes their quarter on paper anymore, but people raised in the current environment still manage to become senior finance executives. And if anything, coding assistants reinforce the idea that coding is just one part of a productive software engineer's job, and not necessarily the most important one.
Likewise, writing assistants and auto-generated meeting summaries can make it much easier for a harried knowledge worker to communicate clearly or avoid missing an important detail. And generative AI assistants could allow people with certain types of disabilities to bring their much-needed perspective to the modern workforce.
But senior tech and business leaders need to realize that they risk "cannibalizing" (as Majors put it) their future by adopting generative AI services solely to reduce headcount in the junior ranks.
Businesses that depend on teamwork to develop products or win contracts need people with a variety of skill sets and experience levels to thrive. Businesses that want to be around for a while also put a lot of emphasis on internal training and development, because recruiting external people at senior levels can be more expensive than building a junior person into a senior role.
That's not to say businesses should ignore the potential of generative AI services in helping employees go about their jobs, but that those services need to be adopted in ways that still allow people to acquire the skills they need to thrive.
And that probably means slowing the pace of generative AI adoption, which is starting to worry AI vendors.
(This post originally appeared in the Runtime newsletter on June 18th, sign up here to get more enterprise tech news three times a week.)
No one really agrees on a strict definition of "agent," but recent breakthroughs in large-language models have allowed companies to build enhanced versions of chatbots that can respond to natural-language queries with a plan of action.
Now that open data formats are here to stay, unifying them to remove incompatibilities will be a challenge. Delta Lake and Iceberg get all the attention, but tech developed by the creators of Hudi could make it happen.
As Snowflake and Databricks gear up for back-to-back user conferences in early June, their customers are increasingly betting on open storage formats that give them new flexibility.
"The more significant decision is, 'what is your strategy for centralizing the data?' Because these data platforms do not do that; they are the place that it gets centralized to, but they do not centralize anything."
