Has there ever been as
much hype about a product that has yet to prove its value for most businesses?
That doesn’t mean you shouldn’t take a hard look at what AI might do for
you.
EXPERT OPINION BY HOWARD TULLMAN, GENERAL MANAGING PARTNER, G2T3V
AND CHICAGO HIGH TECH INVESTORS @HOWARDTULLMAN1
MAR 4, 2025
You just might need AI to help you
list all of the issues and concerns that surround AI today.
One of the most persistent worries in
the business community is whether any of the various large language models
(LLMs) are really ready for prime time and for widespread adoption by companies
looking to incorporate these new technologies into their day-to-day operations.
Even if you are willing to put aside
all of the commentary about hallucinations and false references, and the
circularity problems raised by these systems blindly ingesting the garbage
already being generated by other AI systems and thereby diluting the value and
accuracy of their own outputs, you still reach the fundamental question of
which version of the “truth” your own people can rely upon. Or even choosing
among competing offerings that are now creating and delivering inconsistent and
conflicting results.
It’s a very tough choice for the IT
department to decide which LLM, if any, to endorse and adopt at this point.
While a segregated sandbox to be experimented with wouldn’t cost a bundle
(apart from the overhead and personnel time), once any firm tried to
incorporate these systems into their own workflow at the enterprise level and
install it in hundreds of seats, you’d be talking about a few hundred thousand
dollars.
I guess that if you don’t care where
you end up, and you’ve got money to burn and want to tell your board that
you’re doing something, any road will get you there.
A free consumer offering and a novelty
accessed by millions of curious users is one thing. People will try anything
for nothing, especially folks with plenty of time on their hands and nothing to
lose. But this is not a sustainable solution for serious operators on either
side of the equation and – as we have already seen – it’s also not a remotely
profitable model for the primary providers, since they lose money on every
inquiry.
Why They’re Trying to Get Everyone Hooked on AI
All the big guys are racing to create
a viable AI assistant for the little people in the hopes (as has happened in
the past) that adoption from the outside in (remember all the ad world
creatives using Macs) will eventually dictate which larger solution a given
business will adopt. If your people all love Perplexity, you don’t really want
to start swimming upstream and pushing some other choice.
The civilian population is already
reaching the point of confusion and fatigue because there are at least half a
dozen major offerings in the market with more variations and versions coming
every day. ChatGPT presently towers above the rest with more than 350 million
monthly active users.
But Microsoft, Google, and DeepSeek
are already reaching some reasonable levels of scale and it’s never smart to
bet against fast followers when they are as deeply entrenched and well-funded
as these guys are. Watching Microsoft Teams slowly eat Slack’s lunch is a good indicator of where
these things often end up.
Microsoft’s decision to shut down
Skype and put the functionality into the Teams package is another good
indicator of the old tech rule that winners take all. Remember that Microsoft
itself spent $8.5 billion in 2011 to buy Skype to replace its own mediocre
video offering.
The AI Race Is Still Wide Open
No one is there yet in the AI race.
The main riddle is to make the assistant contextually savvy, and
surprisingly Amazon is a player in this race because of Alexa. With more than
600 million Alexa-enabled devices, the world is already comfortable asking
Alexa for help. And with new tech, familiarity builds acceptance and comfort
rather than contempt. It’s still a “go with what you know” world.
All the major players aspire and claim
to be delivering the most accurate and comprehensive responses to carefully
crafted prompts. In fact, the demand for prompt architects and prompt
engineering has exploded as it becomes clear that even the best answer is
useless if you’re asking the wrong questions.
We’re also seeing a surge in new
businesses aiming to deliver industry-specific AI tools like GPT-4o for Law and
also startups that offer to help companies build their own small and custom
models based on their own proprietary data. The idea is to avoid the
generic overkill and costs of the major LLMs. You don’t have to boil the ocean
and burn big bucks every time you need some straightforward answers about your
own business and customers.
One other interesting new
startup, Avatar
Buddy, builds low-cost, task- and role-specific “buddies” for
sales and support people, as well as experts and digital twins for
educators, which provide real-time assistance and direction to folks in
the field.
But all these conversations tend to
return to the core issue, which is: How is a buyer supposed to evaluate and
decide between these many alternative tools when even extensive, comparative
tests are inconclusive or contradictory? There’s very little credible guidance
so far; the players keep updating their solutions and moving the measurement
goal posts.
Which means that for the foreseeable
future, if you want to hold your nose and jump into the pool, you’re probably
best advised to follow Yogi Berra’s classic advice: When you come to the fork
in the road, take it.
