Business
24-Year-Old College Dropout, Dylan Jacob is the King of the Drinkware Market

At a mere age of 24, Dylan Jacob is a force to reckon with. Already the king of the drinkware market in the United States, Jacob is a serial entrepreneur who has successfully run two businesses before starting BrüMate.
Every year, millions of aspiring entrepreneurs come up with fantastic business ideas. While some fail, some others succeed and set an example for others to follow. Passion, creativity and confidence are traits required in good businessmen. But for them to turn a business into a successful venture, understanding the consumer’s needs is important.
Indiana-based Dylan Jacob believes that, “Before setting out to create any product or service you should be out there talking to your ideal customer base to help shape and transform your concept into a viable product that the general population will get behind.”
Always amongst the top 10 in his class, Jacob studied Engineering at the prestigious Purdue University. It was then that he started a small business of part supply for repair which he sold to one of the company’s franchise customers.
After two semesters at Purdue, Jacob made a risky decision which completely changed his life. He dropped out of college to pursue entrepreneurship full time. He then started a high-end glass tile company and sold it in 2017 which is still a successful venture under the new owners. But his third and the most successful venture, BrüMate is the closest to his heart.
At a Christmas party, Jacob left his drink unattended for a few minutes and found the drink to be quite warm when he returned. He grew curious and started looking for koozies online to keep his drinks cold. He was surprised that there were no koozies available for his choice of beverage. So in 2016, he launched BrüMate, an insulated drinkware brand specializing in adult beverages.
In its first year, BrüMate made $2 million in sales without taking a single penny from investors. In the second year, the company recorded a 1000% profit with $20 million revenue. In 2019, Jacob aims at crossing $35 million in revenue. One of the most popular product of the company, the Hopsulator TRiO keeps your drink cold till you finish it. The Winesulator is another best-selling product which keeps your wine cold for 24 hours. Apart from these, there glitter flasks and a variety of accessories to choose from.
Jacob has made it in the Forbes 30 under 30 list two years in a row and is also one of the finalists for ‘Entrepreneur of the Year – 2019.’ All products by BrüMate are designed and conceptualized by Jacob himself and he’s increasingly adding new products on the shelf based on market requirement. According to a Drinkware Market Report, the industry is estimated to cross $11 billion by 2023 and the rate at which BrüMate is growing, Jacob is sure to be one of the top contenders in the world market.
At 24, Jacob is running one of the fastest growing businesses in all of United States and is the leader in the drinkware market. But even after achieving so much, he wants to explore, take more risks and grow his business further. “I have seen entrepreneurs hesitate to take risks because of fear of failure. However, real success comes to those who dare to take the unexplored path. Today, even though I have established myself in the industry, I wish to experiment and explore newer markets, achieve greater heights, and become a market pioneer,” Jacob says.
Business
MetaWorx: Building Full-Stack AI Teams, Not Just Automation

Automation still dominates most headlines, yet the returns often fail to meet expectations. A sprawling chatbot rollout might shave a few support tickets, but it rarely shifts the profit-and-loss statement in a lasting way.
McKinsey’s 2025 workplace survey pegs AI’s long-term productivity upside at $4.4 trillion, but only one percent of enterprises say they’ve reached true “AI maturity.” MetaWorx, a Dallas, Texas-based AI employee agency founded by Rachel Kite, argues that the shortfall has nothing to do with models and everything to do with people.
“Treat AI like a point solution and you’ll get point-solution results,” shares Kite. “You need a roster that can carry the ball from raw data to governance, or the whole thing stalls at the proof-of-concept phase.”
The pod blueprint
When a plug-and-play automation script collapsed under real-world data drift, costing Kite a lucrative contract, she sketched the six-person “pod” that now anchors every MetaWorx engagement:
- An infrastructure architect to tame compute costs.
- A data engineer to secure and shape pipelines.
- An applied scientist to prototype models against live feedback loops.
- An MLOps engineer to automate rollback and retraining.
- A domain product lead translates forecasts into features users actually notice.
- Ethics and compliance analysts to stress test outputs for bias and keep the audit.
The team’s first sprint still delivers a quick-win bot — “small enough to calm the CFO,” jokes Kite — but the roadmap quickly pivots to reliability, explainability, and eventually optimization. By tying every algorithmic decision to a quantifiable business metric, the pods turn AI from a science project into a growth lever.
Recruiting for curiosity, not credentials
With Bain & Company predicting a global AI-skills crunch through 2027, MetaWorx has stopped chasing unicorn résumés. Instead, it hires “adjacent athletes”: a computer-vision PhD who hops from medical imaging to warehouse surveillance, or a former journalist who recasts her nose for story into prompt-engineering finesse.
“Domain expertise expires fast,” Kite says. “What doesn’t expire is the instinct to ask better questions.” The result is a lattice of overlapping skills that stays flexible when models wander into the long tail of edge-case data.
A culture of rapid experiments
Inside MetaWorx, every idea faces the same litmus test: ship something — anything — into a user’s hands within 21 days. The “three-week rule” forces prototypes into the wild early, where failure is cheap and feedback is swift. Post-mortems, including cost overruns, are circulated company-wide, erasing any stigma associated with missteps.
That laboratory mindset powers velocity. “Our first model is almost always wrong,” Kite admits, “but version 1.0 is the tuition we pay for version 2.0.” The philosophy echoes her TEDx talk on resilience: progress is iterative, not heroic.
How leaders can steal the playbook
Executives itching to replicate MetaWorx’s results don’t need a blank check. Kite offers a five-step sequence:
- Inventory pain points, not tools: Walk the P&L line by line and tag the friction you can measure.
- Map the stack to the problem: A recommendation engine, for instance, requires behavior data, retraining triggers, and feedback capture — automation alone won’t suffice.
- Stand up a pod: Reassign existing talent into a cross-functional tiger team before hiring externally; the chemistry test is free.
- Measure the story, not just the statistic: Pair model accuracy with human-scale metrics like ticket backlog or employee churn.
- Budget for the boring: Reserve at least 30 percent of spend for MLOps and governance; Stanford’s HAI review links most AI failures to neglected upkeep.
Taken together, those steps shift AI from a pilot novelty to an operational habit that compounds value rather than topping out after an initial PR splash.
Character still scales faster than code
MetaWorx plans to double its headcount this year, yet Kite insists the secret isn’t a proprietary framework or a monster war chest. It’s credibility. Clients see a founder who has wrestled with the same outages and surprise bills they face. That authenticity converts skeptics faster than any algorithmic novelty.
“Tools level out,” Kite says. “Culture compounds.”
The insight lands in a marketplace still dazzled by generative fireworks. Yes, MetaWorx ships models and dashboards, but its true product is a mindset: resilience over rigidity, questions over credentials, experiments over edicts. In Kite’s world, automation is merely the appetizer. The main course is a full-stack team that knows why the model matters to the business and who owns its success after launch day.
And that, Kite argues, is how AI finally graduates from cost-cutter to growth engine, one curious pod at a time.
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