AI Thoughtmakers

Full-time hiring takes 3 to 4 months. The market expects results in the same timeframe. Something has to give.

In this episode of AI Thoughtmakers, we sit down with Vikas, Full Stack Engineer and Team Lead at GeekyAnts, for a no-jargon conversation about fractional engineering teams — what they actually are, when they make sense, when they don't, and why the fastest growing companies are already using them to ship faster without bloating their headcount.

From the house renovation analogy that finally makes fractional hiring click, to the biggest myth founders believe about handing over ownership, this episode cuts through the confusion around fractional hiring, outsourcing, and freelancing — and gives you a clear framework for when to use each.

Key topics covered:
• Why traditional tech hiring can slow down product development
• Fractional engineering teams explained in plain language — no jargon
• Fractional vs permanent hiring: speed vs longevity — and how to decide
• The biggest myth about fractional teams that's costing companies trust and output
• When you should NOT hire a fractional engineering team
• How knowledge transfer actually works — and why most companies get it wrong
• The real ROI of fractional engineering — beyond simply reducing costs
• How AI is changing the fractional model and shrinking the team sizes needed to ship
• Why the future of engineering is smaller teams shipping more products

Whether you're a founder building a product, trying to move fast without overhiring, an engineering leader plugging gaps in your team, or an enterprise looking to scale specific capabilities quickly — this episode gives you the playbook.

Subscribe for more conversations on AI, engineering, product development, and the future of software.

Connect with Vikas: LinkedIn -   / vikaskumar75   
Connect with Prem: LinkedIn -   / premgoswami   

What is AI Thoughtmakers?

AI ThoughtMakers is a leadership-driven podcast featuring conversations with CTOs, founders, engineering leaders, and AI experts discussing real-world AI adoption, scalable engineering, product innovation, and the future of technology.