As always, Patric and Shub speak about nothing at all in the venture built world:
Will AI make vertical software obsolete ?
Is funding for Construction-Tech ventures finally thawing ?
Are venture market maps nonsense or even dangerous for founders ?
And what was the craziest business they saw last week ?
Sky-high pre-money valuations seen in Europe for pre-seed construction deals
Patric shares anecdotes of some extremely high pre-money valuations being given in Europe to construction tech startups at the pre-seed stage, with some examples of valuations as high as 25 million euros pre-money for just 2 founders and a deck. This seems to go against the trend of more rational valuations in the current environment. Patric says "I've been telling a few friends, hey, I have the feeling that in Europe right now, there's crazy shit happening. Like there's deals that two founders, a deck 25 pre." Patric and Shubhankar discuss possible reasons like pent-up deployment pressure for investors or a false sense of supply shortage. They agree deals should create win-win outcomes, not fleece founders or investors.
Investors irrationally moving from proven models due to AI fears
Patric recounts how one successful SaaS investor is moving away from proven vertical SaaS models they understand well into unfamiliar spaces like robotics and space tech due to fears of AI's impact. Patric argues this is irrational, as AI is a horizontal enabler that needs verticalization, similar to how "React and mobile don't kill vertical SaaS." However, vertical software without AI elements will be "penalized by competition." Shubhankar agrees this detour seems random and the competencies don't directly transfer.
Construction tech investors should focus on customers and talent, not vanity metrics
Shubhankar argues investor-created landscape lists and top startup rankings often become "graveyards," providing false validation. Patric wants to "backtest past lists to show the highly-touted startups often fail." He believes for early startups pursuing customer adoption and talent retention is key, not pursuing vanity metrics like lists. Shubhankar agrees, noting taking these lists too seriously can cause founders to "gaslight themselves" and implode from unmet expectations.
Construction tech quality not defined just by trendy tech like AI
Shubhankar argues successful companies win through great execution, not just by adopting trendy new technology like AI. He gives examples of past innovations like cars, airlines, and e-commerce where few of the hundreds of pioneers survived long-term. Adopting AI doesn't define construction tech success - only "excellent execution across all elements of the business." Patric agrees AI is a useful addition but not sufficient alone.
Massive labor shortages show need to reimagine workforce creation
Patric and Shubhankar discuss how every major economy is facing labor and workforce challenges, from aging populations in the West to inadequate job creation in emerging markets. This convergence suggests a need to reimagine workforce creation, especially in construction trades facing shortages. Models that bridge surpluses and shortages via better skill-building and job matching are promising.
Construction tech adoption still early, even large firms banning AI
Patric argues that he is more excited about the verticalization of AI to make use of corporate data behind firewalls with even large advanced firms like Samsung banning general AI models until they figure them out. He compares it to how the Internet took decades to transform B2B. Shubhankar agrees AI will ultimately matter but cautions against overestimating its current influence in transforming slow-to-change sectors like construction.
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Keywords: construction tech, pre-seed funding, startup valuations, Europe, AI, robotics, space tech, vertical SaaS, investor trends, startup rankings, customer adoption, talent retention, vanity metrics, successful startups, execution, labor shortages, workforce creation, construction trades, construction tech adoption, digital transformation, Samsung