Second Mountain

Gen AI’s Real Impact on the Job Market Generative AI (Gen AI) has officially moved from hypothetical to here-and-now in reshaping the workforce. A recent study analyzing over 1.3 million job posts shows just how fast tools like ChatGPT and image generators are altering online labor dynamics — and the findings are striking. 🔍 The Numbers Behind the Shift: After ChatGPT’s launch, demand for jobs prone to automation—like writing and software development—fell sharply, with weekly job posts for writers plummeting by 30%. Software, app, and web development roles saw a 20% drop, while engineering roles decreased by over 10%. Similarly, graphic design gigs declined by 17% following the launch of AI image generators like Midjourney and DALL-E. 💼 Rising Competition and New Skill Demand: As job postings dropped, competition for the remaining roles intensified, with bid rates up by 8.5% across automation-prone jobs. Meanwhile, demand for Gen AI-related skills surged: over 88% of jobs that now list “ChatGPT” as a skill requirement belong to automation-prone categories, particularly software development. 💡 Embrace Gen AI to Upskill, Not Downsize: While the numbers suggest change, they also highlight opportunity. As job complexity rises, companies willing to invest in AI training and reskilling will be better equipped to navigate this shift. For leaders, the message is clear: adapt, upskill, and harness Gen AI’s potential for growth, rather than fearing its impact on jobs. Is your organization moving fast enough to embrace Gen AI? If not, let’s connect on how to get these strategies working best for you. Read more here:

Is the middle path to innovation overlooked? A recent study finds that most businesses fall into two extremes when it comes to their innovation model. At one end, companies focus on internal R&D efforts to maintain revenue streams and market share while minimizing R&D budgets. This model often results in incremental innovations that carry low risk but generate modest growth at best. At the other end, companies, such as VCs, favor “transformational” innovations that aim to generate outsize returns. This is a high-risk approach, which bets that returns from successful efforts will more than compensate for failures. Under this model, the exit prices that innovators require mean that a large firm must pay a hefty price to purchase a successfully launched start up. There is however a “middle path” which balances these models. By setting up an external accelerator, companies can target medium-risk innovations that lead to higher growth without the high cost of acquistions. By outsourcing innovation to an accelerator, companies are able to: 1) experiment with bold ideas without disrupting day-to-day operations, 2) develop innovations with the company as the primary customer, meaning they are ready to go to market faster and fit seamlessly with core business needs, and 3) tackle higher-risk projects while keeping costs predictable, offering a controlled way to pursue growth. What innovation model is your organization pursuing? Leave a comment and read more here:

Does AI Help or Hurt Your Company's Strategic Planning? At Second Mountain, we've seen firsthand how AI can streamline decision-making processes and reveal blind spots in strategic planning. However, our experience also shows that while AI excels in offering fresh perspectives, it struggles with nuanced, company-specific insights. So where can AI add value to strategic planning? 1) Divergent Thinking: Generative AI can be used to scan large volumes of industry data and surface external factors and trends that leadership teams might overlook or not have sufficient time and resources to analyze. This can expand the scope of strategic discussions and ground decisions in a more robust fact base. 2) Scenario Analysis: While AI can’t predict future demand or financial performance, it can be highly effective in helping leaders explore a larger number of scenarios, identifying critical uncertainties, potential risks and opportunities. In an increasingly volatile and unpredictable climate, considering a more diverse set of scenarios and deepening the analysis of potential impacts can give businesses a significant competitive advantage. 3) Idea Generation: AI can be used to generate a large number of new ideas quickly. This process can effectively be combined with human input to filter ideas and critically assess their fit with the business before undertaking deeper analysis. Read more on this topic here: How CEOs Are Using Gen AI for Strategic Planning (hbr.org)

Does your Board need a Technology Committee? The Fortune 500 says yes. If your board's tech decisions are too narrowly focused on security and digitization, you're not alone but It’s time to think bigger. A growing number of Fortune 100 and 500 companies have embraced technology committees on their boards, with the objective of not just managing risks—but seizing growth opportunities. Tech committees help companies look beyond immediate concerns to capitalize on advances in fields like climate science, blockchain, and AI where new business models and revenue streams can become a growth engine for the business. For example, Johnson & Johnson’s tech committee ushered in significant innovation in lung cancer treatment by overseeing the integration of cutting-edge imaging, robotics, and surgical technologies across the business. If your board is stuck in a defensive position, a tech committee could be the key to unlocking new growth opportunities. Read more here:

How Venture Capitalists achieve exceptional growth As Alex Rampell of Andreessen Horowitz says, "In the VC world, errors of omission are much more damaging than errors of commission." This simple tenet encapsulates a Venture Capitalist mindset, which has helped companies like Airbnb, Uber, and Zoom skyrocket to success. So what can business leaders learn from VCs to spur innovation and achieve ambitious growth objectives? 1. Embracing Risk and Failure: VCs expect up to 80% of their investments to fail, focusing on the one that will be a home run emphasizing that the fear of missing a transformative opportunity outweighs the fear of failure. 2. Promoting Individual Insights: Successful VCs prioritize individual insights over group consensus. This means creating environments where every team member can share their perspectives, ensuring innovative ideas aren’t drowned out by majority opinion. 3. Encourage Disagreement: VC firms often assign a "devil's advocate" to challenge decisions, ensuring robust debate and diverse viewpoints. This approach prevents groupthink and encourages critical evaluation of all ideas. 4. Prioritize Agility and Speed: Timing is everything in the VC world, which is why successful firms set guaranteed response times and limit the number approvals required internally. These optimizations ensure opportunities are evaluated quickly and keep key team members accountable. How can your team integrate a VC mindset? Tag a colleague who could benefit from this approach. For a deeper dive, read the full HBR article here:

What Business Leaders can Learn from Scientists As business owners and advisors who rely on innovation to drive growth and performance, this recent study drew our attention. Among 759 new ventures examined in Europe, the study finds that firms employing a “scientific method” to innovation generated significantly more revenue than a control group, and importantly, were much more likely to pivot away from commercially unviable ideas early. The scientific method is nothing new – it is a centuries-old discipline of crafting a question, formulating hypothesis, testing it through experiments and deriving conclusions via empirical analysis – thus ensuring that the results are reliable and verifiable. Yet many business decisions continue to be made based on hunches, beliefs and intuition, with limited objective analysis. By thinking like scientists, business leaders can significantly reduce risk, improve robustness of decisions under uncertainty and instill confidence in their leadership with both internal and external stakeholders. Let us know your thoughts on how you approach innovation and read more here: https://lnkd.in/grpcwdy8

Why Bridging the AI Trust Gap should be a Strategic Imperative In a recent HBR article “AI’s Trust Problem”, the author presents a critical challenge organizations face as AI technologies increasingly match human capabilities - the AI trust gap. This gap stems from concerns over AI-driven disinformation, security risks, opaque decision-making processes ("black box"), and inherent biases. These are concerns we hear often in our own work with business leaders, and, if left unaddressed, they can significantly hinder AI adoption even when a robust business case exists. To effectively bridge this trust gap, organizations should prioritize transparency by making AI systems more interpretable and understandable, and adhere to strict ethical standards and regulatory requirements. Moreover, maintaining human oversight in AI decision-making ensures a balanced approach, leveraging both human insight and AI efficiency. Lastly, engaging in continuous AI education and training is crucial. As AI evolves, so must organizational strategies for integrating and overseeing these technologies. By aligning AI deployments with the principles mentioned above, organizations can foster a more trustworthy environment that encourages wider adoption and leverages AI's full potential. How are you tackling the AI trust gap? Share your thoughts to help shape a responsible AI future. Read more here:

Navigating Corporate Transformations As business leaders and advisors, we've witnessed a significant ramp-up in transformation efforts across industries, yet few that have led to lasting impact. This is confirmed by research indicating that at any given moment, over a third of large organizations are navigating through transformative change, while only 12% report achieving their intended outcomes. This trend underscores a critical disconnect between ambition and execution. HBR’s recent article, "Transformations That Work," sheds light on some of the key underlying challenges. However, all is not bleak. Companies like Dell Technologies and Ford Motor Company have demonstrated that with the right approach, transformations can yield phenomenal success. Common to both examples are: · Treating the transformation as a continuous, evolving process rather than a temporary shift · Ensuring active, committed leadership throughout the journey · Rejuvenating organizational energy by recognizing and rewarding success and pacing changes thoughtfully · Driving change from the middle-out by leveraging the insights and capabilities of mid-level managers Read more below and share your experiences (successful or otherwise):

Decarbonizing Shipping (Part 2) Low carbon fuels currently offer the greatest opportunity for GHG emission reduction in the shipping industry. Indeed, in order to meet the 2040 emission reduction target of 40% set by the International Maritime Organization, 64% of GHG reductions need to come from use of alternative fuels. There are multiple technological pathways to produce low-carbon marine fuel, which have different decarbonization potential. Bio-diesel for instance has a “well-to-propeller” GHG reduction potential of only 20-40%, while Bio-LNG offers a 70-80% reduction. Yet one particular fuel solution stands out with as high as 99% decarbonization potential - eMethanol. eMethanol is derived from renewable energy, water and biogenic CO2. It is produced via a “Power-to-Liquid” process, which involves green hydrogen production from renewable electricity, biogenic CO2 capture and conversion into carbon, and subsequent synthesis into fuel. Methanol has a range of advantageous properties, including ambient temperature liquid state, enabling the repurposing of existing infrastructure, including engines and vessels, through relatively efficient retrofits. Fuel producers and shipping operators have begun preparing for a methanol-powered future with the orderbook of methanol-capable ships growing to 1,200 ships by 2030, while over 130 methanol production facilities are in various stages of planning, construction or operation. The International Renewable Energy Agency projects that global methanol production will rise five-fold by 2050, with e-methanol and bio-methanol (an alternative process using organic waste as feedstock) anticipated to constitute 80% of total production. These projections offer hope for a marine shipping future powered by low carbon fuel, if the right incentives remain in place.

Decarbonizing Shipping (Part 1) To meet the GHG reduction commitments set out in the Paris Agreement, urgent action is needed to decarbonize the shipping industry. International shipping is responsible for 90% of marine fuel consumption globally and about 2% of global energy-related CO2 emissions. Fuel consumption in shipping is projected to double by 2030 (from 330M tons to 660M tons), with container ships alone contributing about 30% of global maritime CO2 emissions. Indeed, to comply with the Paris Agreement targets, the International Maritime Organization (IMO) is aiming to reduce total maritime shipping emissions by at least 40% by 2030, and by 70% by 2050 compared to 2008 levels. The industry is pursuing multiple emission reduction strategies to meet this goal including adoption of low-carbon fuel, hybridization and electrification of the marine fleet, fleet optimization, energy efficiency measures, and carbon capture exhaust treatment technologies. Of those, low-carbon fuel stands out as one of the most critical mid- to long-term solutions. Accordingly, governments around the world are rapidly implementing policy mandates and incentives to stimulate low-carbon fuel demand and supply, with Europe and the US leading the way. Ports are also responding to the challenge, by deploying low-carbon fuel distribution infrastructure at scale – for example, methanol fuel is now available at over 100 ports around the world. Still, the supply outlook for low-carbon marine fuel is uncertain and heavily dependent on continued government support, further technological advancements, availability of feedstock and investment decisions by producers (more on this in the next post).