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Submitted URL: https://www.freethink.com/opinion/microsoft-updates-ai))
Effective URL: https://www.freethink.com/opinion/microsoft-updates-ai
Submission: On July 11 via api from BE — Scanned from DE
Effective URL: https://www.freethink.com/opinion/microsoft-updates-ai
Submission: On July 11 via api from BE — Scanned from DE
Form analysis 4 forms found in the DOM
<form data-hs-cf-bound="true">
<div>
<div class="nf-before-form-content"><nf-section>
<div class="nf-form-fields-required">Fields marked with an <span class="ninja-forms-req-symbol">*</span> are required</div>
</nf-section></div>
<div class="nf-form-content "><nf-rows-wrap>
<div class="nf-row">
<nf-cells>
<div class="nf-cell" style="width: 59%;">
<nf-fields><nf-field>
<div id="nf-field-169-container" class="nf-field-container email-container label-hidden ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-169-wrap" class="field-wrap email-wrap" data-field-id="169">
<div class="nf-field-label">
<label for="nf-field-169" id="nf-label-field-169" class=""> Email <span class="ninja-forms-req-symbol">*</span>
</label>
</div>
<div class="nf-field-element">
<input type="email" value="" class="ninja-forms-field nf-element" id="nf-field-169" name="nf-field-169-email" autocomplete="email" aria-invalid="false" aria-describedby="nf-error-169" aria-labelledby="nf-label-field-169"
aria-required="true" required="">
</div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-169" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
<div class="nf-cell" style="width: 41%;">
<nf-fields><nf-field>
<div id="nf-field-170-container" class="nf-field-container submit-container label-above container-submit-inline textbox-container">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-170-wrap" class="field-wrap submit-wrap textbox-wrap" data-field-id="170">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input id="nf-field-170" class="ninja-forms-field nf-element " type="submit" value="Subscribe">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-170" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
</nf-cells>
</div>
<div class="nf-row">
<nf-cells>
<div class="nf-cell" style="width: 14%;">
<nf-fields><nf-field>
<div id="nf-field-171-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-171-wrap" class="field-wrap hidden-wrap" data-field-id="171">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-171" name="nf-field-171" class="ninja-forms-field nf-element" value="859dabc2e6">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-171" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
<div class="nf-cell" style="width: 14%;">
<nf-fields><nf-field>
<div id="nf-field-172-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-172-wrap" class="field-wrap hidden-wrap" data-field-id="172">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-172" name="nf-field-172" class="ninja-forms-field nf-element" value="subscribe">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-172" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
<div class="nf-cell" style="width: 14%;">
<nf-fields><nf-field>
<div id="nf-field-173-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-173-wrap" class="field-wrap hidden-wrap" data-field-id="173">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-173" name="nf-field-173" class="ninja-forms-field nf-element" value="7ba12a7f80">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-173" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
<div class="nf-cell" style="width: 14%;">
<nf-fields><nf-field>
<div id="nf-field-174-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-174-wrap" class="field-wrap hidden-wrap" data-field-id="174">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-174" name="nf-field-174" class="ninja-forms-field nf-element" value="Freethink.com signup form: Inline Newsletter Prompt">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-174" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
<div class="nf-cell" style="width: 14%;">
<nf-fields><nf-field>
<div id="nf-field-175-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-175-wrap" class="field-wrap hidden-wrap" data-field-id="175">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-175" name="nf-field-175" class="ninja-forms-field nf-element" value="https://www.freethink.com/opinion/microsoft-updates-ai">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-175" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
<div class="nf-cell" style="width: 14%;">
<nf-fields><nf-field>
<div id="nf-field-176-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-176-wrap" class="field-wrap hidden-wrap" data-field-id="176">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-176" name="nf-field-176" class="ninja-forms-field nf-element" value="0">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-176" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
<div class="nf-cell" style="width: 14%;">
<nf-fields><nf-field>
<div id="nf-field-177-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-177-wrap" class="field-wrap hidden-wrap" data-field-id="177">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-177" name="nf-field-177" class="ninja-forms-field nf-element" value="9">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-177" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
</nf-cells>
</div>
</nf-rows-wrap></div>
<div class="nf-after-form-content"><nf-section>
<div id="nf-form-errors-23" class="nf-form-errors" role="alert"><nf-errors></nf-errors></div>
<div class="nf-form-hp"><nf-section>
<label id="nf-label-field-hp-23" for="nf-field-hp-23" aria-hidden="true"> If you are a human seeing this field, please leave it empty. <input id="nf-field-hp-23" name="nf-field-hp" class="nf-element nf-field-hp" type="text" value=""
aria-labelledby="nf-label-field-hp-23">
</label>
</nf-section></div>
</nf-section></div>
</div>
</form><form data-hs-cf-bound="true">
<div>
<div class="nf-before-form-content"><nf-section>
<div class="nf-form-fields-required">Fields marked with an <span class="ninja-forms-req-symbol">*</span> are required</div>
</nf-section></div>
<div class="nf-form-content "><nf-rows-wrap>
<div class="nf-row">
<nf-cells>
<div class="nf-cell" style="width: 70%;">
<nf-fields><nf-field>
<div id="nf-field-30-container" class="nf-field-container email-container label-hidden ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-30-wrap" class="field-wrap email-wrap" data-field-id="30">
<div class="nf-field-label">
<label for="nf-field-30" id="nf-label-field-30" class=""> Email <span class="ninja-forms-req-symbol">*</span>
</label>
</div>
<div class="nf-field-element">
<input type="email" value="" class="ninja-forms-field nf-element" id="nf-field-30" name="email" autocomplete="email" placeholder="Enter your email" aria-invalid="false" aria-describedby="nf-error-30"
aria-labelledby="nf-label-field-30" aria-required="true" required="">
</div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-30" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
<div class="nf-cell" style="width: 30%;">
<nf-fields><nf-field>
<div id="nf-field-31-container" class="nf-field-container submit-container label-above container-submit-inline textbox-container">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-31-wrap" class="field-wrap submit-wrap textbox-wrap" data-field-id="31">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input id="nf-field-31" class="ninja-forms-field nf-element " type="submit" value="Subscribe">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-31" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
</nf-cells>
</div>
<div class="nf-row">
<nf-cells>
<div class="nf-cell" style="width: 100%;">
<nf-fields><nf-field>
<div id="nf-field-32-container" class="nf-field-container listcheckbox-container label-hidden list-container">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-32-wrap" class="field-wrap listcheckbox-wrap list-wrap list-checkbox-wrap" data-field-id="32">
<div class="nf-field-label">
<span id="nf-label-field-32" class="nf-label-span "> Checkbox List </span>
</div>
<div class="nf-field-element">
<ul aria-describedby="nf-error-32">
<li>
<input type="checkbox" id="nf-field-32-0" name="nf-field-32" class="ninja-forms-field nf-element nf-checked" value="freethink-weekly" checked="checked" aria-labelledby="nf-label-field-32-0">
<label for="nf-field-32-0" id="nf-label-field-32-0" class="nf-checked-label">Freethink Weekly</label>
</li>
<li>
<input type="checkbox" id="nf-field-32-1" name="nf-field-32" class="ninja-forms-field nf-element " value="future_explored" aria-labelledby="nf-label-field-32-1">
<label for="nf-field-32-1" id="nf-label-field-32-1" class="">Future Explored</label>
</li>
</ul>
</div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-32" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
</nf-cells>
</div>
<div class="nf-row">
<nf-cells>
<div class="nf-cell" style="width: 33%;">
<nf-fields><nf-field>
<div id="nf-field-44-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-44-wrap" class="field-wrap hidden-wrap" data-field-id="44">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-44" name="nf-field-44" class="ninja-forms-field nf-element" value="859dabc2e6">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-44" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
<div class="nf-cell" style="width: 33%;">
<nf-fields><nf-field>
<div id="nf-field-45-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-45-wrap" class="field-wrap hidden-wrap" data-field-id="45">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-45" name="nf-field-45" class="ninja-forms-field nf-element" value="subscribe">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-45" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
<div class="nf-cell" style="width: 33%;">
<nf-fields><nf-field>
<div id="nf-field-135-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-135-wrap" class="field-wrap hidden-wrap" data-field-id="135">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-135" name="nf-field-135" class="ninja-forms-field nf-element" value="Freethink.com signup form: Subscribe module with checkboxes">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-135" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
</nf-cells>
</div>
<div class="nf-row">
<nf-cells>
<div class="nf-cell" style="width: 33%;">
<nf-fields><nf-field>
<div id="nf-field-111-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-111-wrap" class="field-wrap hidden-wrap" data-field-id="111">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-111" name="nf-field-111" class="ninja-forms-field nf-element" value="0">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-111" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
<div class="nf-cell" style="width: 33%;">
<nf-fields><nf-field>
<div id="nf-field-112-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-112-wrap" class="field-wrap hidden-wrap" data-field-id="112">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-112" name="nf-field-112" class="ninja-forms-field nf-element" value="6">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-112" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
<div class="nf-cell" style="width: 33%;">
<nf-fields><nf-field>
<div id="nf-field-131-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-131-wrap" class="field-wrap hidden-wrap" data-field-id="131">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-131" name="nf-field-131" class="ninja-forms-field nf-element" value="https://www.freethink.com/opinion/microsoft-updates-ai">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-131" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
</nf-cells>
</div>
</nf-rows-wrap></div>
<div class="nf-after-form-content"><nf-section>
<div id="nf-form-errors-9" class="nf-form-errors" role="alert"><nf-errors></nf-errors></div>
<div class="nf-form-hp"><nf-section>
<label id="nf-label-field-hp-9" for="nf-field-hp-9" aria-hidden="true"> If you are a human seeing this field, please leave it empty. <input id="nf-field-hp-9" name="nf-field-hp" class="nf-element nf-field-hp" type="text" value=""
aria-labelledby="nf-label-field-hp-9">
</label>
</nf-section></div>
</nf-section></div>
</div>
</form><form data-hs-cf-bound="true">
<div>
<div class="nf-before-form-content"><nf-section>
<div class="nf-form-fields-required">Fields marked with an <span class="ninja-forms-req-symbol">*</span> are required</div>
</nf-section></div>
<div class="nf-form-content "><nf-rows-wrap>
<div class="nf-row">
<nf-cells>
<div class="nf-cell" style="width: 59%;">
<nf-fields><nf-field>
<div id="nf-field-74-container" class="nf-field-container email-container label-hidden ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-74-wrap" class="field-wrap email-wrap" data-field-id="74">
<div class="nf-field-label">
<label for="nf-field-74" id="nf-label-field-74" class=""> Email <span class="ninja-forms-req-symbol">*</span>
</label>
</div>
<div class="nf-field-element">
<input type="email" value="" class="ninja-forms-field nf-element" id="nf-field-74" name="email" autocomplete="email" aria-invalid="false" aria-describedby="nf-error-74" aria-labelledby="nf-label-field-74"
aria-required="true" required="">
</div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-74" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
<div class="nf-cell" style="width: 41%;">
<nf-fields><nf-field>
<div id="nf-field-75-container" class="nf-field-container submit-container label-above container-submit-inline container-submit-arrow textbox-container">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-75-wrap" class="field-wrap submit-wrap textbox-wrap" data-field-id="75">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input id="nf-field-75" class="ninja-forms-field nf-element " type="submit" value="Subscribe">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-75" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
</nf-cells>
</div>
<div class="nf-row">
<nf-cells>
<div class="nf-cell" style="width: 20%;">
<nf-fields><nf-field>
<div id="nf-field-76-container" class="nf-field-container hidden-container label-above ">
<div class="nf-before-field"><nf-section>
</nf-section></div>
<div class="nf-field">
<div id="nf-field-76-wrap" class="field-wrap hidden-wrap" data-field-id="76">
<div class="nf-field-label"></div>
<div class="nf-field-element">
<input type="hidden" id="nf-field-76" name="nf-field-76" class="ninja-forms-field nf-element" value="859dabc2e6">
</div>
<div class="nf-error-wrap"></div>
</div>
</div>
<div class="nf-after-field"><nf-section>
<div class="nf-input-limit"></div>
<div id="nf-error-76" class="nf-error-wrap nf-error" role="alert"></div>
</nf-section></div>
</div>
</nf-field></nf-fields>
</div>
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MICROSOFT’S “PARALLEL BETS” STRATEGY WON THE PC WARS. WILL IT WORK FOR AI?
Will Microsoft's old "portfolio" strategy be the winning formula again for AI?
By Matthew Ball
June 16, 2024
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Computer Science
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The first IBM Personal Computer debuted in August 1981. It was also the first
computer to use Microsoft’s MS-DOS operating system (technically, a nearly
identical copy known as IBM PC DOS, also known as PC DOS or IBM DOS). At the
time, Unix held a roughly 55% share of the market for operating systems running
on personally owned devices; the rest was split between Xerox’s Alto (launched
in 1973 for proto-PCs), CP/M (1974, for microcomputers), TRSDOS (1977, also
microcomputers), Commodore KERNEL (1977, home computers), Atari DOS (1979, home
computers), Apple’s DOS (1978, microcomputers), and SOS (1980, microcomputers).
By 1985, MS-DOS had amassed more than 50% market share. By 1989, it held just
under 90%.
Despite Microsoft’s success throughout the 1980s, the company’s founder and CEO,
Bill Gates, suspected that DOS was approaching its end. Each year, there were
more people who owned a PC, even more who used one as part of work, and leisure
usage was growing rapidly, as was the number of PC developers. These changes
were great for the market, and Microsoft was a primary beneficiary of this
market, but these changes changed the market, too, which could jeopardize
Microsoft’s position. There was also growing evidence of competitive and
structural changes in the marketplace. Apple’s line of graphical user interfaces
(GUI) computers, which debuted with the 1983 Lisa PC, were increasingly popular
and served as an obvious contrast to the command-line interfaces of DOS. In
1984, MIT had kicked off a project to build the Window System for Unix and
Unix-like OS. In 1985, IBM had begun development of OS/2, which Microsoft signed
on to co-develop, but in contrast to IBM DOS, OS/2 was spearheaded by IBM, not
Microsoft, and designed to sell IBM PCs and hardware, not foster the PC hardware
ecosystem or Microsoft’s Windows. In 1986, a group that spanned Sun, AT&T, and
Xerox (which had pioneered the GUI interface) began working on a GUI
specification of Unix (OPEN LOOK, which later progressed into OpenWindows) that
would make the OS more consumer-friendly. In 1988, a similar collective
including IBM, HP, Compaq, and DEC formed to build Digital Unix (a.k.a. Tru64
UNIX).
Taken together, it was not hard to predict that platform-level changes might
soon occur. If so, secondary and tertiary changes were inevitable, too. In other
words, Microsoft faced not just the prospect of new competitors, some of which
were current licensors, but also potential disruption in its OS licensing
business model, all of which threatened its growth, investment, and product
strategy and might alter industry profit pools as well.
To manage this uncertainty, Microsoft undertook a portfolio of bets throughout
the 1980s and early 1990s that were often competitive with one another (or had
competing premises) but collectively replicated the diversity, unpredictability,
and dynamism of the market at large, thereby maximizing Microsoft’s odds of
success in any future state. These bets were roughly as follows:
1. Continue development of MS-DOS;
2. Collaborate with the many companies working on UNIX, namely through the
ongoing development of its Xenix version of Unix (1980–89);
3. Commence major investment(s) in Windows, a GUI OS (development started 1983,
with Windows 1.0 shipping in 1985 and 3.0 in 1991)
4. Form partnership with IBM to develop OS/2 (1985–);
5. Purchase 20% stake in Santa Cruz Operation, the largest seller of Unix
systems on PCs (1989); and
6. Develop suite of applications (namely Microsoft Office, 1990–) that could
operate across operating systems which Microsoft might have no ownership (or
influence) over
While Microsoft had many bets, it still had its preferred one: Winning the
personal computing market, via a licensed OS. They wanted the OS that won out to
be Windows. As Microsoft had been successful with this core endeavor, it also
could have proceeded with that sole bet.
Yet a live-or-die bet wasn’t necessary, and indeed, some of those failed bets
failed precisely because Windows 3.0, which launched in 1990, was so successful.
One such example is OS/2. This jointly developed OS was always challenged by the
dueling priorities of IBM and Microsoft, but following the breakout of Windows
3.0, the partnership between the two companies became impossible to sustain. IBM
took sole ownership in 1992 (the final release was in 2001). By 1993, the Unix
ecosystem had determined that a fully unified Unix was needed to combat Windows,
prompting the Common Open Software Environment initiative, or COSE (founded by
the Santa Cruz Operation, Univel, Unix Systems Laboratories, Sun, HP, and IBM).
These hopes were dashed by the blockbuster Windows 95, which Santa Cruz, HP, and
IBM had little choice but to support, especially as rivals such as Dell picked
up market share. It’s likely that Microsoft also had picked up extensive
knowledge from its various bets, such as the rationale behind various technical
and interface-related decisions across O/2, Unix, and so forth, and used this
knowledge to strengthen relationships with key industry partners, most notably
those who manufactured and distributed PCs.
Even as Windows secured the market in the early 1990s, Microsoft remained
paranoid about the right product offering. Prior to the launch of Windows 95,
the company released Microsoft Bob, which was intended to be an even more
consumer-friendly GUI for novice computer users, but it failed miserably and was
quickly killed the following year.
In 1995, Gates wrote his famous “Internet Tidal Wave memo,” in which he argued
that the Internet was not just a critical new frontier for Microsoft, but one
that might empower the company’s OS competitors or even displace the role of the
OS altogether:
I assign the Internet the highest level of importance… I want to make clear that
our focus on the Internet is crucial to every part of our business… The Internet
is the most important single development to come along since the IBM PC was
introduced in 1981. It is even more important than the arrival of the graphical
user interface (GUI). The PC analogy is apt for many reasons. The PC wasn’t
perfect. Aspects of the PC were arbitrary or even poor. However a phenomena grew
up around the IBM PC that made it a key element of everything that would happen
for the next 15 years. Companies that tried to fight the PC standard often had
good reasons for doing so but they failed because the phenomena overcame any
weaknesses that resisters identified… IBM [also] includes Internet connection
through its network in OS/2 and promotes that as a key feature. Some competitors
have a much deeper involvement in the Internet than Microsoft. All UNIX vendors
are benefiting from the Internet since the default server is still a UNIX box
and not Windows… Sun has exploited this quite effectively… and [is] very
involved in evolving the Internet to stay away from Microsoft… [Moreover], a new
competitor “born” on the Internet is Netscape.
Gates’s memo led to a flood of investment in the company’s digital efforts, from
Internet Explorer (1995) to the MSN portal and search engine (1995), the $400
million acquisition of Hotmail (1997; $760MM in 2023 dollars), Messenger (1999);
the list goes on. The company also remained diversified in its traditional OS
bets, purchasing a 5% stake in Apple in 1997 for $150MM; the deal also involved
Apple making Internet Explorer the Mac’s default browser).
The waves roll on
As we review Microsoft’s parallel bets in 2023, a few takeaways are clear.
Eventually, Microsoft lost most of the market in consumer internet services,
such as search and web portals, email, messaging, and identity. Several of these
markets are more lucrative than operating system licensing. While Microsoft
successfully boxed out Unix, Linux, an open-source OS that launched in 1991, has
thrived (though differently than its creator once imagined). Today, more than
95% of the 150–200 million operating servers run Linux, and Android, the most
used operating system in history, is also based on the OS. Microsoft’s
modern-day strength in productivity software and other horizontal/cross-platform
technology, such as Azure, has also enabled the company to thrive even after its
OS was displaced—although this displacement was not at the hands of PC-related
competitors in the ’80s or ’90s but rather two mobile competitors of the 2000s
and 2010s, iOS and Android. It also turned out that mobile computers would
become more than two-thirds of the market, capping Microsoft’s share of the
total OS market to a third at best.
Crucially, Microsoft’s displacement in the mobile market resulted from
undiversified thesis errors. In his infamous January 2007 CNBC interview,
Microsoft CEO Steve Ballmer laughs at the prospects of the just-announced
iPhone, citing its high price and lack of a keyboard. Popular recollections of
this interview typically ends here, but Ballmer’s full report is more generous,
saying “it may sell very well,” after which he explains why Microsoft’s strategy
is superior. It was the foundational theses of Microsoft’s mobile strategy,
which rested on concepts that were shared by most of the proto-smartphone, most
notably BlackBerry and Palm, that doomed the company’s efforts. Smartphones
should be $100–$200, not $500 or more; they should focus on business users not
consumers; they should feature a keyboard; data usage should be minimized so as
to protect scarce network bandwidth; batteries should last for days, not hours;
fall damage should be minimal, rather than device-wrecking. Though these bets
were likely “right” in the late 1990s and early 2000s, they proved wrong over
time.
Microsoft also continued to apply its PC-era business model, rather than
diversify or really even test other hypotheses. When MS-DOS debuted, there were
already a handful of computer manufacturers, most of which supported multiple
operating systems (and some consumers chose to install their own at a later
time, anyway). Microsoft grew its share of the market not by competing with
these OEMs, by producing its own PC, or by partnering exclusively with IBM but
by licensing Windows for $50–$100 to any or all manufacturers. For the most
part, Apple limited its OS to its own hardware, the first iteration of which
debuted in 1976 (five years before the IBM PC or MS-DOS). Some in the tech
community believed the company produced better “PCs,” but its vertically
integrated approach led to higher prices and constrained distribution, and the
company struggled mightily to overcome the large and incredibly developed PC and
Windows ecosystem. Microsoft, of course, knew that there were potential benefits
from producing its own first-party hardware, but doing so would mean competing
with its many partners, most of which could (and would) renew their own OS
investments in response, or worse, adopt a would-be rival to Windows, such as
Unix.
When it came to the early “smartphones” of the late 1990s and early 2000s,
Microsoft’s approach (Windows Compact and Windows Mobile) was similar to its PC
strategy, though it charged a more modest $10–$25 per device. The challenge here
was that mobile computers were comparatively harder to build than PCs due to the
vastly different constraints on size, power usage, heat generation, and the
like. But in the mobile form factor, which was so nascent that fewer than 60MM
devices had been sold over fifteen years, Apple’s vertically integrated approach
led to a substantially better device. Unlike Microsoft, Apple built a truly
mobile-native OS, rather than porting over most of the design principles of its
Mac OS (Windows Mobile literally had a taskbar and Start menu button). The
result was a device that totally outclassed Windows-based smartphones and
effectively downgraded the other would-be smartphones to proto-smartphones.
Independent smartphone manufacturers rushed to catch-up to Apple’s hardware
designs, and were somewhat successful, while also differentiating on other
designed-related features (e.g., offering better cameras or larger screens).
However, these OEMs fell well short on the OS and ecosystem side, thereby
preserving the PC-era opportunity for an independent OS provider. Here however,
Microsoft’s Windows Mobile lost to Google’s Android, which was not just a more
modern OS (mobile-native, touch- and consumer-focused, and so on) but free to
device makers. In fact, Google offered OEMs (and wireless carriers) a share of
Android-related search and Google Play app store revenues. In effect, the OS
business model had inverted from direct monetization (Microsoft’s bread and
butter) to the sale of profitable hardware (Apple’s main business line and one
in which Microsoft lacked any business at all) and software/services bundles
(where Google thrived and Apple was rapidly growing). By the time Microsoft
pivoted, launching its free-to-license Windows Phone OS in 2010 and an exclusive
partnership with Nokia a year later (Microsoft ended up buying it in 2014), it
was too late. Had Microsoft been earlier in its shift, or just partly wrong
about smartphones when the iPhone launched, its mobile OS might have survived.
(Incidentally, MacOS, iOS, and Android are all UNIX-based).
Smart, confident but seemingly confused
I think a lot about parallel bets when it comes to AI, a field with such
obvious, diverse, and immense potential that also faces great uncertainty around
technology, business model, timing, cost, and implementation. Few debate that
the emergence of the “AI Era” could produce a new tech titan, just as few debate
that it could crush (or outright destroy) one that today seems indefatigable.
Perhaps it is no surprise, then, that Microsoft is deploying a wide array of
strategies to manage various uncertainties, many of which compete with one
another or otherwise seem to conflict with the company’s overall AI strategies
and investments.
Though Microsoft Research was not formally established until 1991, Microsoft’s
market-leading investments into AI began not long after Gates founded the
company in 1975. The results of these investments were similarly prompt.
Microsoft’s first text processor, Word 1.0 for DOS, launched in 1983 and
differentiated from market leaders and other would-be competitors through the
integration of “Spell Check.” At the time, MicroPro’s WordStar required users to
launch a separate spell-checking program, while WordPerfect lacked the
capability altogether. Though “Spell Check” falls short of modern definitions of
AI (more on this later), Microsoft slowly grew from basic probability matching
(e.g., when a word didn’t match a known word in Microsoft Word’s database, its
letters and their sequence would be checked for the best fit against that
database) to a more complex system that could recognize words that were spelled
correctly but didn’t suit a sentence (e.g., homophones), recommendations that
were based on the rest of a sentence rather than just the word itself (e.g.,
“lichense” might be closer to “lichens,” but “license” better fits the sentence
overall) and then eventually learning from a specific user’s errors as well as
those of all users, too. Along the way, Microsoft also added grammar check, too,
which started with basic corrections (e.g., tense and number agreement) before
moving to stylistic suggestions such as clarity and concision. Microsoft’s
Clippy, which debuted in 1996, was not a popular product, but it was a pioneer
in digital assistants and using Natural Language Processing to infer what users
were attempting to do in order to provide advance recommendations. Though Clippy
was removed from Microsoft’s Office Suite in 2007, turned off by default in
Office XP, and ignored or disabled by users earlier still, the very investments
behind Clippy powered Microsoft’s far more successful features, such as
predictive data entry (e.g., drag and fill), inconsistent formula warnings), and
recommended analysis in Excel, as well as formatting and template suggestions in
Word, Mail, and PowerPoint, and later, Microsoft Dynamics CRM.
By the 2020s, it’s likely that no company had invested more in AI than
Microsoft. Still, the company’s executives had begun to fear that it was falling
behind—and that just as was the case in smartphones, competitors might quickly
surge beyond them. In a series of 2019 emails (which were released in 2024 as
part of the Department of Justice’s antitrust case against Google), Microsoft’s
CTO (Kevin Scott), CFO (Amy Hood), CEO (Satya Nadella), and founder (Gates)
express in detail this very fear. Scott, who initiates the thread, writes that
he had previously been “highly dismissive of [competitor’s] efforts. . .. [but]
that was a mistake. . . [I am] very, very worried.” Scott’s epiphany was
prompted by work to better understand how Google was bringing together its
largely disconnected and sometimes rivalrous internal AI initiatives—i.e., its
parallel bets, of which the most notable are Google Brain and Deep Mind—as well
as the advances of OpenAI, which had been founded only four years earlier and
had roughly 100 employees.
“The thing that’s interesting about what [they] are doing is the scale of their
ambition, and how that ambition is driving everywhere from datacenter design to
compute silicon to networks and distributed systems architectures to numerical
optimizers, compilers, programming frameworks, and the high-level abstractions
that model developers have at their disposal,” wrote Scott. “When they took all
of the infrastructure that they had built to build [natural language processing]
models that we couldn’t easily replicate, I started to take things more
seriously. And as I dug in to try to understand where all of the capability gaps
were between Google and us for model training, I got very, very worried. We have
very smart [machine learning] people in Bing, in the vision team, and in the
speech team. But the core deep learning teams within each of these bigger teams
are very small, and their ambitions have also been constrained…. and we are
multiple years behind the competition.”
In response to Scott’s email, Nadella replied, “Very good email that explains
why I want us to do this [deal] and also why we will then ensure our infra folks
execute.” The deal in question was a $1 billion investment into OpenAI, which
had privately released its GPT-1 model only a year earlier (2018) that was based
on the transformer model that had been initially proposed by Google Brain and
Google Research barely a year before that (2017). Before shifting to
transformers, OpenAI had invested in many alternative approaches to AI,
including evolutionary algorithms and Long Short-Term Memory (LSTM). As part of
its investment, Microsoft also granted OpenAI with free access to its Bing
search database, which the startup then used to train and improve its generative
pre-trained transformer (GPT) models.
Between 2020 and 2022, Microsoft invested another $2 billion into OpenAI while
continuing to scale its internal teams and outside investments. In 2021,
Microsoft announced its second-largest acquisition ever, spending $20 billion to
acquire Nuance Communications, a thirty-year-old AI pioneer with particular
strength in natural language processing and speech recognition (Nuance’s engine
was reportedly at the foundation of Apple’s Siri) as well as healthcare-specific
AI.
Despite Microsoft’s acquisition of Nuance, internal investments in AI, and its
chief officer’s hopes that its OpenAI investment would help inspire Microsoft’s
internal groups to scale up, catch up, and “execute,” Nadella remained
dissatisfied. By December 2022, Nadella had reportedly concluded that the models
developed by OpenAI’s 250-person team (GPT-3.5 and a pre-release version of
GPT-4) had surpassed those of Microsoft Research so thoroughly that he asked
division’s chief, “Why do we have Microsoft Research at all?” Barely a month
later, Microsoft invested $10 billion into OpenAI in exchange for a (de facto)
49% ownership stake in the company as well as 75% of profits until that $10
billion is recouped (and 49% thereafter, to an unknown cap), in exchange for
broad rights to incorporate its technologies into Microsoft’s own offerings. And
then Microsoft quickly went to work.
Only two weeks after Microsoft’s OpenAI investment, the companies announced the
“New Bing,” with Microsoft proudly boasting it was powered by OpenAI’s GPT-4 and
even co-branding its search engine as “BingChat with ChatGPT-4” or even “Bing
Powered by ChatGPT’s GPT-4.” That same month Microsoft announced Copilot, a
GPT-powered chatbot that would be integrated across its Office Suite and other
tools, including Microsoft 365 and, most importantly, GitHub. Three months
later, Microsoft announced the end of Cortana, the virtual assistant launched by
the company in 2014 following the release of Apple’s Siri (2011), Google’s
Now/Assistant (2012), and Alexa (a month earlier in 2014).
Given Microsoft’s highly marketed deployment of OpenAI-branded products as well
as its ongoing deal to provide search data to OpenAI and its near-majority stake
in the company and even greater rights to OpenAI’s foreseeable profits, one
might assume that Microsoft had picked its “One Big Bet.” Not so! Although
Microsoft makes extensive use of ChatGPT’s branding, its products technically
run on Microsoft’s own Prometheus model. Prometheus is built on GPT-4’s
foundational large language model but was subsequently fine-tuned by Microsoft
using both supervised and reinforcement learning techniques. As such, Microsoft
not only owns the end-user of when they use “Powered by GPT” products, as well
as their related engagement/query data, they also operate part of the model
itself and can, if they so choose, begin to substitute OpenAI’s products out
over time. Indeed, The Information reports that this is Microsoft’s express
goal—starting with a model that “may not perform as well . . . [but] costs far
less to operate,” as Microsoft covers all of the compute-related costs from
resolving a GPT-powered query or prompt. Microsoft’s GPT-powered (and often
branded) products are also in obvious competition with OpenAI’s own
consumer-facing GPT-3.5, GPT-4, GPT-4o, Codex, et al.; a user who picks the
latter to resolve a specific query or answer a specific prompt has no need for
Microsoft to do the same.
When Meta open-sourced its Llama model—a move that threatened OpenAI’s business
model and thus also Microsoft’s potential royalties from said business
model—Meta did so in headline partnership with Microsoft, which Meta dubbed its
“preferred partner.” In February 2024, Microsoft invested in Mistral, another
transformed-based AI leader, at a $2.5B valuation. Notably, Mistral was founded
two months after Microsoft struck its groundbreaking $10B OpenAI investment;
notably, like Meta, Mistral produces open-source models. A month later,
Microsoft struck a $650-million deal with Inflection AI, a $4 billion-valued
startup founded by Reid Hoffman, a co-founder of LinkedIn, and Mustafa Suleyman,
who had previously co-founded DeepMind, acquired by Google in 2014. As part of
this deal, Microsoft not only licensed most of Inflection’s models, it also took
on most of Inflection AI’s staff, while Suleyman became CEO of the newly
announced Microsoft AI, which reported directly to Nadella. Karén Simonyan,
another co-founder of Inflection AI, also joined Microsoft as “Chief Scientist
of Microsoft AI.” There are some reports that Microsoft has made its Bing search
database available to other AI startups and companies interested in building
their own foundation models.
Microsoft’s relentless diversification away from OpenAI is a easier to
understand when placed in the context of OpenAI’s own efforts. Take search. The
day Microsoft launched the New Bing, Nadella boasted to The Verge, “I hope that,
with our innovation, [Google] will definitely want to come out and show that
they can dance. And I want people to know that we made them dance.” The company
also estimated that every 1% in market share that New Bing might acquire would
be worth as much as $2 billion annually. Yet only a month after the release of
New Bing, DuckDuckGo, the 6th-most-used search engine globally with 0.55%
(compared to Bing’s 2nd place position with 2.75%), announced its
ChatGPT-powered DuckAssist service. According to reports, OpenAI has recently
struck a deal to power a new version of Apple’s Siri (which, incidentally,
defaults to Google for traditional search!). OpenAI is also rumored to be
developing its own search engine, and recently hired Shivakumar Venkataraman, a
21-year Google veteran who ran Google Search’s ad business. Microsoft has touted
its GPT-powered Copilot integration in its Dynamics 365 CRM offering, yet that
same product’s chief competitor, Salesforce, has also licensed ChatGPT in order
to build its similar EinsteinGPT feature. In May 2024, OpenAI announced it had
developed a desktop application for its suite of AI products—but it was
Mac-only, with the company’s CTO blandly maintaining that the company is “just
prioritizing where our users are.”
The most threatening limits of Microsoft’s influence over and benefits from
OpenAI are deeper beneath the surface. Even with its enormous stake in the
company, Microsoft was not notified that OpenAI’s CEO was under (quasi?)
investigation by its board for a lack of candor— (apparently?) —nor that he was
due to be terminated. And while Microsoft has broad rights to integrate OpenAI’s
products into their own, these rights do not extend to any systems the OpenAI
board determines has “attained” artificial general intelligence (AGI), which
would also make the “legacy” systems for which Microsoft still had rights rather
obsolete.
And fundamentally, Microsoft’s competition with OpenAI’s direct-to-consumer
products like ChatGPT or its integration into third-party services, such as
Einstein GPT, threatens more than just the odd point of market share here and
there. Microsoft understands that multimodal LLMs—which ChatGPT defines as “AI
systems capable of understanding and generating content across different types
of data, such as text, images, and audio, enabling them to process and integrate
information from multiple sensory modalities”—can replace existing software
interfaces entirely, rather than just diverting various queries, prompts, or
functions or otherwise sitting alongside existing interfaces. And the interface
layer tends to be the most profitable part of the digital value chain. It’s for
this reason that Microsoft chooses to control the “reinforcement learning from
human feedback” data in Prometheus, rather than relay it to OpenAI (or other
partners) and/or use ChatGPT directly. It’s also why AI is now being shoved into
anything that users can touch/see/use (at Microsoft and elsewhere). The crucial
importance of RLHF is what makes AI zero-sum—and why a growing many believe
Microsoft’s OpenAI investment/partnership to be both strategically brilliant and
bound to erode (if not collapse outright).
Parallel lessons
Parallel bet strategies have several core advantages, from increasing corporate
optionality (especially when it comes to acquisitions) to covering strategic
bases, maximizing learnings, and (potentially) neutralizing (or at least
moderating the risk of) any potential competitors. But there are costs. More
money tends to be spent, but each bet tends to receive less funding than they
might have under a more focused approach, which can constrain the would-be
“winners.” Overseeing many bets can also lead to mixed signals on what is (and
is not) the “right bet” and harm internal morale. Few teams feel good about
competing against their colleagues. Worse still, these teams typically hate when
their corporate parent funds external competitors that, with a little more
funding and support, they might been able to beat but now threaten to put them
out of a job.
In the context of Microsoft, it’s not hard to “what if” its parallel bets
strategy. What if Microsoft had bought 49% of OpenAI earlier, if not the entire
company, rather than hedging on the startup and other internal investments? Or
at least formed a (more exclusive) commercial deal around the time of GPT-2 or
GPT-3, back when OpenAI’s lead was more modest and its leverage similarly
smaller, too? Perhaps with more money and attention and a longer competitive
runway, Microsoft’s internal efforts might have surpassed those of OpenAI,
rather than helped to make it the market leader. (Incidentally, the Motorola
ROKR, also known as the “iTunes Phone,” was a parallel bet of both Motorola and
Apple but was a failure for the former and helped drive and accelerate the
latter’s iPhone initiative, which then helped destroy Motorola’s market share.)
There are, however, a number of timing-related flaws with those “what ifs.” The
sudden maturation of transformer models was not predictable, for example, nor
was the fact that OpenAI, rather than Cohere or Anthropic or Hugging Face, would
be the market leader. OpenAI might not remain the market leader, and transformer
models may eventually be replaced, too. And though powerful, transformer models
address only some AI use cases.
Microsoft’s approach also contrasts with that of Amazon. Beginning in 2012, the
company bet that the era of the “smart assistant” had begun. Amazon was not
alone in that bet; Apple had shipped Siri with iOS a year earlier, with Google
deploying its Google Now eight months later. Unlike Apple and Google, Amazon
lacked a smartphone, and so the company instead focused on building a suite of
lightweight devices that could be placed throughout the home and office and
called upon at any moment. By 2016, the Alexa device was shipping tens of
millions of units per year, with Amazon founder and CEO Jeff Bezos speaking
openly of his hope that it would become the fourth pillar of the company,
sitting alongside Amazon Marketplace, Amazon Prime, and Amazon Web Services. By
2020, lifetime device sales were in the hundreds of millions, an order of
magnitude higher than those of Google’s Home/Nest devices, with Apple’s
competitor, the HomePod, flunking out of the market. Amazon also began to unlock
the Alexa software from its first-party hardware, deploying it widely in
vehicles, such as those of Ford and Toyota, and even quasi-rivalrous speaker
systems like Sonos.
Though Alexa’s topline performance suggested it was or at least could be the
winner in “consumer AI,” the platform struggled to overcome core engagement
problems. For example, the average consumer didn’t use Alexa very much—and when
they did, queries were rudimentary (“What time is it?,” “Set an alarm for
2:05PM,” “Will it rain today?,” “Play Dua Lipa,” etc.). Few developers had
produced Alexa apps (“Skills”), and even fewer integrated the platform (or its
device) into their products. The result was a device that sold well but
generated only abstract value to Amazon—and, if the reports are to be believed,
tens of billions in losses. When Amazon began its layoffs in 2022, the Alexa
division, which had an estimated 10,000–15,000 employees, was disproportionately
affected. As transformer models took off toward the end of 2022, the optics of
Amazon’s investments worsened as it became clear its costly bet had been
wrong—at least for now. The Information has since reported that Amazon had
planned to launch its own transformer LLM (“Bedrock”) in November of 2022 but
shelved it after seeing ChatGPT and also that the company had passed on various
product partnership and equity proposals from OpenAI, Anthropic, and Cohere
dating as far back to 2018.
Throughout 2023, Amazon has reset its AI strategy. Dave Limp, the head of
Amazon’s Alexa division, announced he was “retiring.” Amazon repurposed its
Bedrock name to launch an AWS marketplace for third-party AI solutions and
models, with the goal that AWS customers would run these products on AWS
compute. In October 2023, Amazon announced a $1.25 billion investment in
Anthropic, which had raised several hundred million dollars from Google across
two fundraising rounds in January and May of that same year. As part of its
investment, Amazon announced a “framework” through which it might invest another
$2.75 billion into the company. A week later, Google responded with a $2 billion
follow-on investment in Anthropic. Unsurprisingly, Amazon continues to develop
its own foundational models, too.
Breakfast and dinner
Parallel bets strategies are best suited to (1) cash-rich companies . . . that
are (2) pursuing “must-win” categories” . . . in which (3) their assets and
strategies are a good fit . . . but (4) may not be configured correctly . . .
and (5) there is a high rate of change . . . and (6) many uncertainties . . .
and (7) many players . . . with (8) progress often occurring out of sight.
Deployed correctly, a company can cover all of the bases while also neutralizing
the existential threat of a new competitor. Parallel bets are therefore likely
the right strategy generally for “Big Tech” and during this phase of AI, during
which there are many unresolved and interconnected hypotheses.
* Will closed or open models be more capable? If closed models are technically
superior, will open models nevertheless be considered “superior” on a
cost-adjusted basis? What is the trade-off between the quality of a
generative AI response and its cost? How does this vary by vertical?
* How many of the potential uses of generative AI will result in new
companies/applications, rather than new or improved functionality in the
products of existing market leaders? Put another way, is the technology or
distribution more important? Is there a hybrid model in which users access
existing applications, such as PhotoShop or Microsoft Office, but while
logging into a third-party AI service, such as OpenAI?
* Which AI products or integrations will warrant additional revenue from the
user, rather than just be baked into the core product as a new table-stakes
feature?
* To what extent are the answers to these questions path-dependent, that is,
subject to specific decisions by specific companies and the quality of their
specific products—as was the case with Meta open-sourcing its Llama 2 LLM).
And how, again, do the answers differ by vertical?
Eventually, though, it will be necessary for parallel bets to be winnowed; all
strategy is eventually about execution. Note how quickly Microsoft focused its
OS strategy on Windows after the success of Windows 3.0 in 1990 (the company was
later accused of following an “Embrace, Extend, Extinguish” model where one-time
partners would be crushed once emerging markets stabilized). In April 2023,
Google announced that its two core AI divisions, Google Brain (established in
2011) and Deep Mind (established with the 2014 acquisition of Deep Mind) would
be merged into a singular unit, Google Deep Mind. The questions here, of course,
are “When,” “How Much,” and “How do you know?”
Microsoft never halted its investments in applications and productivity tools,
nor Internet services, and is better off as a result. Sometimes parallel bets
lead to growth in new adjacent markets, rather than displace a current one (to
that end, Microsoft’s more direct OS-bets were eventually paired). It’s possible
that Amazon’s Alexa device footprint will still yet enable the company to regain
market leadership. After all, the “era of AI” seems likely to involve some new
device form factors, such as glasses (Amazon’s first pair of Alexa Frames
debuted in 2018), and potentially pendants/badges, or new sorts of smartphones
altogether (as OpenAI CEO Sam Altman and former Apple Chief Design Officer Jony
Ive reportedly believe). As I detailed in the Streaming Book, there was nothing
inherently wrong with Hollywood’s initial approach to internet-based video
delivery, which spanned UltraViolet, IPTV VOD, TV Everywhere, à la carte SVOD
subscriptions, licensing to SVOD services such as Netflix and Prime Video,
distributing linear networks through virtual MVPDs such as Sling TV, and more.
The issue was how long and hard Hollywood stuck to these experiments before
properly embracing the “Streaming Wars” and the models proven by Netflix.
Of course, it’s not always clear when uncertainties have been “settled,” nor for
how long. And there may be no field where this is more difficult to determine
than in AI. In fact, the field’s pioneers have spent three quarters of a century
debating what, exactly, “artificial intelligence” is and is not. In the 1950s,
Marvin Minsky, a computer scientist who also co-founded MIT’s Artificial
Intelligence Laboratory (and is fictionally attributed with the breakthroughs
that enabled the creation of HAL 9000 in 2001: A Space Odyssey), offered one of
the first commonly used definitions of AI—“the science of making machines do
things that would require intelligence if done by humans.” Decades later, many
AI researchers consider many anthropocentric definitions of AI to be misleading
at best and unhelpful at worst as they suggest that intelligence requires a
machine to process information like—if not outright behave similar to—a human.
Still, the field has nevertheless aligned on an anthropocentric observation
known as the “AI Effect.”
The “AI Effect” was coined by AI pioneer John McCarthy (who was, incidentally,
Minsky’s co-founder at MIT’s AI Lab) in the 1960s to describe the “common public
misperception, that as soon as AI successfully solves a problem, that solution
method is no longer within the domain of AI.” Put another way, society quickly
progresses from amazement at “artificial intelligence” to considering it not
“real” Intelligence but instead just computation or, worse still, probabilities.
A few decades later, Larry Tesler, who pioneered human-computer interfaces at
Xerox PARC in the early 1970s and Apple in the 1980s, developed “Tesler’s
Theorem,” which posited simply that artificial intelligence is “whatever hasn’t
been done yet.” It’s through the AI Effect and Tesler’s Theorem that the
one-time marvels of spell check—or an even more important AI benchmark, AI chess
programs—have become so rote and superficially unimpressive. Someday soon
transformer models may succumb to the same fate. To that end, OpenAI’s CEO Sam
Altman has said that ten years ago “a lot of people” would have said “something
like GPT-4” would be artificial general intelligence, but “now, people are like,
‘well it’s like a nice little chatbot or whatever.’”
What, then, is the future of today’s transformer models? In 2024, this
architecture seems paramount. Their rate of improvement—which spans many
different use cases, instantiations, and companies—has been too significant
since this architecture was first described in 2017. And yet, this is the exact
phenomenon that might lead various parties astray. Perhaps GPT-4 will be
remembered not just as a “nice little chatbot,” but the wrong technical
foundation overall. Especially for AGI.
Consider, as an example, the perspective of John Carmack, who is considered the
“father of 3D graphics” due to his pioneering work at id Software, which he
co-founded in 1991, and in 2013 joined Oculus VR as its first CTO. In 2022,
Carmack left Meta to found Keen Technologies, a startup focused exclusively on
creating artificial general intelligence (AGI), after which he explained to
Dallas Innovates that he believed the number of “billion-dollar off-ramps” for
AI technologies has become a de facto obstacle to achieving true AGI: “There are
extremely powerful things possible now in the narrow machine-learning stuff . .
. [but] it’s not clear those are the necessary steps to get all the way to
artificial general intelligence. . . . Most of what the mainstream is doing
[this research and development is] because it’s fabulous, it’s useful . . .
[but] there is this kind of groupthink . . . that is really clear, if you look
at it, about all these brilliant researchers—they all have similar backgrounds,
and they’re all kind of swimming in the same direction.” It is not difficult to
understand what Carmack is referring to when he refers to groupthink. The 2017
Google Brain research paper that proposed the modern transformer model is now
the basis for OpenAI’s models (one of the paper’s eight authors joined OpenAI in
2019) as well as those of Anthropic, Cohere (founded by another author),
Character.AI (founded by another author), Mistral (co-founded by an 8-year
alumnus of Meta’s AI Research Lab), Inflection.AI (founded by a founder of
Google DeepMind), Perplexity.ai (founded by an alumnus of Google DeepMind and
OpenAI), and Meta’s LLaMA. Perhaps, then, Microsoft’s parallel bets are, for the
most part, just different versions of the same bet?
For that matter, Meta’s Chief AI Scientist, Yann LeCunn has consistently argued
that LLM and GPT models will never reach human intelligence levels, let alone
AGI. In a May 2024 interview, he explained to Financial Times these models have
“very limited understanding of logic . . . do not understand the physical world,
do not have persistent memory, cannot reason in any reasonable definition of the
term and cannot plan . . . hierarchically” and are “intrinsically unsafe” as
they can operate accurately only if fed accurate information. A day later, he
tweeted that “If you are a student interested in building the next generation of
AI systems, don’t work on LLMs.”
In contrast to today’s consensus approaches, Carmack is optimistic about “a
handful of ideas that are not the mainstream . . . some work from like the ’70s,
’80s, and ’90s that I actually think might be interesting, because a lot of
things happened back then that didn’t pan out, just because they didn’t have
enough scale. They were trying to do this on one-megahertz computers, not
clusters of GPUs. . . . One of the things I say—and some people don’t like it,”
Carmack continued, “is that the source code, the computer programming necessary
for artificial general intelligence, is going to be a few tens of thousands of
lines of code. Now, a big program is millions of lines of code—the Chrome
browser is like 20 to 30 million lines of code. Elon just mentioned that Twitter
runs on like 20 million lines of Scala. These are big programs, and there’s no
chance that one person can go and rewrite that. You literally cannot type enough
in your remaining life to write all of that code. But it’s my belief that I can
really back up that the programming for AGI is going to be something that one
person could write.” Would that make it a big bet or a small one?
Matthew Ball is the CEO of Epyllion. The fully revised and updated edition of
his instant nationally and internationally bestselling book The Metaverse:
Building the Spatial Internet will be released in July 2024. A version of this
article first appeared on Matthew Ball’s website.
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